sign in sign up
<<
Home , 3DXML, DXOMARK, FastPictureViewer, Geeqie, Image organizer, Image sensor format

b bb

bbb

bbbb

bbbb Basic in 180 Days Book VI - Image Editor: Ramon F. aeroramon.com Contents

1 Day 1 1 1.1 ...... 1 1.1.1 Education and training ...... 1 1.1.2 Drawing ...... 1 1.1.3 Painting ...... 3 1.1.4 Printmaking ...... 5 1.1.5 Photography ...... 5 1.1.6 ...... 6 1.1.7 art ...... 6 1.1.8 Plastic arts ...... 6 1.1.9 of America definition of visual art ...... 7 1.1.10 See also ...... 7 1.1.11 References ...... 9 1.1.12 Bibliography ...... 9 1.1.13 External links ...... 10 1.2 Image ...... 20 1.2.1 Characteristics ...... 21 1.2.2 Imagery (literary term) ...... 21 1.2.3 Moving image ...... 22 1.2.4 See also ...... 22 1.2.5 References ...... 23 1.2.6 External links ...... 23

2 Day 2 24 2.1 ...... 24 2.1.1 Raster ...... 24 2.1.2 Vector ...... 24 2.1.3 Image viewing ...... 25 2.1.4 History ...... 25 2.1.5 See also ...... 25 2.1.6 References ...... 26

3 Day 3 28

i ii CONTENTS

3.1 Two-dimensional space ...... 28 3.1.1 History ...... 28 3.1.2 In geometry ...... 28 3.1.3 In linear algebra ...... 31 3.1.4 In calculus ...... 32 3.1.5 In topology ...... 33 3.1.6 In graph theory ...... 33 3.1.7 References ...... 33 3.1.8 See also ...... 34

4 Day 4 35 4.1 ...... 35 4.1.1 Resolution of digital images ...... 35 4.1.2 Resolution in various media ...... 38 4.1.3 See also ...... 39 4.1.4 References ...... 39 4.1.5 External links ...... 40

5 Day 5 41 5.1 ...... 41 5.1.1 History ...... 41 5.1.2 Types of ...... 44 5.1.3 Preservation ...... 45 5.1.4 Myths and beliefs ...... 46 5.1.5 See also ...... 46 5.1.6 References ...... 46 5.1.7 External links ...... 46

6 Day 6 47 6.1 Image file formats ...... 47 6.1.1 Image file sizes ...... 47 6.1.2 Image file compression ...... 47 6.1.3 Major graphic file formats ...... 48 6.1.4 References ...... 53

7 Day 7 54 7.1 ...... 54 7.1.1 Etymology ...... 55 7.1.2 Technical ...... 56 7.1.3 Megapixel ...... 59 7.1.4 See also ...... 60 7.1.5 References ...... 61 7.1.6 External links ...... 62 CONTENTS iii

8 Day 8 63 8.1 histogram ...... 63 8.1.1 Overview ...... 63 8.1.2 Definition ...... 64 8.1.3 Characteristics of a color histogram ...... 64 8.1.4 Principles of the formation of a color histogram ...... 64 8.1.5 Examples ...... 64 8.1.6 Drawbacks and other approaches ...... 66 8.1.7 Intensity histogram of continuous data ...... 67 8.1.8 References ...... 68 8.1.9 External links ...... 69 8.2 Image histogram ...... 69 8.2.1 Image manipulation and histograms ...... 70 8.2.2 See also ...... 70 8.2.3 References ...... 70 8.2.4 External links ...... 70

9 Day 9 71 9.1 Pixel density ...... 71 9.1.1 Computer displays ...... 71 9.1.2 Scanners and ...... 74 9.1.3 ...... 74 9.1.4 Named pixel densities ...... 74 9.1.5 Metrication ...... 75 9.1.6 Image file format support ...... 75 9.1.7 See also ...... 75 9.1.8 References ...... 75 9.1.9 External links ...... 76 9.2 Dots per inch ...... 76 9.2.1 DPI measurement in printing ...... 76 9.2.2 Computer monitor DPI standards ...... 78 9.2.3 Proposed metrication ...... 81 9.2.4 See also ...... 81 9.2.5 References ...... 82 9.2.6 External links ...... 82

10 Day 10 83 10.1 Imaging technology ...... 83 10.1.1 Examples ...... 83 10.1.2 References ...... 84 10.2 ...... 84 10.2.1 Features ...... 84 iv CONTENTS

10.2.2 Common image viewers ...... 85 10.2.3 See also ...... 85 10.3 ...... 85 10.3.1 Common image organizers features ...... 86 10.3.2 Not so common, or differentiating features ...... 86 10.3.3 Two categories of image organizers ...... 86 10.3.4 Future of image organization ...... 87 10.3.5 Notable image organizers ...... 87 10.3.6 See also ...... 87 10.3.7 References ...... 87 10.3.8 Additional reading ...... 88 10.3.9 External links ...... 88 10.4 Image retrieval ...... 88 10.4.1 Search methods ...... 88 10.4.2 Data Scope ...... 89 10.4.3 Evaluations ...... 89 10.4.4 See also ...... 89 10.4.5 References ...... 89 10.4.6 External links ...... 90

11 Text and image sources, contributors, and licenses 91 11.1 Text ...... 91 11.2 Images ...... 95 11.3 Content license ...... 99 Chapter 1

Day 1

1.1 Visual arts

“Visuals” redirects here. For the Ukrainian media project, see Visuals (project). The visual arts are art forms such as ceramics, drawing, painting, sculpture, printmaking, design, crafts, photography, , filmmaking, literature, and architecture. Many artistic disciplines (performing arts, conceptual art, textile arts) involve aspects of the visual arts as well as arts of other types. Also included within the visual arts[1] are the applied arts[2] such as industrial design, , fashion design, interior design and decorative art.[3] Current usage of the term “visual arts” includes fine art as well as the applied, decorative arts and crafts, but this was not always the case. Before the Arts and Crafts Movement in Britain and elsewhere at the turn of the 20th century, the term 'artist' was often restricted to a person working in the fine arts (such as painting, sculpture, or printmaking) and not the handicraft, craft, or applied art media. The distinction was emphasized by artists of the Arts and Crafts Movement, who valued vernacular art forms as much as high forms.[4] Art schools made a distinction between the fine arts and the crafts, maintaining that a craftsperson could not be considered a practitioner of the arts. The increasing tendency to privilege painting, and to a lesser degree sculpture, above other arts has been a feature of Western art as well as East Asian art. In both regions painting has been seen as relying to the highest degree on the imagination of the artist, and the furthest removed from manual labour – in Chinese painting the most highly valued styles were those of “scholar-painting”, at least in theory practiced by gentleman amateurs. The Western hierarchy of genres reflected similar attitudes.

1.1.1 Education and training

Main article: Visual arts education

Training in the visual arts has generally been through variations of the apprentice and workshop systems. In Europe the Renaissance movement to increase the prestige of the artist led to the academy system for training artists, and today most of the people who are pursuing a career in arts train in art schools at tertiary levels. Visual arts have now become an elective subject in most education systems. (See also art education.)

1.1.2 Drawing

Main article: Drawing

Drawing is a means of making an image, using any of a wide variety of tools and techniques. It generally involves making marks on a surface by applying pressure from a tool, or moving a tool across a surface using dry media such as graphite pencils, pen and ink, inked brushes, wax color pencils, crayons, charcoals, pastels, and markers. Digital tools that simulate the effects of these are also used. The main techniques used in drawing are: line drawing, hatching, crosshatching, random hatching, scribbling, stippling, and blending. An artist who excels in drawing is referred to as a draftsman or draughtsman.

1 2 CHAPTER 1. DAY 1

Vincent van Gogh: The Church at Auvers (1890)

Drawing goes back at least 16,000 years to cave representations of animals such as those at Lascaux in France and Altamira in Spain. In ancient Egypt, ink drawings on papyrus, often depicting people, were used as models for painting or sculpture. Drawings on Greek vases, initially geometric, later developed to the human form with black-figure pottery during the 7th century BC.[5] With paper becoming common in Europe by the 15th century, drawing was adopted by masters such as Sandro Botticelli, Raphael, Michelangelo, and Leonardo da Vinci who sometimes treated drawing as an art in its own right rather than a preparatory stage for painting or sculpture.[6] 1.1. VISUAL ARTS 3

1.1.3 Painting

Mosaic of Battle of Issus

Main article: Painting Painting taken literally is the practice of applying pigment suspended in a carrier (or medium) and a binding agent (a glue) to a surface (support) such as paper, canvas or a wall. However, when used in an artistic sense it means the use of this activity in combination with drawing, composition and, or, other aesthetic considerations in order to manifest the expressive and conceptual intention of the practitioner. Painting is also used to express spiritual motifs and ideas; sites of this kind of painting range from artwork depicting mythological figures on pottery to The Sistine Chapel to the human body itself.

Origins and early history

Main article: History of painting

Like drawing, painting has its documented origins in caves and on rock faces. The finest examples, believed by some to be 32,000 years old, are in the Chauvet and Lascaux caves in southern France. In shades of red, brown, yellow and black, the paintings on the walls and ceilings are of bison, cattle, horses and deer. Paintings of human figures can be found in the tombs of ancient Egypt. In the great temple of Ramses II, Nefertari, his , is depicted being led by Isis.[7] The Greeks contributed to painting but much of their work has been lost. One of the best remaining representations are the hellenistic Fayum mummy portraits. Another example is mosaic of the Battle of Issus at , which was probably based on a Greek painting. Greek and Roman art contributed to Byzantine art in the 4th century BC, which initiated a tradition in painting.

The Renaissance

Main article: Italian Renaissance painting

Apart from the illuminated manuscripts produced by monks during the Middle Ages, the next significant contribution to European art was from Italy’s renaissance painters. From Giotto in the 13th century to Leonardo da Vinci and Raphael at the beginning of the 16th century, this was the richest period in Italian art as the chiaroscuro techniques were used to create the illusion of 3-D space.[8] 4 CHAPTER 1. DAY 1

Painters in northern Europe too were influenced by the Italian school. Jan van Eyck from Belgium, Pieter Bruegel the Elder from the Netherlands and Hans Holbein the Younger from Germany are among the most successful painters of the times. They used the glazing technique with oils to achieve depth and luminosity.

Dutch masters

Main article: Dutch painting

The 17th century witnessed the emergence of the great Dutch masters such as the versatile Rembrandt who was especially remembered for his portraits and Bible scenes, and Vermeer who specialized in interior scenes of Dutch life.

Baroque

Main article: Baroque

The Baroque started after the Renaissance, from the late 16th century to the late 17th century. Main artists of the Baroque included Caravaggio, who made heavy use of tenebrism. Peter Paul Rubens was a flemish painter who studied in Italy, worked for local churches in Antwerp and also painted a series for Marie de' Medici. Annibale Carracci took influences from the Sistine Chapel and created the genre of illusionistic ceiling painting. Much of the development that happened in the Baroque was because of the Protestant Reformation and the resulting Counter Reformation. Much of what defines the Baroque is dramatic lighting and overall visuals.[9]

Impressionism

Main article: Impressionism

Impressionism began in France in the 19th century with a loose association of artists including Claude Monet, Pierre- Auguste Renoir and Paul Cézanne who brought a new freely brushed style to painting, often choosing to paint realistic scenes of modern life outside rather than in the studio. This was achieved through a new expression of aesthetic features demonstrated by brush strokes and the impression of reality. They achieved intense colour vibration by using pure, unmixed colours and short brush strokes. The movement influenced art as a dynamic, moving through time and adjusting to new found techniques and perception of art. Attention to detail became less of a priority in achieving, whilst exploring a biased view of landscapes and nature to the artists eye.[10][11]

Post-impressionism

Main article: Post-Impressionism

Towards the end of the 19th century, several young painters took impressionism a stage further, using geometric forms and unnatural colour to depict emotions while striving for deeper symbolism. Of particular note are Paul Gauguin, who was strongly influenced by Asian, African and Japanese art, Vincent van Gogh, a Dutchman who moved to France where he drew on the strong sunlight of the south, and Toulouse-Lautrec, remembered for his vivid paintings of night life in the district of Montmartre.[12]

Symbolism, expressionism and cubism

Main article: Modern art

Edvard Munch, a Norwegian artist, developed his symbolistic approach at the end of the 19th century, inspired by the French impressionist Manet. The Scream (1893), his most famous work, is widely interpreted as representing the universal anxiety of modern man. Partly as a result of Munch’s influence, the German expressionist movement originated in Germany at the beginning of the 20th century as artists such as Ernst Kirschner and Erich Heckel began 1.1. VISUAL ARTS 5 to distort reality for an emotional effect. In parallel, the style known as cubism developed in France as artists focused on the volume and space of sharp structures within a composition. Pablo Picasso and Georges Braque were the leading proponents of the movement. Objects are broken up, analyzed, and re-assembled in an abstracted form. By the 1920s, the style had developed into surrealism with Dali and Magritte.[13]

1.1.4 Printmaking

Main article: Printmaking

Printmaking is creating, for artistic purposes, an image on a matrix that is then transferred to a two-dimensional (flat) surface by means of ink (or another form of pigmentation). Except in the case of a monotype, the same matrix can be used to produce many examples of the print. Historically, the major techniques (also called media) involved are woodcut, line engraving, etching, lithography, and screenprinting (serigraphy, silkscreening) but there are many others, including modern digital techniques. Normally, the print is printed on paper, but other mediums range from cloth and vellum to more modern materials. Major printmaking traditions include that of Japan (ukiyo-e).

European history

Main article: Old master print

Prints in the Western tradition produced before about 1830 are known as old master prints. In Europe, from around 1400 AD woodcut, was used for master prints on paper by using printing techniques developed in the Byzantine and Islamic worlds. Michael Wolgemut improved German woodcut from about 1475, and Erhard Reuwich, a Dutchman, was the first to use cross-hatching. At the end of the century Albrecht Dürer brought the Western woodcut to a stage that has never been surpassed, increasing the status of the single-leaf woodcut.[14]

Chinese origin and practice

Main article: Woodblock printing

In China, the art of printmaking developed some 1,100 years ago as alongside text cut in woodblocks for printing on paper. Initially images were mainly religious but in the Song Dynasty, artists began to cut landscapes. During the Ming (1368–1644) and Qing (1616–1911) dynasties, the technique was perfected for both religious and artistic engravings.[15][16]

Development In Japan 1603-1867

Main article: Woodblock printing in Japan

Woodblock printing in Japan (Japanese: , moku hanga) is a technique best known for its use in the ukiyo-e artistic genre; however, it was also used very widely for printing books in the same period. Woodblock printing had been used in China for centuries to print books, long before the advent of movable type, but was only widely adopted in Japan surprisingly late, during the Edo period (1603-1867). Although similar to woodcut in western printmaking in some regards, moku hanga differs greatly in that water-based inks are used (as opposed to western woodcut, which uses oil-based inks), allowing for a wide range of vivid color, glazes and color .

1.1.5 Photography

Main article: Photography 6 CHAPTER 1. DAY 1

Photography is the process of making pictures by means of the action of light. Light patterns reflected or emitted from objects are recorded onto a sensitive medium or storage chip through a timed . The process is done through mechanical shutters or electronically timed exposure of photons into chemical processing or digitizing devices known as cameras. The word comes from the Greek words φως phos (“light”), and γραφις graphis (“stylus”, “”) or γραφη graphê, together meaning “drawing with light” or “representation by means of lines” or “drawing.” Traditionally, the product of photography has been called a photograph. The term photo is an abbreviation; many people also call them pictures. In , the term image has begun to replace photograph. (The term image is traditional in geometric optics.)

1.1.6 Filmmaking

Main article: Filmmaking

Filmmaking is the process of making a motion-picture, from an initial conception and research, through scriptwriting, shooting and recording, or other special effects, editing, sound and music work and finally distribution to an audience; it refers broadly to the creation of all types of films, embracing documentary, strains of theatre and literature in film, and poetic or experimental practices, and is often used to refer to video-based processes as well.

1.1.7 Computer art

Main article: Computer art

Visual artists are no longer limited to traditional art media. have been used as an ever more common tool in the visual arts since the 1960s. Uses include the capturing or creating of images and forms, the editing of those images and forms (including exploring multiple compositions) and the final rendering and/or printing (including 3D printing). Computer art is any in which computers played a role in production or display. Such art can be an image, sound, animation, video, CD-ROM, DVD, , website, algorithm, performance or gallery installation. Many tra- ditional disciplines are now integrating digital technologies and, as a result, the lines between traditional works of art and new media works created using computers have been blurred. For instance, an artist may combine traditional painting with algorithmic art and other digital techniques. As a result, defining computer art by its end product can be difficult. Nevertheless, this type of art is beginning to appear in art museum exhibits, though it has yet to prove its legitimacy as a form unto itself and this technology is widely seen in contemporary art more as a tool rather than a form as with painting. Computer usage has blurred the distinctions between illustrators, , photo editors, 3-D modelers, and handicraft artists. Sophisticated rendering and editing has led to multi-skilled image developers. Photographers may become digital artists. Illustrators may become animators. Handicraft may be computer-aided or use computer- generated imagery as a template. Computer clip art usage has also made the clear distinction between visual arts and page layout less obvious due to the easy access and editing of clip art in the process of paginating a document, especially to the unskilled observer.

1.1.8 Plastic arts

Main article: Plastic arts

Plastic arts is a term, now largely forgotten, encompassing art forms that involve physical manipulation of a plastic medium by moulding or modeling such as sculpture or ceramics. The term has also been applied to all the visual (non-literary, non-musical) arts.[17][18] Materials that can be carved or shaped, such as stone or wood, concrete or steel, have also been included in the narrower definition, since, with appropriate tools, such materials are also capable of modulation. This use of the term “plastic” in the arts should not be confused with Piet Mondrian's use, nor with the movement he termed, in French and English, "Neoplasticism.” 1.1. VISUAL ARTS 7

Sculpture

Main article: Sculpture

Sculpture is three-dimensional artwork created by shaping or combining hard and/or plastic material, sound, and/or text and or light, commonly stone (either rock or marble), clay, metal, glass, or wood. Some sculptures are created directly by finding or carving; others are assembled, built together and fired, welded, molded, or cast. Sculptures are often painted.[19] A person who creates sculptures is called a sculptor. Because sculpture involves the use of materials that can be moulded or modulated, it is considered one of the plastic arts. The majority of public art is sculpture. Many sculptures together in a garden setting may be referred to as a sculpture garden. Sculptors do not always make sculptures by hand. With increasing technology in the 20th century and the popularity of conceptual art over technical mastery, more sculptors turned to art fabricators to produce their artworks. With fabrication, the artist creates a design and pays a fabricator to produce it. This allows sculptors to create larger and more complex sculptures out of material like cement, metal and plastic, that they would not be able to create by hand. Sculptures can also be made with 3-d printing technology.

1.1.9 United States of America copyright definition of visual art

In the United States, the protecting the copyright over a piece of visual art gives a more restrictive definition of “visual art”. The following quote is from the Copyright Law of the United States of America- Chapter 1:[20]

A “work of visual art” is — (1) a painting, drawing, print or sculpture, existing in a single copy, in a limited edition of 200 copies or fewer that are signed and consecutively numbered by the author, or, in the case of a sculpture, in multiple cast, carved, or fabricated sculptures of 200 or fewer that are consecutively numbered by the author and the signature or other identifying mark of the author; or (2) a still photographic image produced for exhibition purposes only, existing in a single copy that is signed by the author, or in a limited edition of 200 copies or fewer that are signed and consecutively numbered by the author. A work of visual art does not include — (A)(i) any poster, map, globe, chart, technical drawing, diagram, , applied art, motion picture or other audiovisual work, book, magazine, newspaper, periodical, data base, electronic information service, electronic publication, or similar publication; (ii) any merchandising item or advertising, promotional, descriptive, covering, or packaging material or container; (iii) any portion or part of any item described in clause (i) or (ii); (B) any work made for hire; or (C) any work not subject to copyright protection under this title.

1.1.10 See also

Main article: Outline of visual arts

• Art materials • Asemic writing • Avant-garde • Cave painting • Child art • Collage • Comics 8 CHAPTER 1. DAY 1

• Composition • Conceptual art • Contemporary art • Craft • Crowdsourcing creative work • Décollage • Decorative arts • Design • Eastern art history • Environmental art • Fine art • Found object • Graffiti • Graphic design • Handicraft • History of art • History of graphic design • History of film • History of painting • History of sculpture • • Indigenous Australian art • Installation art • Interactive art • Islamic art • Landscape art • • Mathematics and art • Media (arts) • Mixed media • Naïve art • Old master print • Portraiture • Process art • Recording medium • Sketch (drawing) 1.1. VISUAL ARTS 9

• • Sound art • Textile arts • Video art • Kandyan Era Frescoes

1.1.11 References

[1] An About.com article by art expert, Shelley Esaak: What Is Visual Art?

[2] Different Forms of Art- Applied Art. Buzzle.com. Retrieved 11 Dec 2010.

[3] “Centre for Arts and Design in Toronto, Canada”. Georgebrown.ca. 2011-02-15. Retrieved 2011-10-30.

[4] Art History: Arts and Crafts Movement: (1861-1900). From World Wide Arts Resources. Retrieved 24 October 2009.

[5] History of Drawing. From Dibujos para Pintar. Retrieved 23 October 2009.

[6] Drawing. From History.com. Retrieved 23 October 2009.

[7] History of Painting. From History World. Retrieved 23 October 2009.

[8] History of Renaissance Painting. From ART 340 Painting. Retrieved 24 October 2009.

[9] https://www.ashgate.com/pdf/SamplePages/Rethinking_the_Baroque_Intro.

[10] http://www.impressionism.org

[11] Impressionism. Webmuseum, Paris. Retrieved 24 October 2009

[12] Post-Impressionism. Metropolitan Museum of Art. Retrieved 25 October 2009.

[13] Modern Art Movements. Irish Art Encyclopedia. Retrieved 25 October 2009.

[14] The Printed Image in the West: History and Techniques. The Metropolitan Museum of Art. Retrieved 25 October 2009.

[15] Engraving in Chinese Art. From Engraving Review. Retrieved 23 October 2009.

[16] The History of Engraving in China. From ChinaVista. Retrieved 25 October 2009.

[17] ART TERMINOLOGY at KSU

[18] “Merriam-Webster Online (entry for “plastic arts”)". Merriam-webster.com. Retrieved 2011-10-30.

[19] Gods in Color: Painted Sculpture of Classical Antiquity 22 September 2007 Through 20 January 2008, The Arthur M. Sackler Museum Archived 4 January 2009 at the .

[20] “Copyright Law of the United States of America- Chapter 1 (101. Definitions)". Copyright.gov. Retrieved 2011-10-30.

1.1.12 Bibliography

• Barnes, A. C., The Art in Painting, 3rd ed., 1937, Harcourt, Brace & World, Inc., NY. • Bukumirovic, D. (1998). Maga Magazinovic. Biblioteka Fatalne srpkinje knj. br. 4. Beograd: Narodna knj. • Fazenda, M. J. (1997). Between the pictorial and the expression of ideas: the plastic arts and literature in the dance of Paula Massano. N.p. • Gerón, C. (2000). Enciclopedia de las artes plásticas dominicanas: 1844-2000. 4th ed. Dominican Republic s.n. • Oliver Grau (Ed.): MediaArtHistories. MIT-Press, Cambridge 2007. with Rudolf Arnheim, Barbara Stafford, Sean Cubitt, W. J. T. Mitchell, Lev Manovich, Christiane Paul, Peter Weibel a.o. Rezensionen • Laban, R. V. (1976). The language of movement: a guidebook to choreutics. Boston: Plays. 10 CHAPTER 1. DAY 1

• La Farge, O. (1930). Plastic prayers: dances of the Southwestern Indians. N.p.

• Restany, P. (1974). Plastics in arts. Paris, New York: N.p. • University of Pennsylvania. (1969). Plastics and new art. Philadelphia: The Falcon Pr.

1.1.13 External links

• ArtLex - online dictionary of visual art terms. • Calendar for Artists - calendar listing of visual art festivals.

• Art History Timeline by the Metropolitan Museum of Art. 1.1. VISUAL ARTS 11

Nefertari with Isis 12 CHAPTER 1. DAY 1

Raphael: Spasimo (1514-1516) 1.1. VISUAL ARTS 13

Rembrandt: The Night Watch 14 CHAPTER 1. DAY 1

Claude Monet: Déjeuner sur l'herbe (1866) 1.1. VISUAL ARTS 15

Paul Gauguin: The Vision After the Sermon (1888) 16 CHAPTER 1. DAY 1

Edvard Munch: The Scream (1893) 1.1. VISUAL ARTS 17

Ancient Chinese engraving of female instrumentalists 18 CHAPTER 1. DAY 1

Albrecht Dürer: Melancholia I (1541) 1.1. VISUAL ARTS 19

The Chinese Diamond Sutra, the world’s oldest printed book (868 CE)

Hokusai: “Red Fuji southern wind clear morning” from Thirty-six Views of Mount Fuji 20 CHAPTER 1. DAY 1

1.2 Image

This article is about visual artifacts or reproductions. For other uses, see Image (disambiguation). “Picture” redirects here. For other uses, see Picture (disambiguation). An image (from Latin: imago) is an artifact that depicts visual perception, for example a two-dimensional picture,

A man painting an image of himself.

A scanned image of the definition of image and imagery, from Thomas Blount’s Glossographia Anglicana Nova, 1707. that has a similar appearance to some subject—usually a physical object or a person, thus providing a depiction of it. 1.2. IMAGE 21

An SAR radar image acquired by the SIR-C/X-SAR radar on board the Space Shuttle Endeavour shows the Teide volcano. The city of Santa Cruz de Tenerife is visible as the purple and white area on the lower right edge of the island. Lava flows at the summit crater appear in shades of green and brown, while vegetation zones appear as areas of purple, green and yellow on the volcano’s flanks.

1.2.1 Characteristics

Images may be two-dimensional, such as a photograph or screen display, or three-dimensional, such as a statue or hologram. They may be captured by optical devices – such as cameras, mirrors, lenses, telescopes, microscopes, etc. and natural objects and phenomena, such as the human eye or water. The word 'image' is also used in the broader sense of any two-dimensional figure such as a map, a graph, a pie chart, or a painting. In this wider sense, images can also be rendered manually, such as by drawing, the art of painting, carving, rendered automatically by printing or computer technology, or developed by a combination of methods, especially in a pseudo-photograph. A volatile image is one that exists only for a short period of time. This may be a reflection of an object by a mirror, a projection of a obscura, or a scene displayed on a cathode ray tube. A fixed image, also called a hard copy, is one that has been recorded on a material object, such as paper or textile by photography or any other digital process. A mental image exists in an individual’s mind, as something one remembers or imagines. The subject of an image need not be real; it may be an abstract concept, such as a graph, function, or “imaginary” entity. For example, claimed to have dreamed purely in aural-images of dialogs. The development of synthetic acoustic technologies and the creation of sound art have led to a consideration of the possibilities of a sound-image made up of irreducible phonic substance beyond linguistic or musicological analysis. A still image is a single static image, as distinguished from a kinetic image (see below). This phrase is used in photography, visual media and the computer industry to emphasize that one is not talking about movies, or in very precise or pedantic technical writing such as a . A film still is a photograph taken on the set of a movie or television program during production, used for promotional purposes.

1.2.2 Imagery (literary term)

Main article: Imagery 22 CHAPTER 1. DAY 1

The act of photographing the surrounding environment with a camera, while the display of the mobile phone shows a live display of the image.

In literature, imagery is a “mental picture” which appeals to the senses.[1] It can both be figurative and literal.[1]

1.2.3 Moving image

Main article: Film

A moving image is typically a movie (film) or video, including digital video. It could also be an animated display such as a zoetrope.

1.2.4 See also

• Aniconism • Avatar (computing) • Cinematography • Computer animation • Computer-generated imagery • Digital image • Digital imaging • Fine art photography • Graphics 1.2. IMAGE 23

•

1.2.5 References

[1] Chris Baldick (2008). The Oxford Dictionary of Literary Terms. Oxford University Press. pp. 165–. ISBN 978-0-19- 920827-2.

1.2.6 External links

• Media related to Images at Wikimedia Commons

• Quotations related to Image at Wikiquote • The dictionary definition of image at Wiktionary

• The B-Z Reaction: The Moving or the Still Image?

• Library of Congress – Format Descriptions for Still Images • Image Processing – Online Open Research Group

• Legal Issues Regarding Images • Image Copyright Case Chapter 2

Day 2

2.1 Digital image

For a broader coverage related to this topic, see Digital imaging.

A digital image is a numeric representation of (normally binary) a two-dimensional image. Depending on whether the image resolution is fixed, it may be of vector or raster type. By itself, the term “digital image” usually refers to raster images or bitmapped images.

2.1.1 Raster

Raster images have a finite set of digital values, called picture elements or . The digital image contains a fixed number of rows and columns of pixels. Pixels are the smallest individual element in an image, holding antiquated values that represent the brightness of a given color at any specific point. Typically, the pixels are stored in computer memory as a raster image or raster map, a two-dimensional array of small integers. These values are often transmitted or stored in a compressed form. Raster images can be created by a variety of input devices and techniques, such as digital cameras, scanners, coordinate- measuring machines, seismographic profiling, airborne radar, and more. They can also be synthesized from arbitrary non-image data, such as mathematical functions or three-dimensional geometric models; the latter being a major sub- area of . The field of is the study of algorithms for their transformation.

Raster file formats

Most users come into contact with raster images through digital cameras, which use any of several image file formats. Some digital cameras give access to almost all the data captured by the camera, using a . The Universal Photographic Imaging Guidelines (UPDIG) suggests these formats be used when possible since raw files produce the best quality images. These file formats allow the and the processing agent the greatest level of control and accuracy for output. Their use is inhibited by the prevalence of proprietary information ( secrets) for some camera makers, but there have been initiatives such as OpenRAW to influence manufacturers to release these records publicly. An alternative may be Digital (DNG), a proprietary Adobe product described as “the public, archival format for raw data”.[1] Although this format is not yet universally accepted, support for the product is growing, and increasingly professional archivists and conservationists, working for respectable organizations, variously suggest or recommend DNG for archival purposes.[2][3][4][5][6][7][8][9][10]

2.1.2 Vector

Vector images resulted from mathematical geometry (vector). In mathematical terms, a vector consists of point that has both direction and length.

24 2.1. DIGITAL IMAGE 25

Often, both raster and vector elements will be combined in one image; for example, in the case of a billboard with text (vector) and photographs (raster).

2.1.3 Image viewing

Image viewer software displays images. browsers can display standard image formats including GIF, JPEG, and PNG. Some can show SVG format which is a standard W3C format. In the past, when Internet was still slow, it was common to provide “preview” image that would load and appear on the web site before being replaced by the main image (to give at preliminary impression). Now Internet is fast enough and this preview image is seldom used. Some scientific images can be very large (for instance, the 46 gigapixel size image of the Milky Way, about 194 Gb in size). [11] Such images are difficult to download and are usually browsed online through more complex web interfaces. Some viewers offer a slideshow utility to display a sequence of images.

2.1.4 History

Early Digital fax machines such as the Bartlane cable picture transmission system preceded digital cameras and computers by decades. The first picture to be scanned, stored, and recreated in digital pixels was displayed on the Standards Eastern Automatic Computer (SEAC) at NIST.[12] The advancement of digital imagery continued in the early 1960s, alongside development of the space program and in medical research. Projects at the Jet Propulsion Laboratory, MIT, Bell Labs and the University of Maryland, among others, used digital images to advance satellite imagery, wirephoto standards conversion, medical imaging, videophone technology, recognition, and photo enhancement.[13] Rapid advances in digital imaging began with the introduction of microprocessors in the early 1970s, alongside progress in related storage and display technologies. The invention of computerized axial tomography (CAT scan- ning), using x-rays to produce a digital image of a “slice” through a three-dimensional object, was of great importance to medical diagnostics. As well as origination of digital images, digitization of analog images allowed the enhancement and restoration of archaeological artifacts and began to be used in fields as diverse as nuclear medicine, astronomy, law enforcement, defence and industry.[14] Advances in microprocessor technology paved the way for the development and marketing of charge-coupled de- (CCDs) for use in a wide range of image capture devices and gradually displaced the use of analog film and tape in photography and videography towards the end of the 20th century. The computing power necessary to pro- cess digital image capture also allowed computer-generated digital images to achieve a level of refinement close to photorealism.[15]

2.1.5 See also

• Computer printer

• Digital image editing

• Digital geometry

• Digital photography

• Geocoded photo

• Optical character recognition

• Signal processing

• Digital image correlation

• DICOM 26 CHAPTER 2. DAY 2

The first scan done by the SEAC in 1957

2.1.6 References

[1] (DNG) Specification. San Jose: Adobe, 2005. Vers. 1.1.0.0. p. 9. Accessed on October 10, 2007.

[2] universal photographic digital imaging guidelines (UPDIG): File formats - the raw file issue

[3] Archaeology Data Service / Digital Antiquity: Guides to Good Practice - Section 3 Archiving Raster Images - File Formats

[4] University of Connecticut: “Raw as Archival Still Image Format: A Consideration” by Michael J. Bennett and F. Barry Wheeler

[5] Inter-University Consortium for Political and Social Research: Obsolescence - File Formats and Software

[6] JISC Digital Media - Still Images: Choosing a for Digital Still Images - File formats for master archive 2.1. DIGITAL IMAGE 27

The SEAC scanner

[7] International Digital Enterprise Alliance, Digital Image Submission Criteria (DISC) Guidelines & Specifications 2007 (PDF)

[8] The J. Paul Getty Museum - Department of Photographs: Rapid Capture Backlog Project - Presentation

[9] American Institute for Conservation - Electronic Media Group: Digital

[10] Archives Association of British Columbia: Born Digital Photographs: Acquisition and Preservation Strategies (Rosaleen Hill)

[11] http://www.techradar.com/news/world-of-tech/this-is-the-milky-way-in-46-billion-pixels-1307463

[12] Fiftieth Anniversary of First Digital Image.

[13] Azriel Rosenfeld, Picture Processing by Computer, New York: Academic Press, 1969

[14] Gonzalez, Rafael, C; Woods, Richard E (2008). Digital Image Processing, 3rd Edition. Pearson Prentice Hall. p. 577. ISBN 978-0-13-168728-8.

[15] Jähne, Bernd (1993). Spatio-temporal image processing, Theory and Scientific Applications. Springer Verlag. p. 208. ISBN 3-540-57418-2. Chapter 3

Day 3

3.1 Two-dimensional space

This article is about 2-dimensional Euclidean space. For the general theory of 2D objects, see Surface (mathematics). In physics and mathematics, two-dimensional space or bi-dimensional space is a geometric model of the planar projection of the physical universe. The two dimensions are commonly called length and width. Both directions lie in the same plane. A sequence of n real numbers can be understood as a location in n-dimensional space. When n = 2, the set of all such locations is called two-dimensional space or bi-dimensional space, and usually is thought of as a Euclidean space.

3.1.1 History

Books I through IV and VI of Euclid’s Elements dealt with two-dimensional geometry, developing such notions as similarity of shapes, the Pythagorean theorem (Proposition 47), equality of angles and areas, parallelism, the sum of the angles in a triangle, and the three cases in which triangles are “equal” (have the same area), among many other topics. Later, the plane was described in a so-called Cartesian coordinate system, a coordinate system that specifies each point uniquely in a plane by a pair of numerical coordinates, which are the signed distances from the point to two fixed perpendicular directed lines, measured in the same unit of length. Each reference line is called a coordinate axis or just axis of the system, and the point where they meet is its origin, usually at ordered pair (0, 0). The coordinates can also be defined as the positions of the perpendicular projections of the point onto the two axes, expressed as signed distances from the origin. The idea of this system was developed in 1637 in writings by Descartes and independently by Pierre de Fermat, although Fermat also worked in three dimensions, and did not publish the discovery.[1] Both authors used a single axis in their treatments and have a variable length measured in reference to this axis. The concept of using a pair of axes was introduced later, after Descartes’ La Géométrie was translated into Latin in 1649 by Frans van Schooten and his students. These commentators introduced several concepts while trying to clarify the ideas contained in Descartes’ work.[2] Later, the plane was thought of as a field, where any two points could be multiplied and, except for 0, divided. This was known as the complex plane. The complex plane is sometimes called the Argand plane because it is used in Argand diagrams. These are named after Jean-Robert Argand (1768–1822), although they were first described by Norwegian-Danish land surveyor and mathematician Caspar Wessel (1745–1818).[3] Argand diagrams are frequently used to plot the positions of the poles and zeroes of a function in the complex plane.

3.1.2 In geometry

Main article: Plane (geometry) See also: Euclidean geometry

28 3.1. TWO-DIMENSIONAL SPACE 29 y (2,3) 3

2 (−3,1) 1 (0,0) x −3−2−1 1 2 3 −1

−2

(−1.5,−2.5) −3

Bi-dimensional Cartesian coordinate system

Coordinate systems

Further information: Coordinate system “Plane coordinates” redirects here. It is not to be confused with Coordinate plane.

In mathematics, analytic geometry (also called Cartesian geometry) describes every point in two-dimensional space by means of two coordinates. Two perpendicular coordinate axes are given which cross each other at the origin. They are usually labeled x and y. Relative to these axes, the position of any point in two-dimensional space is given by an ordered pair of real numbers, each number giving the distance of that point from the origin measured along the given axis, which is equal to the distance of that point from the other axis. Another widely used coordinate system is the polar coordinate system, which specifies a point in terms of its distance from the origin and its angle relative to a rightward reference ray. 30 CHAPTER 3. DAY 3

y y P

x

x

• Cartesian coordinate system

y

P

ρ θ x

• Polar coordinate system

Polytopes

Main article: Polygon

In two dimensions, there are infinitely many polytopes: the polygons. The first few regular ones are shown below:

Convex The Schläfli symbol {p} represents a regular p-gon.

Degenerate (spherical) The regular henagon {1} and regular digon {2} can be considered degenerate regular polygons. They can exist nondegenerately in non-Euclidean spaces like on a 2-sphere or a 2-torus.

Non-convex There exist infinitely many non-convex regular polytopes in two dimensions, whose Schläfli symbols consist of rational numbers {n/m}. They are called star polygons and share the same vertex arrangements of the convex regular polygons. In general, for any natural number n, there are n-pointed non-convex regular polygonal stars with Schläfli symbols {n/m} for all m such that m < n/2 (strictly speaking {n/m} = {n/(n − m)}) and m and n are coprime.

Circle

Main article: Circle The hypersphere in 2 dimensions is a circle, sometimes called a 1-sphere (S1) because it is a one-dimensional manifold. In a Euclidean plane, it has the length 2πr and the area of its interior is

A = πr2 where r is the radius.

Other shapes

Main article: List of two-dimensional geometric shapes

There are an infinitude of other curved shapes in two dimensions, notably including the conic sections: the ellipse, the parabola, and the hyperbola. 3.1. TWO-DIMENSIONAL SPACE 31

3.1.3 In linear algebra

Another mathematical way of viewing two-dimensional space is found in linear algebra, where the idea of indepen- dence is crucial. The plane has two dimensions because the length of a rectangle is independent of its width. In the technical language of linear algebra, the plane is two-dimensional because every point in the plane can be described by a linear combination of two independent vectors.

Dot product, angle, and length

Main article: Dot product

[4] The dot product of two vectors A = [A1, A2] and B = [B1, B2] is defined as:

A · B = A1B1 + A2B2

A vector can be pictured as an arrow. Its magnitude is its length, and its direction is the direction the arrow points. The magnitude of a vector A is denoted by ∥A∥ . In this viewpoint, the dot product of two Euclidean vectors A and B is defined by[5] 32 CHAPTER 3. DAY 3

A · B = ∥A∥ ∥B∥ cos θ,

where θ is the angle between A and B. The dot product of a vector A by itself is

A · A = ∥A∥2,

which gives

√ ∥A∥ = A · A,

the formula for the Euclidean length of the vector.

3.1.4 In calculus

Gradient

In a rectangular coordinate system, the gradient is given by

∂f ∂f ∇f = i + j ∂x ∂y

Line integrals and double integrals

For some scalar field f : U ⊆ R2 → R, the line integral along a piecewise smooth curve C ⊂ U is defined as

∫ ∫ b f ds = f(r(t))|r′(t)| dt. a C

where r: [a, b] → C is an arbitrary bijective parametrization of the curve C such that r(a) and r(b) give the endpoints of C and a < b . For a vector field F : U ⊆ R2 → R2, the line integral along a piecewise smooth curve C ⊂ U, in the direction of r, is defined as

∫ ∫ b F(r) · dr = F(r(t)) · r′(t) dt. a C where · is the dot product and r: [a, b] → C is a bijective parametrization of the curve C such that r(a) and r(b) give the endpoints of C. A double integral refers to an integral within a region D in R2 of a function f(x, y), and is usually written as:

∫∫ f(x, y) dx dy.

D 3.1. TWO-DIMENSIONAL SPACE 33

Fundamental theorem of line integrals

Main article: Fundamental theorem of line integrals

The fundamental theorem of line integrals, says that a line integral through a gradient field can be evaluated by evaluating the original scalar field at the endpoints of the curve. Let φ : U ⊆ R2 → R . Then

∫ φ (q) − φ (p) = ∇φ(r) · dr. γ[p, q]

Green’s theorem

Main article: Green’s theorem

Let C be a positively oriented, piecewise smooth, simple closed curve in a plane, and let D be the region bounded by C. If L and M are functions of (x, y) defined on an open region containing D and have continuous partial derivatives there, then[6][7]

I ∫∫ ( ) ∂M ∂L (L dx + M dy) = − dx dy C D ∂x ∂y where the path of integration along C is counterclockwise.

3.1.5 In topology

In topology, the plane is characterized as being the unique contractible 2-manifold. Its dimension is characterized by the fact that removing a point from the plane leaves a space that is connected, but not simply connected.

3.1.6 In graph theory

In graph theory, a planar graph is a graph that can be embedded in the plane, i.e., it can be drawn on the plane in such a way that its edges intersect only at their endpoints. In other words, it can be drawn in such a way that no edges cross each other.[8] Such a drawing is called a plane graph or planar embedding of the graph. A plane graph can be defined as a planar graph with a mapping from every node to a point on a plane, and from every edge to a plane curve on that plane, such that the extreme points of each curve are the points mapped from its end nodes, and all curves are disjoint except on their extreme points.

3.1.7 References

[1] “Analytic geometry”. Encyclopædia Britannica (Encyclopædia Britannica Online ed.). 2008.

[2] Burton 2011, p. 374

[3] Wessel’s memoir was presented to the Danish Academy in 1797; Argand’s paper was published in 1806. (Whittaker & Watson, 1927, p. 9)

[4] S. Lipschutz; M. Lipson (2009). Linear Algebra (Schaum’s Outlines) (4th ed.). McGraw Hill. ISBN 978-0-07-154352-1.

[5] M.R. Spiegel; S. Lipschutz; D. Spellman (2009). Vector Analysis (Schaum’s Outlines) (2nd ed.). McGraw Hill. ISBN 978-0-07-161545-7.

[6] Mathematical methods for physics and engineering, K.F. Riley, M.P. Hobson, S.J. Bence, Cambridge University Press, 2010, ISBN 978-0-521-86153-3 34 CHAPTER 3. DAY 3

[7] Vector Analysis (2nd Edition), M.R. Spiegel, S. Lipschutz, D. Spellman, Schaum’s Outlines, McGraw Hill (USA), 2009, ISBN 978-0-07-161545-7

[8] Trudeau, Richard J. (1993). Introduction to Graph Theory (Corrected, enlarged republication. ed.). New York: Dover Pub. p. 64. ISBN 978-0-486-67870-2. Retrieved 8 August 2012. Thus a planar graph, when drawn on a flat surface, either has no edge-crossings or can be redrawn without them.

3.1.8 See also

• Two-dimensional graph Chapter 4

Day 4

4.1 Image resolution

“Hi-res” redirects here. For London-based design firm, see Hi-ReS!

Image resolution is the detail an image holds. The term applies to raster digital images, film images, and other types of images. Higher resolution means more image detail. Image resolution can be measured in various ways. Resolution quantifies how close lines can be to each other and still be visibly resolved. Resolution units can be tied to physical sizes (e.g. lines per mm, lines per inch), to the overall size of a picture (lines per picture height, also known simply as lines, TV lines, or TVL), or to angular subtense. Line pairs are often used instead of lines; a line pair comprises a dark line and an adjacent light line. A line is either a dark line or a light line. A resolution of 10 lines per millimeter means 5 dark lines alternating with 5 light lines, or 5 line pairs per millimeter (5 LP/mm). Photographic lens and film resolution are most often quoted in line pairs per millimeter.

4.1.1 Resolution of digital images

The resolution of digital cameras can be described in many different ways.

Pixel resolution

Resolution is the capability of the sensor to observe or measure the smallest object clearly with distinct boundaries. There is a difference between the resolution and a pixel. A pixel is actually a unit of the digital Resolution depends upon the size of the pixel. Usually, with any given lens setting, the smaller the size of the pixel, the higher the resolution will be and the clearer the object in the image will be. Images having smaller pixel sizes might consist of more pixels. The number of pixels correlates to the amount of information within the image. The term resolution is often used for a pixel count in digital imaging, even though British, American, Japanese, and international standards specify that it should not be so used, at least in the digital camera field.[1][2] An image of N pixels height by M pixels wide can have any resolution less than N lines per picture height, or N TV lines. But when the pixel counts are referred to as resolution, the convention is to describe the pixel resolution with the set of two positive integer numbers, where the first number is the number of pixel columns (width) and the second is the number of pixel rows (height), for example as 7680 by 6876. Another popular convention is to cite resolution as the total number of pixels in the image, typically given as number of megapixels, which can be calculated by multiplying pixel columns by pixel rows and dividing by one million. Other conventions include describing pixels per length unit or pixels per area unit, such as pixels per inch or per square inch. None of these pixel resolutions are true resolutions, but they are widely referred to as such; they serve as upper bounds on image resolution. According to the same standards, the number of effective pixels that an or digital camera has is the count of elementary pixel sensors that contribute to the final image, as opposed to the number of total pixels, which includes unused or light-shielded pixels around the edges.

35 36 CHAPTER 4. DAY 4

Below is an illustration of how the same image might appear at different pixel resolutions, if the pixels were poorly rendered as sharp squares (normally, a smooth image reconstruction from pixels would be preferred, but for illustration of pixels, the sharp squares make the point better).

An image that is 2048 pixels in width and 1536 pixels in height has a total of 2048×1536 = 3,145,728 pixels or 3.1 megapixels. One could refer to it as 2048 by 1536 or a 3.1-megapixel image. Or, you can think of it as a very low quality image (72ppi) if printed at about 28.5 inches wide, or a very good quality (300ppi) image if printed at about 7 inches wide. Unfortunately, the count of pixels isn't a real measure of the resolution of digital camera images, because color image sensors are typically set up to alternate color filter types over the light sensitive individual pixel sensors. Digital images ultimately require a red, green, and blue value for each pixel to be displayed or printed, but one individual pixel in the image sensor will only supply one of those three pieces of information. The image has to be interpolated or demosaiced to produce all three for each output pixel.

Spatial resolution

Spatial resolution in radiology refers to the ability of the imaging modality to differentiate two objects. Low spatial resolution techniques will be unable to differentiate between two objects that are relatively close together.

Image at left has a higher pixel count than the one to the right, but is still of worse spatial resolution.

The measure of how closely lines can be resolved in an image is called spatial resolution, and it depends on properties of the system creating the image, not just the pixel resolution in pixels per inch (ppi). For practical purposes the clarity of the image is decided by its spatial resolution, not the number of pixels in an image. In effect, spatial resolution refers to the number of independent pixel values per unit length. The spatial resolution of computer monitors is generally 72 to 100 lines per inch, corresponding to pixel resolutions of 72 to 100 ppi. With scanners, optical resolution is sometimes used to distinguish spatial resolution from the number of pixels per inch. In remote sensing, spatial resolution is typically limited by diffraction, as well as by aberrations, imperfect focus, and atmospheric distortion. The ground sample distance (GSD) of an image, the pixel spacing on the Earth’s surface, is typically considerably smaller than the resolvable spot size. In astronomy, one often measures spatial resolution in data points per arcsecond subtended at the point of observation, because the physical distance between objects in the image depends on their distance away and this varies widely with the object of interest. On the other hand, in electron microscopy, line or fringe resolution refers to the minimum 4.1. IMAGE RESOLUTION 37 separation detectable between adjacent parallel lines (e.g. between planes of atoms), whereas point resolution instead refers to the minimum separation between adjacent points that can be both detected and interpreted e.g. as adjacent columns of atoms, for instance. The former often helps one detect periodicity in specimens, whereas the latter (although more difficult to achieve) is key to visualizing how individual atoms interact. In Stereoscopic 3D images, spatial resolution could be defined as the spatial information recorded or captured by two viewpoints of a stereo camera (left and right camera). The effects of spatial resolution on overall perceived resolution of an image on a person’s mind are yet not fully documented. It could be argued that such “spatial resolution” could add an image that then would not depend solely on pixel count or Dots per inch alone, when classifying and interpreting overall resolution of a given photographic image or video frame.

Spectral resolution

Spectral resolution is the ability to resolve spectral features and bands into their separate components. Color images distinguish light of different spectra. Multispectral images resolve even finer differences of spectrum or wavelength than is needed to reproduce color. That is, multispectral images have higher spectral resolution than normal color images. Spectral resolution is the ability to resolve spectral features and bands into their separate components. The spectral resolution required by the analyst or researcher depends upon the application involved. For example, routine analysis for basic sample identification typically requires low/medium resolution.

Temporal resolution

Temporal resolution (TR) refers to the precision of a measurement with respect to time. Often there is a trade off between temporal resolution of a measurement and its spatial resolution due to uncertainty principle which is an inherent property of Fourier transform. Movie cameras and high-speed cameras can resolve events at different points in time. The time resolution used for movies is usually 24 to 48 frames per second (frames/s), whereas high-speed cameras may resolve 50 to 300 frames/s, or even more. Many cameras and displays offset the color components relative to each other or mix up temporal with spatial reso- lution:

• digital camera (Bayer color filter array)

• LCD (Triangular pixel geometry)

• CRT (shadow mask) 38 CHAPTER 4. DAY 4

Radiometric resolution

Main article:

Radiometric resolution determines how finely a system can represent or distinguish differences of intensity, and is usually expressed as a number of levels or a number of bits, for example 8 bits or 256 levels that is typical of computer image files. The higher the radiometric resolution, the better subtle differences of intensity or reflectivity can be represented, at least in theory. In practice, the effective radiometric resolution is typically limited by the noise level, rather than by the number of bits of representation.

4.1.2 Resolution in various media

This is a list of traditional, analog horizontal resolutions for various media. The list only includes popular formats, not rare formats, and all values are approximate, because the actual quality can vary machine-to-machine or tape-to- tape. For ease-of-comparison, all values are for the NTSC system. (For PAL systems, replace 480 with 576.) Analog formats usually had less chroma resolution.

• Analog and early digital[3] • 352×240 : Video CD • 333×480 : VHS, Video8, Umatic • 350×480 : • 420×480 : Super Betamax, Betacam • 460×480 : Betacam SP, Umatic SP, NTSC (Over-The-Air TV) • 580×480 : Super VHS, Hi8, LaserDisc • 700×480 : Enhanced Definition Betamax, Analog broadcast limit (NTSC) • 768×576 : Analog broadcast limit (PAL, SECAM) • Digital • 500×480 : Digital8 • 720×480 : D-VHS, DVD, miniDV, Digital Betacam (NTSC) • 720×480 : Widescreen DVD (anamorphic) (NTSC) • 720x576 : D-VHS, DVD, miniDV, Digital8, Digital Betacam (PAL/SECAM) • 720x576 : Widescreen DVD (anamorphic) (PAL/SECAM) • 1280×720 : D-VHS, HD DVD, Blu-ray, HDV (miniDV) • 1440×1080 : HDV (miniDV) • 1920×1080 : HDV (miniDV), AVCHD, HD DVD, Blu-ray, HDCAM SR • 1998x1080 : 2K Flat (1.85:1) • 2048×1080 : 2K Digital Cinema • 3840x2160 : 4K UHDTV • 4096×2160 : 4K Digital Cinema • 7680×4320 : 8K UHDTV • 15360x8640 : 16K Digital Cinema • 30720x17280 : 32K resolution • 46080X25920 : 48K resolution • 61440X34560 : 64K resolution • 122880X69120 : 128K resolution • Sequences from newer films are scanned at 2,000, 4,000, or even 8,000 columns, called 2K, 4K, and 8K, for quality visual-effects editing on computers. 4.1. IMAGE RESOLUTION 39

• IMAX, including IMAX HD and OMNIMAX: approximately 10,000×7,000 (7,000 lines) resolution. It is about 70 Mpix, which is currently highest-resolution single-sensor digital cinema camera (as of January 2012). • Film • 35 mm film is scanned for release on DVD at 1080 or 2000 lines as of 2005. • The actual resolution of 35 mm camera original negatives is the subject of much debate. Measured resolutions of negative film have ranged from 25-200 lp/mm, which equates to a range of 325 lines for 2-perf, to (theoretically) over 2300 lines for 4-perf on T-Max 100.[4][5][6] states that 35mm film has the equivalent of 6K resolution according to a Senior President of IMAX.[7] • A 15 perforation 70mm IMAX film negative captures at an estimated 18K, which is the equivalent of 18,000 horizontal pixels.[7] • Print

• Modern digital camera resolutions • Digital camera - single, not combined one large digital sensor - 80 Mpix(starting from 2011, current as of 2013) - 10320 x 7752 or 10380 x 7816(81.1Mpix).[8][9][10][11] • Mobile phone - Nokia 808 PureView - 41 Mpix (7728 x 5368), - also 41 Mpix (7712 x 5360) • Digital still camera - Canon EOS 5DS - 51 Mpix (8688 x 5792)

4.1.3 See also

• Display resolution • Dots per inch • Image scanner • Pixel density • High imaging • Kell factor, which typically limits the number of visible lines to 0.7x of the device resolution

4.1.4 References

[1] Guideline for Noting Digital Camera Specifications in Catalogs. “The term 'Resolution' shall not be used for the number of recorded pixels”

[2] ANSI/I3A IT10.7000-2004 Photography - Digital Still Cameras - Guidelines for Reporting Pixel-Related Specifications

[3] http://www.derose.net/steve/resources/video-resolution.html

[4] Kodak 500t Film spec sheet

[5] An analysis of film resolution

[6] Explanation of MTF

[7] "/Film Interview: IMAX Executives Talk 'The Hunger Games: Catching Fire' and IMAX Misconceptions”. Slash Film. December 2, 2013. Retrieved December 17, 2013.

[8] http://www.phaseone.com/en/camera-systems/iq-series.aspx

[9] http://www.mamiyaleaf.com/leaf_aptus.html

[10] http://www.dxomark.com/Cameras/Camera-Sensor-Database/Phase-One/IQ180-Digital-Back

[11] http://web.forret.com/tools/megapixel.asp?width=10380&height=7816 40 CHAPTER 4. DAY 4

4.1.5 External links

• Luminous Landscape’s Res-Demyst; on why pixel count is not always a good proxy for resolution • Do Sensors “Outresolve” Lenses?; on lens and sensor resolution interaction. Chapter 5

Day 5

5.1 Photograph

“Photos” redirects here. For the Apple application, see Photos (Apple). For the Microsoft application, see Photos (Windows). For other uses, see Photograph (disambiguation). For the technique, see Photography. A photograph or photo is an image created by light falling on a light-sensitive surface, usually photographic film or an electronic medium such as a CCD or a CMOS chip. Most photographs are created using a camera, which uses a lens to focus the scene’s visible wavelengths of light into a reproduction of what the human eye would see. The process and practice of creating photographs is called photography. The word “photograph” was coined in 1839 by Sir John Herschel and is based on the Greek φῶς (phos), meaning “light”, and γραφή (graphê), meaning “drawing, writing”, together meaning “drawing with light”.[1]

5.1.1 History

Main article:

The first permanent photograph, a contact-exposed copy of an engraving, was made in 1822 using the bitumen-based "" process developed by Nicéphore Niépce. The first photographs of a real-world scene, made using a , followed a few years later, but Niépce’s process was not sensitive enough to be practical for that application: a camera exposure lasting for hours or days was required.[2] In 1829 Niépce entered into a partnership with Louis Daguerre and the two collaborated to work out a similar but more sensitive and otherwise improved process. After Niépce’s death in 1833, Daguerre concentrated on silver halide-based alternatives. He exposed a silver-plated copper sheet to iodine vapor, creating a layer of light-sensitive silver iodide; exposed it in the camera for a few minutes; developed the resulting invisible latent image to visibility with mercury fumes; then bathed the plate in a hot salt solution to remove the remaining silver iodide, making the results light-fast. He named this first practical process for making photographs with a camera the , after himself. Its existence was announced to the world on 7 January 1839 but working details were not made public until 19 August. Other inventors soon made improvements which reduced the required exposure time from a few minutes to a few seconds, making truly practical and widely popular. The daguerreotype had shortcomings, notably the fragility of the mirror-like image surface and the particular viewing conditions required to see the image properly. Each was a unique opaque positive that could only be duplicated by copying it with a camera. Inventors set about working out improved processes that would be more practical. By the end of the 1850s the daguerreotype had been replaced by the less expensive and more easily viewed ambrotype and tintype, which made use of the recently introduced collodion process. Glass plate collodion negatives used to make prints on albumen paper soon became the preferred photographic method and held that position for many years, even after the introduction of the more convenient gelatin process in 1871. Refinements of the gelatin process have remained the primary black-and-white photographic process to this day, differing primarily in the sensitivity of the emulsion and the support material used, which was originally glass, then a variety of flexible plastic films, along with

41 42 CHAPTER 5. DAY 5

Woman picking flowers various types of paper for the final prints. is almost as old as black-and-white, with early experiments including John Herschel's Anthotype prints in 1842, the pioneering work of Louis Ducos du Hauron in the 1860s, and the Lippmann process unveiled in 1891, but for many years color photography remained little more than a laboratory curiosity. It first became a 5.1. PHOTOGRAPH 43

The earliest known surviving product of Nicéphore Niépce's heliography process, 1825. It is an ink-on-paper print and reproduces a 17th-century Flemish engraving showing a man leading a horse.

View from the Window at Le Gras (1826 or 1827), by Nicéphore Niépce, the earliest known surviving photograph of a real-world scene, made with a camera obscura 44 CHAPTER 5. DAY 5

widespread commercial reality with the introduction of Autochrome plates in 1907, but the plates were very expen- sive and not suitable for casual -taking with hand-held cameras. The mid-1930s saw the introduction of Kodachrome and Agfacolor Neu, the first easy-to-use color films of the modern multi-layer chromogenic type. These early processes produced transparencies for use in slide projectors and viewing devices, but color prints became in- creasingly popular after the introduction of chromogenic color print paper in the 1940s. The needs of the motion picture industry generated a number of special processes and systems, perhaps the best-known being the now-obsolete three-strip Technicolor process. In contemporary times, were developed wherein prohibitions have been placed against the productions of certain photographs, such as those of highly classified regions,[3] copyrighted works[4] and children’s genitalia.[5]

5.1.2 Types of photographs

Long-exposure photograph of the Very Large Telescope.[6]

Non-digital photographs are produced with a two-step chemical process. In the two-step process the light-sensitive film captures a negative image (colors and lights/darks are inverted). To produce a positive image, the negative is most commonly transferred ('printed') onto photographic paper. Printing the negative onto transparent film stock is used to manufacture motion picture films. Alternatively, the film is processed to invert the negative image, yielding positive transparencies. Such positive images are usually mounted in frames, called slides. Before recent advances in digital photography, transparencies were widely used by professionals because of their sharpness and accuracy of color rendition. Most photographs published in magazines were taken on color transparency film. Originally, all photographs were monochromatic or hand-painted in color. Although methods for developing color photos were available as early as 1861, they did not become widely available until the 1940s or 1950s, and even so, until the 1960s most photographs were taken in . Since then, color photography has dominated popular photography, although black and white is still used, being easier to develop than color. Panoramic format images can be taken with cameras like the Xpan on standard film. Since the 1990s, panoramic photos have been available on the (APS) film. APS was developed by several of the major film manufacturers to provide a film with different formats and computerized options available, though APS panoramas were created using a mask in panorama-capable cameras, far less desirable than a true panoramic camera, which achieves its effect through a wider film format. APS has become less popular and is being discontinued. 5.1. PHOTOGRAPH 45

The advent of the microcomputer and digital photography has led to the rise of digital prints. These prints are created from stored graphic formats such as JPEG, TIFF, and RAW. The types of printers used include inkjet printers, dye- printer, laser printers, and thermal printers. Inkjet prints are sometimes given the coined name "Giclée". The web has been a popular medium for storing and sharing photos ever since the first photograph was published on the web by Tim Berners-Lee in 1992 (an image of the CERN house band Horribles Cernettes). Today popular sites such as , , PhotoBucket and 500px are used by millions of people to share their pictures.

5.1.3 Preservation

Paper folders

Ideal photograph storage involves placing each photo in an individual folder constructed from buffered, or acid-free paper.[7] Buffered paper folders are especially recommended in cases when a photograph was previously mounted onto poor quality material or using an adhesive that will lead to even more acid creation.[8] Store photographs measuring 8x10 inches or smaller vertically along the longer edge of the photo in the buffered paper folder, within a larger archival box, and label each folder with relevant information to identify it. The rigid nature of the folder protects the photo from slumping or creasing, as long as the box is not packed too tightly or under filled. Folder larger photos or brittle photos stacked flat within archival boxes with other materials of comparable size.[9]

Polyester enclosures

The most stable of plastics used in photo preservation, polyester, does not generate any harmful chemical elements, but nor does it have any capability to absorb acids generated by the photograph itself. Polyester sleeves and encapsulation have been praised for their ability to protect the photograph from humidity and environmental pollution, slowing the reaction between the item and the atmosphere. This is true, however the polyester just as frequently traps these elements next to the material it is intended to protect. This is especially risky in a storage environment that experiences drastic fluctuations in humidity or temperature, leading to ferrotyping, or sticking of the photograph to the plastic.[7] Photographs sleeved or encapsulated in polyester cannot be stored vertically in boxes because they will slide down next to each other within the box, bending and folding, nor can the archivist write directly onto the polyester to identify the photograph. Therefore, it is necessary to either stack polyester protected photographs horizontally within a box, or bind them in a three ring binder. Stacking the photos horizontally within a flat box will greatly reduce ease of access, and binders leave three sides of the photo exposed to the effects of light[10] and do not support the photograph evenly on both sides, leading to slumping and bending within the binder. The plastic used for enclosures has been manufactured to be as frictionless as possible to prevent scratching photos during insertion to the sleeves. Unfortunately, the slippery nature of the enclosure generates a build-up of static electricity, which attracts dust and lint particles. The static can attract the dust to the inside of the sleeve, as well, where it can scratch the photograph.[7] Likewise, these components that aid in insertion of the photo, referred to as slip agents, can break down and transfer from the plastic to the photograph, where they deposit as an oily film, attracting further lint and dust. At this time, there is no test to evaluate the long-term effects of these components on photographs. In addition, the plastic sleeves can develop kinks or creases in the surface, which will scratch away at the emulsion during handling.[10]

Handling and care

It is best to leave photographs lying flat on the table when viewing them. Do not pick it up from a corner, or even from two sides and hold it at eye level. Every time the photograph bends, even a little, this can break down the emulsion.[11] The very nature of enclosing a photograph in plastic encourages users to pick it up; users tend to handle plastic enclosed photographs less gently than non-enclosed photographs, simply because they feel the plastic enclosure makes the photo impervious to all mishandling. As long as a photo is in its folder, there is no need to touch it; simply remove the folder from the box, lay it flat on the table, and open the folder. If for some reason the researcher or archivist does need to handle the actual photo, perhaps to examine the verso for writing, he or she can use gloves if there appears to be a risk from oils or dirt on the hands. 46 CHAPTER 5. DAY 5

5.1.4 Myths and beliefs

See also: Aniconism

Because were rendered on a mirrored surface, many spiritualists also became practitioners of the new art form. Spiritualists would claim that the human image on the mirrored surface was akin to looking into one’s soul. The spiritualists also believed that it would open their souls and let demons in. Aborigines believed that taking one’s picture took part of one’s soul away. Among Muslims, it is makruh (offensive) to perform salah (worship) in a place where photographs are decorated.[12]

5.1.5 See also

• • Archival science • Largest photographs in the world • Slide show • Photograph stability • Pseudo-photograph • Director of Photography • Hand-colouring of photographs

5.1.6 References

[1] “Online Etymology Dictionary”. Retrieved 16 January 2017. [2] “The First Photograph - Heliography”. Retrieved 2009-09-29. from Helmut Gernsheim’s article, “The 150th Anniversary of Photography,” in History of Photography, Vol. I, No. 1, January 1977: ... In 1822, Niépce coated a glass plate ... The sunlight passing through ... This first permanent example ... was destroyed ... some years later. [3] Masco, Joseph. "“Sensitive but Unclassified”: Secrecy and the Counterterrorist State.” Public Culture 22.3 (2010): 433- 463. [4] Turnbull, Bruce H. “Important legal developments regarding protection of copyrighted content against unauthorized copy- ing.” IEEE Communications Magazine 39.8 (2001): 92-100. [5] Slane, Andrea. “From scanning to : The scope of protection of dignity-based privacy in Canadian child law.” Osgoode Hall Law Journal 48 (2010): 543. [6] “A Stream of Stars over Paranal”. ESO Picture of the Week. Retrieved 27 May 2014. [7] “5.6 Storage Enclosures for Photographic Materials”. Retrieved 16 January 2017. [8] Norris, Debbie Hess. “Caring for Your Photographic Collections.” Library of Congress. Feb. 9 2008, LOC.gov [9] “How Should I Store my Photographic Prints?" Preservation and Archives Professionals. The National Archives and Records Administration. 9 February 2008, Archives.gov [10] International Organization for Standardization. ISO 18902:2001(E). Geneva, Switzerland: ISO Office, 2007. [11] Baggett, James L. “Handle with Care: Photos.” Alabama Librarian. 54.1 (2004): 5. [12] Rizvi, Sayyid. Your Questions Answered. p. 32.

5.1.7 External links

• Media related to Photographs at Wikimedia Commons • The dictionary definition of photograph at Wiktionary Chapter 6

Day 6

6.1 Image file formats

“Image format” redirects here. For the camera sensor format, see Image sensor format. This article is about digital image formats used to store photographic and other images. For disk-image file formats, see Disk image. For digital file formats in general, see File format.

Image file formats are standardized means of organizing and storing digital images. Image files are composed of digital data in one of these formats that can be rasterized for use on a computer display or printer. An image file format may store data in uncompressed, compressed, or vector formats. Once rasterized, an image becomes a grid of pixels, each of which has a number of bits to designate its color equal to the color depth of the device displaying it.

6.1.1 Image file sizes

The size of raster image files is positively correlated with the resolution and images size (number of pixels) and the color depth (bits per pixel). Images can be compressed in various ways, however. A compression algorithm stores either an exact representation or an approximation of the original image in a smaller number of bytes that can be expanded back to its uncompressed form with a corresponding decompression algorithm. Images with the same number of pixels and color depth can have very different compressed file size. Considering exactly the same compression, number of pixels, and color depth for two images, different graphical complexity of the original images may also result in very different file sizes after compression due to the nature of compression algorithms. With some compression formats, images that are less complex may result in smaller compressed file sizes. This characteristic sometimes results in a smaller file size for some lossless formats than lossy formats. For example, graphically simple images (i.e. images with large continuous regions like line art or animation sequences) may be losslessly compressed into a GIF or PNG format and result in a smaller file size than a lossy JPEG format. Vector images, unlike raster images, can be any dimension independent of file size. File size increases only with the addition of more vectors. For example, a 640 * 480 pixel image with 24-bit color would occupy almost a megabyte of space: 640 * 480 * 24 = 7,372,800 bits = 921,600 bytes = 900 kB

6.1.2 Image file compression

There are two types of image file compression algorithms: lossless and lossy. Lossless compression algorithms reduce file size while preserving a perfect copy of the original uncompressed image. Lossless compression generally, but not always, results in larger files than lossy compression. Lossless compression should be used to avoid accumulating stages of re-compression when editing images. Lossy compression algorithms preserve a representation of the original uncompressed image that may appear to be

47 48 CHAPTER 6. DAY 6

a perfect copy, but it is not a perfect copy. Often lossy compression is able to achieve smaller file sizes than lossless compression. Most lossy compression algorithms allow for variable compression that trades image quality for file size.

6.1.3 Major graphic file formats

Including proprietary types, there are hundreds of image file types. The PNG, JPEG, and GIF formats are most often used to display images on the Internet. These graphic formats are listed and briefly described below, separated into the two main families of graphics: raster and vector. In addition to straight image formats, Metafile formats are portable formats which can include both raster and vec- information. Examples are application-independent formats such as WMF and EMF. The metafile format is an intermediate format. Most applications open metafiles and then save them in their own native format. Page descrip- tion language refers to formats used to describe the layout of a printed page containing text, objects and images. Examples are PostScript, PDF and PCL.

Raster formats

JPEG/JFIF JPEG (Joint Photographic Experts Group) is a lossy compression method; JPEG-compressed images are usually stored in the JFIF (JPEG File Interchange Format) file format. The JPEG/JFIF filename extension is JPG or JPEG. Nearly every digital camera can save images in the JPEG/JFIF format, which supports eight-bit grayscale images and 24-bit color images (eight bits each for red, green, and blue). JPEG applies lossy compression to images, which can result in a significant reduction of the file size. Applications can determine the degree of compression to apply, and the amount of compression affects the visual quality of the result. When not too great, the compression does not noticeably affect or detract from the image’s quality, but JPEG files suffer generational degradation when repeatedly edited and saved. (JPEG also provides lossless image storage, but the lossless version is not widely supported.)

JPEG 2000 JPEG 2000 is a compression standard enabling both lossless and lossy storage. The compression methods used are different from the ones in standard JFIF/JPEG; they improve quality and compression ratios, but also require more computational power to process. JPEG 2000 also adds features that are missing in JPEG. It is not nearly as common as JPEG, but it is used currently in professional movie editing and distribution (some digital cinemas, for example, use JPEG 2000 for individual movie frames).

Exif The (Exchangeable image file format) format is a file standard similar to the JFIF format with TIFF extensions; it is incorporated in the JPEG-writing software used in most cameras. Its purpose is to record and to standardize the exchange of images with image between digital cameras and editing and viewing software. The metadata are recorded for individual images and include such things as camera settings, time and date, speed, exposure, image size, compression, name of camera, color information. When images are viewed or edited by image editing software, all of this image information can be displayed. The actual Exif metadata as such may be carried within different host formats, e.g. TIFF, JFIF (JPEG) or PNG. IFF-META is another example.

TIFF The TIFF (Tagged Image File Format) format is a flexible format that normally saves eight bits or sixteen bits per color (red, green, blue) for 24-bit and 48-bit totals, respectively, usually using either the TIFF or TIF filename extension. The tagged structure was designed to be easily extendible, and many vendors have introduced proprietary special-purpose tags – with the result that no one reader handles every flavor of TIFF file. can be lossy or lossless, depending on the technique chosen for storing the pixel data. Some offer relatively good lossless compression for bi-level (black&white) images. Some digital cameras can save images in TIFF format, using the LZW compression algorithm for lossless storage. TIFF image format is not widely supported by web browsers. TIFF remains widely accepted as a photograph file standard in the printing business. TIFF can handle device-specific color spaces, such as the CMYK defined by a particular set of inks. OCR (Optical Character Recognition) software packages commonly generate some form of TIFF image (often monochromatic) for scanned text pages. 6.1. IMAGE FILE FORMATS 49

GIF GIF (Graphics Interchange Format) is in normal use limited to an 8-bit palette, or 256 colors (while 24-bit color depth is technically possible).[1][2] GIF is most suitable for storing graphics with few colors, such as simple diagrams, shapes, logos, and cartoon style images, as it uses LZW lossless compression, which is more effective when large areas have a single color, and less effective for photographic or dithered images. Due to GIF’s and age, it achieved almost universal software support. Due to its animation capabilities, it is still widely used to provide image animation effects, despite its low compression ratio compared to modern video formats.

BMP The BMP file format (Windows ) handles graphic files within the OS. Typically, BMP files are uncompressed, and therefore large and lossless; their advantage is their simple structure and wide acceptance in Windows programs.

PNG The PNG (Portable Network Graphics) file format was created as a free, open-source alternative to GIF. The PNG file format supports eight-bit paletted images (with optional transparency for all palette colors) and 24-bit truecolor (16 million colors) or 48-bit truecolor with and without alpha channel - while GIF supports only 256 colors and a single transparent color. Compared to JPEG, PNG excels when the image has large, uniformly colored areas. Even for photographs – where JPEG is often the choice for final distribution since its compression technique typically yields smaller file sizes – PNG is still well-suited to storing images during the editing process because of its lossless compression. PNG provides a patent-free replacement for GIF (though GIF is itself now patent-free), and can also replace many common uses of TIFF. Indexed-color, grayscale, and truecolor images are supported, plus an optional alpha channel. The Adam7 interlacing allows an early preview, even when only a small percentage of the image data has been transmitted. PNG can store and chromaticity data for improved color matching on heterogeneous platforms. PNG is designed to work well in online viewing applications like web browsers and can be fully streamed with a progressive display option. PNG is robust, providing both full file integrity checking and simple detection of common transmission errors. Animated formats derived from PNG are MNG and APNG. The latter is supported by Mozilla Firefox and Opera and is backwards compatible with PNG.

PPM, PGM, PBM, and PNM format is a family including the portable pixmap file format (PPM), the portable graymap file format (PGM) and the portable bitmap file format (PBM). These are either pure ASCII files or raw binary files with an ASCII header that provide very basic functionality and serve as a lowest common denominator for converting pixmap, graymap, or bitmap files between different platforms. Several applications refer to them collectively as PNM (Portable aNy Map).

WebP WebP is a new open image format that uses both lossless and lossy compression. It was designed by to reduce image file size to speed up web page loading: its principal purpose is to supersede JPEG as the primary format for photographs on the web. WebP is based on VP8's intra-frame coding and uses a container based on RIFF.

HDR raster formats Most typical raster formats cannot store HDR data (32 bit floating point values per pixel component), which is why some relatively old or complex formats are still predominant here, and worth mention- ing separately. Newer alternatives are showing up, though. RGBE is the format for HDR images originating from Radiance and also supported by .

HEIF The High Efficiency Image File Format (HEIF) is an image container format that was standardized by MPEG on the basis of the ISO base media file format. While HEIF can be used with any image compression format, the HEIF standard specifies the storage of HEVC intra-coded images and HEVC-coded image sequences taking advantage of inter-picture prediction.

BAT BAT was released into the public domain by C-Cube Microsystems. The “official” file format for JPEG files is SPIFF (Still Picture Interchange File Format), but by the time it was released, BAT had already achieved wide acceptance. SPIFF, which has the ISO designation 10918-3, offers more versatile compression, , and metadata capacity than JPEG/BAT, but it has little support. It may be superseded by JPEG 2000/DIG 2000: 50 CHAPTER 6. DAY 6

ISO SC29/WG1, JPEG - Information Links. Digital Imaging Group, “JPEG 2000 and the DIG: The Picture of Compatibility.”

BPG BPG () is a new image format. Its purpose is to replace the JPEG image format when quality or file size is an issue. Its main advantages are:

• High compression ratio. Files are much smaller than JPEG for similar quality. • Supported by most Web browsers with a small Javascript decoder (gzipped size: 76 KB). • Based on a subset of the HEVC open video compression standard. • Supports the same chroma formats as JPEG (grayscale, YCbCr 4:2:0, 4:2:2, 4:4:4) to reduce the losses during the conversion. An alpha channel is supported. The RGB, YCgCo and CMYK color spaces are also supported. • Native support of 8 to 14 bits per channel for a higher dynamic range. • Lossless compression is supported. • Various meta data (such as EXIF) can be included.

Other raster formats

• CD5 (Chasys Draw Image) • DEEP (IFF-style format used by TVPaint) • ECW (Enhanced Compression Wavelet) • FITS (Flexible Image Transport System) • FLIF (Free Lossless Image Format) - a work-in-progress lossless image format which claims to outperform PNG, lossless WebP, lossless BPG and lossless JPEG2000 in terms of compression ratio. It uses the MANIAC (Meta-Adaptive Near-zero Integer Arithmetic Coding) entropy encoding algorithm, a variant of the CABAC (-adaptive binary arithmetic coding) entropy encoding algogithm. • ICO, container for one or more icons (subsets of BMP and/or PNG) • ILBM (IFF-style format for up to 32 bit in planar representation, plus optional 64 bit extensions) • IMG (ERDAS IMAGINE Image) • IMG (Graphics Environment Manager (GEM) image file; planar, run-length encoded) • JPEG XR (New JPEG standard based on Microsoft HD Photo) • Layered Image File Format for microscope image processing • Nrrd (Nearly raw raster data) • PAM (Portable Arbitrary Map) is a late addition to the Netpbm family • PCX ( eXchange), obsolete • PGF (Progressive Graphics File) • PLBM - Planar Bitmap, proprietary Amiga format • SGI • SID (multiresolution seamless image database, MrSID) • is an obsolete format • TGA (TARGA), obsolete • VICAR file format (NASA/JPL image transport format) • XISF (Extensible Image Serialization Format) 6.1. IMAGE FILE FORMATS 51

Container formats of editors These image formats contain various images, layers and objects, out of which the final image is to be composed

• CPT (Corel Photo Paint)

• PSD (Adobe PhotoShop Document)

• PSP (Corel Paint Shop Pro)

• XCF (eXperimental Computing Facility format, native GIMP format)

Vector formats

Main article:

As opposed to the raster image formats above (where the data describes the characteristics of each individual pixel), vector image formats contain a geometric description which can be rendered smoothly at any desired display size. At some point, all vector graphics must be rasterized in order to be displayed on digital monitors. Vector images may also be displayed with analog CRT technology such as that used in some electronic test equipment, medical monitors, radar displays, laser shows and early video games. Plotters are printers that use vector data rather than pixel data to draw graphics.

CGM CGM (Computer Graphics Metafile) is a file format for 2D vector graphics, raster graphics, and text, and is defined by ISO/IEC 8632. All graphical elements can be specified in a textual source file that can be compiled into a binary file or one of two text representations. CGM provides a means of graphics data interchange for computer representation of 2D graphical information independent from any particular application, system, platform, or device. It has been adopted to some extent in the areas of technical illustration and professional design, but has largely been superseded by formats such as SVG and DXF.

Gerber format (RS-274X) The (aka Extended Gerber, RS-274X) was developed by Gerber Sys- tems Corp., now Ucamco, and is a 2D bi-level image description format. It is the de facto standard format used by printed circuit board or PCB software. It is also widely used in other industries requiring high-precision 2D bi-level images.[3]

SVG SVG () is an open standard created and developed by the Con- sortium to address the need (and attempts of several corporations) for a versatile, scriptable and all-purpose vector format for the web and otherwise. The SVG format does not have a compression scheme of its own, but due to the textual nature of XML, an SVG graphic can be compressed using a program such as gzip. Because of its scripting potential, SVG is a key component in web applications: interactive web pages that look and act like applications.

Other 2D vector formats

• AI (Adobe Illustrator Artwork)

• CDR (CorelDRAW)

• DrawingML

• GEM metafiles (interpreted and written by the Graphics Environment Manager VDI subsystem)

• Graphics Layout Engine

• HPGL, introduced on Hewlett-Packard plotters, but generalized into a printer language

• HVIF (Haiku Vector Icon Format)

• MathML 52 CHAPTER 6. DAY 6

• NAPLPS (North American Presentation Layer Protocol Syntax)

• ODG (OpenDocument Graphics)

• !DRAW, a native vector graphic format (in several backward compatible versions) for the RISC-OS computer system begun by in the mid-1980s and still present on that platform today

• POV-Ray markup language

• Precision Graphics Markup Language, a W3C submission that was not adopted as a recommendation.

• PSTricks and PGF/TikZ are languages for creating graphics in TeX documents.

• ReGIS, used by DEC computer terminals

• Remote imaging protocol

• VML ()

• WMF / EMF (Windows Metafile / Enhanced Metafile)

• format used in vector applications from Xara

• XPS (XML Paper Specification)

3D vector formats

• AMF - Additive Manufacturing File Format

• Asymptote - A language that lifts TeX to 3D.

• .blend -

• COLLADA

• .dgn

• .dwf

• .dwg

• .dxf

• eDrawings

• .flt - OpenFlight

• HSF

• IGES

• IMML - Immersive Media Markup Language

• IPA

• JT

• .MA (Maya ASCII format)

• .MB (Maya Binary format)

• .OBJ (Alias|Wavefront file format)

• OpenGEX - Open Game Engine Exchange

• PRC

• STEP 6.1. IMAGE FILE FORMATS 53

• SKP

• STL - A stereolithography format • U3D - file format

• VRML - Virtual Reality Modeling Language • XAML

• XGL • XVL

• xVRML •

• .3D

• 3DF • .3DM

• .3ds - Autodesk 3D Studio • 3DXML

• X3D - Vector format used in 3D applications from Xara

Compound formats (see also Metafile)

These are formats containing both pixel and vector data, possible other data, e.g. the interactive features of PDF.

• EPS (Encapsulated PostScript)

• PDF (Portable Document Format) • PostScript, a page description language with strong graphics capabilities

• PICT (Classic Macintosh QuickDraw file) • SWF (Shockwave )

• XAML User interface language using vector graphics for images.

Stereo formats

• MPO The Multi Picture Object (.mpo) format consists of multiple JPEG images (Camera & Imaging Products Association) (CIPA). • PNS The PNG Stereo (.pns) format consists of a side-by-side image based on PNG (Portable Network Graph- ics). • JPS The JPEG Stereo (.jps) format consists of a side-by-side image format based on JPEG.

6.1.4 References

[1] Andreas Kleinert (2007). “GIF 24 Bit (truecolor) extensions”. Retrieved 23 March 2012.

[2] Philip Howard. “True-Color GIF Example”. Retrieved 23 March 2012.

[3] “Gerber File Format Specification”. Ucamco. Chapter 7

Day 7

7.1 Pixel

This article is about the picture element. For other uses, see Pixel (disambiguation). In digital imaging, a pixel, pel,[1] dots, or picture element[2] is a physical point in a raster image, or the smallest

This example shows an image with a portion greatly enlarged, in which the individual pixels are rendered as small squares and can easily be seen.

addressable element in an all points addressable display device; so it is the smallest controllable element of a picture represented on the screen. The address of a pixel corresponds to its physical coordinates. LCD pixels are manufac- tured in a two-dimensional grid, and are often represented using dots or squares, but CRT pixels correspond to their timing mechanisms . Each pixel is a sample of an original image; more samples typically provide more accurate representations of the original. The intensity of each pixel is variable. In color imaging systems, a color is typically represented by three or four component intensities such as red, green, and blue, or cyan, magenta, yellow, and black. In some contexts (such as descriptions of camera sensors), the term pixel is used to refer to a single scalar element of a multi-component representation (more precisely called a photosite in the camera sensor context, although the neologism sensel is sometimes used to describe the elements of a digital camera’s sensor),[3] while in yet other contexts the term may be used to refer to the set of component intensities for a spatial position, though this is more accurately termed a sample. Drawing a distinction between pixels, photosite and samples avoids confusion when describing color systems that use chroma subsampling or cameras that use Bayer filter to produce color components via upsampling. The word pixel is based on a contraction of pix (from word “pictures”, where it is shortened to “pics”, and “cs” in “pics” sounds like “x”) and el (for “element”); similar formations with 'el' include the words ,[4] texel[4] and maxel

54 7.1. PIXEL 55

A photograph of sub-pixel display elements on a laptop’s LCD screen

(for magnetic pixel).[5]

7.1.1 Etymology

The word “pixel” was first published in 1965 by Frederic C. Billingsley of JPL, to describe the picture elements of video images from space probes to the Moon and Mars.[6] Billingsley had learned the word from Keith E. McFar- land, at the Link Division of General Precision in Palo Alto, who in turn said he did not know where it originated. McFarland said simply it was “in use at the time” (circa 1963).[7] The word is a combination of pix, for picture, and element. The word pix appeared in Variety magazine headlines in 1932, as an abbreviation for the word pictures, in reference to movies.[8] By 1938, “pix” was being used in reference to still pictures by photojournalists.[7] The concept of a “picture element” dates to the earliest days of television, for example as "Bildpunkt" (the German word for pixel, literally 'picture point') in the 1888 German patent of Paul Nipkow. According to various etymologies, the earliest publication of the term picture element itself was in Wireless World magazine in 1927,[9] though it had been used earlier in various U.S. patents filed as early as 1911.[10] Some authors explain pixel as picture cell, as early as 1972.[11] In graphics and in image and video processing, pel is often used instead of pixel.[12] For example, IBM used it in their Technical Reference for the original PC. Pixilation, spelled with a second i, is an unrelated filmmaking technique that dates to the beginnings of cinema, in which live actors are posed frame by frame and photographed to create stop-motion animation. An archaic British word meaning “possession by spirits (pixies),” the term has been used to describe the animation process since the early 1950s; various animators, including Norman McLaren and Grant Munro, are credited with popularizing it.[13] 56 CHAPTER 7. DAY 7

7.1.2 Technical

A pixel does not need to be rendered as a small square. This image shows alternative ways of reconstructing an image from a set of pixel values, using dots, lines, or smooth filtering.

A pixel is generally thought of as the smallest single component of a digital image. However, the definition is highly context-sensitive. For example, there can be "printed pixels" in a page, or pixels carried by electronic signals, or represented by digital values, or pixels on a display device, or pixels in a digital camera (photosensor elements). This list is not exhaustive and, depending on context, synonyms include pel, sample, byte, bit, dot, and spot. Pixels can be used as a unit of measure such as: 2400 pixels per inch, 640 pixels per line, or spaced 10 pixels apart. The measures dots per inch (dpi) and pixels per inch (ppi) are sometimes used interchangeably, but have distinct meanings, especially for printer devices, where dpi is a measure of the printer’s density of dot (e.g. ink droplet) placement.[14] For example, a high-quality photographic image may be printed with 600 ppi on a 1200 dpi inkjet printer.[15] Even higher dpi numbers, such as the 4800 dpi quoted by printer manufacturers since 2002, do not mean much in terms of achievable resolution.[16] The more pixels used to represent an image, the closer the result can resemble the original. The number of pixels in an image is sometimes called the resolution, though resolution has a more specific definition. Pixel counts can be expressed as a single number, as in a “three-megapixel” digital camera, which has a nominal three million pixels, or as a pair of numbers, as in a “640 by 480 display”, which has 640 pixels from side to side and 480 from top to bottom (as in a VGA display), and therefore has a total number of 640×480 = 307,200 pixels or 0.3 megapixels. The pixels, or color samples, that form a digitized image (such as a JPEG file used on a web page) may or may not be in one-to-one correspondence with screen pixels, depending on how a computer displays an image. In computing, an image composed of pixels is known as a bitmapped image or a raster image. The word raster originates from television scanning patterns, and has been widely used to describe similar halftone printing and storage techniques.

Sampling patterns

For convenience, pixels are normally arranged in a regular two-dimensional grid. By using this arrangement, many common operations can be implemented by uniformly applying the same operation to each pixel independently. Other arrangements of pixels are possible, with some sampling patterns even changing the shape (or kernel) of each pixel across the image. For this reason, care must be taken when acquiring an image on one device and displaying it on another, or when converting image data from one pixel format to another. For example:

• LCD screens typically use a staggered grid, where the red, green, and blue components are sampled at slightly different locations. Subpixel rendering is a technology which takes advantage of these differences to improve the rendering of text on LCD screens.

• The vast majority of color digital cameras use a Bayer filter, resulting in a regular grid of pixels where the color of each pixel depends on its position on the grid.

• A clipmap uses a hierarchical sampling pattern, where the size of the support of each pixel depends on its location within the hierarchy. 7.1. PIXEL 57

Text rendered using ClearType

• Warped grids are used when the underlying geometry is non-planar, such as images of the earth from space.[17]

• The use of non-uniform grids is an active research area, attempting to bypass the traditional Nyquist limit.[18]

• Pixels on computer monitors are normally “square” (this is, having equal horizontal and vertical sampling pitch); pixels in other systems are often “rectangular” (that is, having unequal horizontal and vertical sampling pitch – oblong in shape), as are digital video formats with diverse aspect ratios, such as the anamorphic widescreen formats of the Rec. 601 digital video standard.

Resolution of computer monitors

Computers can use pixels to display an image, often an abstract image that represents a GUI. The resolution of this image is called the display resolution and is determined by the video card of the computer. LCD monitors also use pixels to display an image, and have a native resolution. Each pixel is made up of triads, with the number of these triads determining the native resolution. On some CRT monitors, the beam sweep rate may be fixed, resulting in a fixed native resolution. Most CRT monitors do not have a fixed beam sweep rate, meaning they do not have a native resolution at all - instead they have a set of resolutions that are equally well supported. To produce the sharpest images possible on an LCD, the user must ensure the display resolution of the computer matches the native resolution of the monitor.

Resolution of telescopes

The pixel scale used in astronomy is the angular distance between two objects on the sky that fall one pixel apart on the detector (CCD or infrared chip). The scale s measured in radians is the ratio of the pixel spacing p and f of the preceding optics, s=p/f. (The focal length is the product of the focal ratio by the diameter of the associated lens or mirror.) Because p is usually expressed in units of arcseconds per pixel, because 1 radian equals 180/π*3600≈206,265 arcseconds, and because diameters are often given in millimeters and pixel sizes in micrometers which yields another factor of 1,000, the formula is often quoted as s=206p/f.

Bits per pixel

Main article: Color depth

The number of distinct colors that can be represented by a pixel depends on the number of bits per pixel (bpp). A 1 bpp image uses 1-bit for each pixel, so each pixel can be either on or off. Each additional bit doubles the number of colors available, so a 2 bpp image can have 4 colors, and a 3 bpp image can have 8 colors:

• 1 bpp, 21 = 2 colors (monochrome)

• 2 bpp, 22 = 4 colors

• 3 bpp, 23 = 8 colors

... 58 CHAPTER 7. DAY 7

• 8 bpp, 28 = 256 colors

• 16 bpp, 216 = 65,536 colors ("Highcolor")

• 24 bpp, 224 = 16,777,216 colors ("Truecolor")

For color depths of 15 or more bits per pixel, the depth is normally the sum of the bits allocated to each of the red, green, and blue components. Highcolor, usually meaning 16 bpp, normally has five bits for red and blue, and six bits for green, as the human eye is more sensitive to errors in green than in the other two primary colors. For applications involving transparency, the 16 bits may be divided into five bits each of red, green, and blue, with one bit left for transparency. A 24-bit depth allows 8 bits per component. On some systems, 32-bit depth is available: this means that each 24-bit pixel has an extra 8 bits to describe its opacity (for purposes of combining with another image).

Subpixels

Geometry of color elements of various CRT and LCD displays; phosphor dots in a color CRTs display (top row) bear no relation to pixels or subpixels.

Many display and image-acquisition systems are, for various reasons, not capable of displaying or sensing the different color channels at the same site. Therefore, the pixel grid is divided into single-color regions that contribute to the displayed or sensed color when viewed at a distance. In some displays, such as LCD, LED, and plasma displays, 7.1. PIXEL 59 these single-color regions are separately addressable elements, which have come to be known as subpixels.[19] For example, LCDs typically divide each pixel vertically into three subpixels. When the square pixel is divided into three subpixels, each subpixel is necessarily rectangular. In display industry terminology, subpixels are often referred to as pixels, as they are the basic addressable elements in a viewpoint of hardware, and hence pixel circuits rather than subpixel circuits is used. Most digital camera image sensors use single-color sensor regions, for example using the Bayer filter pattern, and in the camera industry these are known as pixels just like in the display industry, not subpixels. For systems with subpixels, two different approaches can be taken:

• The subpixels can be ignored, with full-color pixels being treated as the smallest addressable imaging element; or • The subpixels can be included in rendering calculations, which requires more analysis and processing time, but can produce apparently superior images in some cases.

This latter approach, referred to as subpixel rendering, uses knowledge of pixel geometry to manipulate the three colored subpixels separately, producing an increase in the apparent resolution of color displays. While CRT displays use red-green-blue-masked phosphor areas, dictated by a mesh grid called the shadow mask, it would require a difficult calibration step to be aligned with the displayed pixel raster, and so CRTs do not currently use subpixel rendering. The concept of subpixels is related to samples.

7.1.3 Megapixel

640 × 480 ≈ 0,3 MP

1134 × 756 ≈ 0,9 MP 1024 × 768 ≈ 0,7 MP 1152 × 864 ≈ 1,0 MP 1440 × 960 ≈ 1,4 MP 1280 × 960 ≈ 1,2 MP 1536 × 1024 ≈ 1,6 MP 1920 × 1080 ≈ 2,0 MP 1756 × 1168 ≈ 2,0 MP 1600 × 1200 ≈ 1,9 MP 1720 × 1280 ≈ 2,2 MP 2048 × 1360 ≈ 2,8 MP 2048 × 1536 ≈ 3,1 MP

2592 × 1728 ≈ 4,5 MP 2560 × 1920 ≈ 4,9 MP 3008 × 2008 ≈ 6,0 MP 3072 × 2048 ≈ 6,3 MP

3504 × 2336 ≈ 8,2 MP 3264 × 2448 ≈ 8,0 MP 4520 × 2540 ≈ 11,5 MP 3888 × 2592 ≈ 10,0 MP

4256 × 2848 ≈ 12,0 MP

4048 × 3040 ≈ 12,0 MP

4992 × 3328 ≈ 16,6 MP 5184 × 3456 ≈ 18,0 MP

5472 × 3648 ≈ 20,0 MP

5760 × 3840 ≈ 22,0 MP 6000 × 4000 = 24,0 MP 6048 × 4032 ≈ 24,3 MP 16:9

3:2 4:3

7360 × 4912 ≈ 36,0 MP

Diagram of common sensor resolutions of digital cameras including megapixel values

A megapixel (MP) is a million pixels; the term is used not only for the number of pixels in an image, but also to express the number of image sensor elements of digital cameras or the number of display elements of digital displays. For example, a camera that makes a 2048×1536 pixel image (3,145,728 finished image pixels) typically uses a few extra rows and columns of sensor elements and is commonly said to have “3.2 megapixels” or “3.4 megapixels”, depending on whether the number reported is the “effective” or the “total” pixel count.[20] Digital cameras use photosensitive electronics, either charge-coupled device (CCD) or complementary metal–oxide– semiconductor (CMOS) image sensors, consisting of a large number of single sensor elements, each of which records a measured intensity level. In most digital cameras, the sensor array is covered with a patterned color filter mosaic having red, green, and blue regions in the Bayer filter arrangement, so that each sensor element can record the intensity of a single of light. The camera interpolates the color information of neighboring sensor 60 CHAPTER 7. DAY 7

Marking on a that has about 2 million effective pixels. elements, through a process called demosaicing, to create the final image. These sensor elements are often called “pixels”, even though they only record 1 channel (only red, or green, or blue) of the final color image. Thus, two of the three color channels for each sensor must be interpolated and a so-called N-megapixel camera that produces an N-megapixel image provides only one-third of the information that an image of the same size could get from a scanner. Thus, certain color contrasts may look fuzzier than others, depending on the allocation of the primary colors (green has twice as many elements as red or blue in the Bayer arrangement). DxO Labs invented the Perceptual MegaPixel (P-MPix) to measure the sharpness that a camera produces when paired to a particular lens – as opposed to the MP a manufacturer states for a camera product which is based only on the camera’s sensor. The new P-MPix claims to be a more accurate and relevant value for photographers to consider when weighing-up camera sharpness.[21] As of mid-2013, the Sigma 35mm F1.4 DG HSM mounted on a D800 has the highest measured P-MPix. However, with a value of 23 MP, it still wipes-off more than one-third of the D800’s 36.3 MP sensor.[22] A camera with a full-frame image sensor, and a camera with an APS-C image sensor, may have the same pixel count (for example, 16 MP), but the full-frame camera may allow better dynamic range, less noise, and improved low- light shooting performance than an APS-C camera. This is because the full-frame camera has a larger image sensor than the APS-C camera, therefore more information can be captured per pixel. A full-frame camera that shoots photographs at 36 megapixels has roughly the same pixel size as an APS-C camera that shoots at 16 megapixels.[23] One new method to add Megapixels has been introduced in a Micro camera which only uses 16MP sensor, but can produce 64MP RAW (40MP JPEG) by expose-shift-expose-shift the sensor a half pixel each time to both directions. Using a to take level multi-shots within an instance, the multiple 16MP images are then generated into a unified 64MP image.[24]

7.1.4 See also

• Computer display standard • Dexel • Gigapixel image 7.1. PIXEL 61

• Image resolution

• Intrapixel and Interpixel processing

• LCD crosstalk

• PenTile matrix family

• Pixel advertising

• Pixel art

• Pixel art scaling algorithms

• Pixel

• Point (typography)

• Glossary of video terms

• Voxel

7.1.5 References

[1] Foley, J. D.; Van Dam, A. (1982). Fundamentals of Interactive Computer Graphics. Reading, MA: Addison-Wesley. ISBN 0201144689.

[2] Rudolf F. Graf (1999). Modern Dictionary of Electronics. Oxford: Newnes. p. 569. ISBN 0-7506-4331-5.

[3] Michael Goesele (2004). New Acquisition Techniques for Real Objects and Light Sources in Computer Graphics. Books on Demand. ISBN 3-8334-1489-8.

[4] Foley, James D.; Andries van Dam; John F. Hughes; Steven K. Feiner (1990). “Spatial-partitioning representations; Surface detail”. Computer Graphics: Principles and Practice. The Systems Programming Series. Addison-Wesley. ISBN 0-201- 12110-7. These cells are often called (volume elements), in analogy to pixels.

[5] By. “Just When You Thought Magnets Weren't Magic; Magnets Are Mechanisms”. Hackaday. Retrieved 2016-03-22.

[6] Fred C. Billingsley, “Processing Ranger and Mariner Photography,” in Computerized Imaging Techniques, Proceedings of SPIE, Vol. 0010, pp. XV-1–19, Jan. 1967 (Aug. 1965, San Francisco).

[7] Lyon, Richard F. (2006). A brief history of 'pixel'. IS&T/SPIE Symposium on Electronic Imaging.

[8] “Online Etymology Dictionary”.

[9] “On language; Modem, I'm Odem”, , April 2, 1995. Accessed April 7, 2008.

[10] Sinding-Larsen, Alf Transmission of Pictures of Moving Objects, US Patent 1,175,313, issued March 14, 1916.

[11] Robert L. Lillestrand (1972). “Techniques for Change Detection”. IEEE Trans. Comput. C–21 (7).

[12] Lewis, Peter H. (February 12, 1989). The Executive Computer; Compaq Sharpens Its Video Option. The New York Times.

[13] Frame by Frame Stop Motion: NonTraditional Approaches to Stop Motion Animation - Tom Gasek - Google Books

[14] Derek Doeffinger (2005). The Magic of Digital Printing. Lark Books. p. 24. ISBN 1-57990-689-3.

[15] “Experiments with Pixels Per Inch (PPI) on Printed Image Sharpness”. ClarkVision.com. July 3, 2005.

[16] Harald Johnson (2002). Mastering Digital Printing. Thomson Course Technology. p. 40. ISBN 1-929685-65-3.

[17] “Image registration of blurred satellite images”. Retrieved 2008-05-09.

[18] “ScienceDirect - Pattern Recognition: Image representation by a new optimal non-uniform morphological sampling:". Retrieved 2008-05-09.

[19] "Subpixel in Science”. dictionary.com. Retrieved 4 July 2015.

[20] Now a megapixel is really a megapixel 62 CHAPTER 7. DAY 7

[21] http://www.dxomark.com/en/Reviews/Looking-for-new-photo-gear-DxOMark-s-Perceptual-Megapixel-can-help-you

[22] http://www.dxomark.com/index.php/Lenses/Camera-Lens-Ratings/Optical-Metric-Scores

[23] “Camera sensor size: Why does it matter and exactly how big are they?". March 21, 2013.

[24] Damien Demolder (February 14, 2015). “Soon, 40MP without the tripod: A conversation with Setsuya Kataoka from Olympus”. Retrieved March 8, 2015.

7.1.6 External links

• A Pixel Is Not A Little Square: Microsoft Memo by computer graphics pioneer Alvy Ray Smith.

• Video of talk on pixel history at the Computer History Museum • Square and non-Square Pixels: Technical info on pixel aspect ratios of modern video standards (480i, 576i, 1080i, 720p), plus software implications. • 120 Megapixel is here now: A lot of information about MegaPixel and Gigapixel. Chapter 8

Day 8

8.1 Color histogram

Not to be confused with Image histogram.

In image processing and photography, a color histogram is a representation of the distribution of colors in an image. For digital images, a color histogram represents the number of pixels that have colors in each of a fixed list of color ranges, that span the image’s , the set of all possible colors. The color histogram can be built for any kind of color space, although the term is more often used for three- dimensional spaces like RGB or HSV. For monochromatic images, the term intensity histogram may be used instead. For multi-spectral images, where each pixel is represented by an arbitrary number of measurements (for example, beyond the three measurements in RGB), the color histogram is N-dimensional, with N being the number of measurements taken. Each measurement has its own wavelength range of the light spectrum, some of which may be outside the visible spectrum. If the set of possible color values is sufficiently small, each of those colors may be placed on a range by itself; then the histogram is merely the count of pixels that have each possible color. Most often, the space is divided into an appropriate number of ranges, often arranged as a regular grid, each containing many similar color values. The color histogram may also be represented and displayed as a smooth function defined over the color space that approximates the pixel counts. Like other kinds of histograms, the color histogram is a statistic that can be viewed as an approximation of an underlying continuous distribution of colors values.

8.1.1 Overview

Color histograms are flexible constructs that can be built from images in various color spaces, whether RGB, rg chromaticity or any other color space of any dimension. A histogram of an image is produced first by discretization of the colors in the image into a number of bins, and counting the number of image pixels in each bin. For example, a Red–Blue chromaticity histogram can be formed by first normalizing color pixel values by dividing RGB values by R+G+B, then quantizing the normalized R and B coordinates into N bins each. A two-dimensional histogram of Red-Blue chromaticity divided into four bins (N=4) might yield a histogram that looks like this table: A histogram can be N-dimensional. Although harder to display, a three-dimensional color histogram for the above example could be thought of as four separate Red-Blue histograms, where each of the four histograms contains the Red-Blue values for a bin of green (0-63, 64-127, 128-191, and 192-255). The histogram provides a compact summarization of the distribution of data in an image. The color histogram of an image is relatively invariant with translation and rotation about the viewing axis, and varies only slowly with the .[1] By comparing histograms signatures of two images and matching the color content of one image with the other, the color histogram is particularly well suited for the problem of recognizing an object of unknown position and rotation within a scene. Importantly, translation of an RGB image into the illumination invariant rg-chromaticity space allows the histogram to operate well in varying light levels.

63 64 CHAPTER 8. DAY 8

8.1.2 Definition

1.What is a histogram? A histogram is a graphical representation of the number of pixels in an image. In a more simple way to explain, a histogram is a bar graph, whose X-axis represents the tonal scale(black at the left and white at the right), and Y-axis represents the number of pixels in an image in a certain area of the tonal scale. For example, the graph of a luminance histogram shows the number of pixels for each brightness level(from black to white), and when there are more pixels, the peak at the certain luminance level is higher. 2.What is a color histogram? A color histogram of an image represents the distribution of the composition of colors in the image. It shows different types of colors appeared and the number of pixels in each type of the colors appeared. The relation between a color histogram and a luminance histogram is that a color histogram can be also expressed as “Three Color Histograms”, each of which shows the brightness distribution of each individual Red/Green/Blue color channel.

8.1.3 Characteristics of a color histogram

Note that a color histogram focuses only on the proportion of the number of different types of colors, regardless of the spatial location of the colors. The values of a color histogram are from statistics. They show the statistical distribution of colors and the essential tone of an image. Note that in general, as the color distributions of the foreground and background in an image are different, there might be a bimodal distribution in the histogram. Also note that for the luminance histogram alone, there is no perfect histogram and in general, the histogram can tell whether it is over exposure or not, but there are times when you might think the image is over exposed by viewing the histogram; however, in reality it is not.

8.1.4 Principles of the formation of a color histogram

The formation of a color histogram is rather simple. From the definition above, we can simply count the number of pixels for each 256 scales in each of the 3 RGB channel, and plot them on 3 individual bar graphs. In general, a color histogram is based on a certain color space, such as RGB or HSV. When we compute the pixels of different colors in an image, if the color space is large, then we can first divide the color space into certain numbers of small intervals. Each of the intervals is called a bin. This process is called . Then, by counting the number of pixels in each of the bins, we get the color histogram of the image. The concrete steps of the principles can be viewed in Example 2.

8.1.5 Examples

Example 1

Given the following image of a cat (an original version and a version that has been reduced to 256 colors for easy histogram purposes), the following data represents a color histogram in the RGB color space, using four bins. Bin 0 corresponds to intensities 0-63, bin 1 is 64-127, bin 2 is 128-191, and bin 3 is 192-255.

Example 2

Application in camera: Nowadays, some cameras have the ability of showing the 3 color histograms when we take photos. We can examine clips(spikes on either the black or white side of the scale) in each of the 3 RGB color histograms. If we find one or more clipping on a channel of the 3 RGB channels, then this would result in a loss of detail for that color. To illustrate this, consider this example: 8.1. COLOR HISTOGRAM 65

A picture of a cat

Color histogram of the above cat picture with x-axis being RGB and y-axis being the frequency.

1. We know that each of the three R,G,B channels has a range of values from 0-255(8 bit). So consider a photo that has a luminance range of 0-255. 2. Assume the photo we take is made of 4 blocks that are adjacent to each other and we set the luminance scale for each of the 4 blocks of original photo to be 10, 100, 205, 245. Thus, the image looks like the first figure on the right. 3. Then, we over expose the photo a little, say, the luminance scale of each block is increased by 10. Thus, the luminance scale for each of the 4 blocks of new photo is 20, 110, 215, 255. Then, the image looks like the second figure on the right. There is not much difference between figure 8 and figure 9, all we can see is that the whole image becomes brighter(the contrast for each of the blocks remain the same). 66 CHAPTER 8. DAY 8

A picture of a cat reduced to 256 colors in the RGB color space

4. Now, we over expose the original photo again, this time the luminance scale of each block is increased by 50. Thus, the luminance scale for each of the 4 blocks of new photo is 60, 150, 255, 255. The new image now looks like the third figure on the right. Note that the scale for last block is 255 instead of 295, for 255 is the top scale and thus the last block has clipped! When this happens, we lose the contrast of the last 2 blocks, and thus, we cannot recover the image no matter how we adjust it. To conclude, when taking photos with a camera that displays histograms, always keep the brightest tone in the image below the largest scale 255 on the histogram in order to avoid losing details.

8.1.6 Drawbacks and other approaches

The main drawback of histograms for classification is that the representation is dependent of the color of the object being studied, ignoring its shape and texture. Color histograms can potentially be identical for two images with different object content which happens to share color information. Conversely, without spatial or shape information, similar objects of different color may be indistinguishable based solely on color histogram comparisons. There is no way to distinguish a red and white cup from a red and white plate. Put another way, histogram-based algorithms have no concept of a generic 'cup', and a model of a red and white cup is no use when given an otherwise identical blue and white cup. Another problem is that color histograms have high sensitivity to noisy interference such as lighting intensity changes and quantization errors. High dimensionality (bins) color histograms are also another issue. Some color histogram feature spaces often occupy more than one hundred dimensions.[2] Some of the proposed solutions have been color histogram intersection, color constant indexing, cumulative color histogram, quadratic distance, and color correlograms. Although there are drawbacks of using histograms for indexing and classification, using color in a real-time system has several advantages. One is that color information is faster to compute compared to other invariants. It has been shown in some cases that color can be an efficient method for identifying objects of known location and appearance. 8.1. COLOR HISTOGRAM 67

Further research into the relationship between color histogram data to the physical properties of the objects in an image has shown they can represent not only object color and illumination but relate to surface roughness and image geometry and provide an improved estimate of illumination and object color.[3] Usually, Euclidean distance, histogram intersection, or cosine or quadratic distances are used for the calculation of image similarity ratings.[4] Any of these values do not reflect the similarity rate of two images in itself; it is useful only when used in comparison to other similar values. This is the reason that all the practical implementations of content- based image retrieval must complete computation of all images from the database, and is the main disadvantage of these implementations. Another approach to representative color image content is two-dimensional color histogram. A two-dimensional color histogram considers the relation between the pixel pair colors (not only the lighting component).[5] A two-dimensional color histogram is a two-dimensional array. The size of each dimension is the number of colors that were used in the phase of color quantization. These arrays are treated as matrices, each element of which stores a normalized count of pixel pairs, with each color corresponding to the index of an element in each pixel neighborhood. For comparison of two-dimensional color histograms it is suggested calculating their correlation, because constructed as described above, is a random vector (in other words, a multi-dimensional random value). While creating a set of final images, the images should be arranged in decreasing order of the correlation coefficient. The correlation coefficient may also be used for color histogram comparison. Retrieval results with correlation co- efficient are better than with other metrics.[6]

8.1.7 Intensity histogram of continuous data

The idea of an intensity histogram can be generalized to continuous data, say audio signals represented by real func- tions or images represented by functions with two-dimensional domain. 68 CHAPTER 8. DAY 8

Let f ∈ L1(Rn) (see Lebesgue space), then the cumulative histogram operator H can be defined by:

H(f)(y) = µ{x : f(x) ≤ y} µ is the Lebesgue measure of sets. H(f) in turn is a real function. The (non-cumulative) histogram is defined as its derivative. h(f) = H(f)′

8.1.8 References

[1] Shapiro, Linda G. and Stockman, George C. “Computer Vision” Prentice Hall, 2003 ISBN 0-13-030796-3 [2] Xiang-Yang Wang, Jun-Feng Wu1 and Hong-Ying Yang “Robust image retrieval based on color histogram of local feature regions” Springer Netherlands, 2009 ISSN 1573-7721 [3] Anatomy of a color histogram; Novak, C.L.; Shafer, S.A.; Computer Vision and Pattern Recognition, 1992. Proceedings CVPR '92., 1992 IEEE Computer Conference on 15–18 June 1992 Page(s):599 - 605 doi:10.1109/CVPR.1992.223129 [4] Integrated Spatial and Feature Image Systems: Retrieval, Analysis and Compression; Smith, J.R.; Graduate School of Arts and Sciences, Columbia University, 1997 [5] Effectiveness estimation of image retrieval by 2D color histogram; Bashkov, E.A.; Kostyukova, N.S.; Journal of Automation and Information Sciences, 2006 (6) Page(s): 84-89 [6] Content-Based Image Retrieval Using Color Histogram Correlation; Bashkov, E.A.; Shozda, N.S.; Graphicon proceedings, 2002 Page(s): 8.2. IMAGE HISTOGRAM 69

8.1.9 External links

• 3D Color Inspector/Color Histogram, by Kai Uwe Barthel. (Free Java applet.)

• QBIC Image Retrieval, by State Hermitage Museum

• Stanford Student Project on Image Based Retrieval - more in depth look at equations/application

• MATLAB/Octave code for plotting Color Histograms and Color Clouds - The source code can be ported to other languages

8.2 Image histogram

Sunflower image 70 CHAPTER 8. DAY 8

Histogram of sunflower image

An image histogram is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image.[1] It plots the number of pixels for each tonal value. By looking at the histogram for a specific image a viewer will be able to judge the entire tonal distribution at a glance. Image histograms are present on many modern digital cameras. Photographers can use them as an aid to show the distribution of tones captured, and whether image detail has been lost to blown-out highlights or blacked-out shadows.[2] This is less useful when using a raw image format, as the dynamic range of the displayed image may only be an approximation to that in the raw file. The horizontal axis of the graph represents the tonal variations, while the vertical axis represents the number of pixels in that particular tone.[1] The left side of the horizontal axis represents the black and dark areas, the middle represents medium grey and the right hand side represents light and pure white areas. The vertical axis represents the size of the area that is captured in each one of these zones. Thus, the histogram for a very dark image will have the majority of its data points on the left side and center of the graph. Conversely, the histogram for a very bright image with few dark areas and/or shadows will have most of its data points on the right side and center of the graph.

8.2.1 Image manipulation and histograms

Image editors typically have provisions to create a histogram of the image being edited. The histogram plots the number of pixels in the image (vertical axis) with a particular brightness value (horizontal axis). Algorithms in the digital editor allow the user to visually adjust the brightness value of each pixel and to dynamically display the results as adjustments are made.[3] Improvements in picture brightness and contrast can thus be obtained. In the field of computer vision, image histograms can be useful tools for thresholding. Because the information contained in the graph is a representation of pixel distribution as a function of tonal variation, image histograms can be analyzed for peaks and/or valleys. This threshold value can then be used for edge detection, image segmentation, and co-occurrence matrices.

8.2.2 See also

• Image editing • Color histogram, a multidimensional histogram of the distribution of color in an image • Histogram equalization • Histogram matching

8.2.3 References

[1] Ed Sutton. “Histograms and the ”. Illustrated Photography. [2] Michael Freeman (2005). The Digital SLR Handbook. Ilex. ISBN 1-904705-36-7. [3] Martin Evening (2007). Adobe Photoshop CS3 for Photographers: A Professional Image Editor’s Guide... Focal Press. ISBN 0-240-52028-9.

8.2.4 External links

• CAMERA HISTOGRAMS: TONES & CONTRAST at cambridgeincolour.com Chapter 9

Day 9

9.1 Pixel density

Pixels per inch (PPI) or pixels per centimeter (PPCM) is a measurement of the pixel density (resolution) of an electronic image device, such as a computer monitor or television display, or image digitizing device such as a camera or image scanner. Horizontal and vertical density are usually the same, as most devices have square pixels, but differ on devices that have non-square pixels. PPI can also describe the resolution, in pixels, of an image file. The unit is not square centimeters—a 100×100 pixel image printed in a 1 cm square has a resolution of 100 pixels per centimeter (ppcm). Used this way, the measurement is meaningful when printing an image. It has become commonplace to refer to PPI as DPI, even though PPI refers to input resolution. Industry standard, good quality photographs usually require 300 pixels per inch, at 100% size, when printed onto coated paper stock, using a printing screen of 150 lines per inch (lpi). This delivers a quality factor of 2, which is optimum. The lowest acceptable quality factor is considered 1.5, which equates to printing a 225 ppi image using a 150 lpi screen onto coated paper. Screen frequency is determined by the type of paper the image is printed on. An absorbent paper surface, uncoated recycled paper for instance, lets ink droplets spread (dot gain)—so requires a more open printing screen. Input resolution can therefore be reduced to minimize file size without loss in quality, as long as the quality factor of 2 is maintained. This is easily determined by doubling the line frequency. For example, printing on an uncoated paper stock often limits printing screen frequency to no more than 120 lpi, therefore, a quality factor of 2 is achieved with images of 240 ppi.

9.1.1 Computer displays

The PPI of a computer display is related to the size of the display in inches and the total number of pixels in the horizontal and vertical directions. This measurement is often referred to as dots per inch, though that measurement more accurately refers to the resolution of a computer printer. For example, a 15 inch (38 cm) display whose dimensions work out to 12 inches (30.48 cm) wide by 9 inches (22.86 cm) high, capable of a maximum 1024×768 (or XGA) pixel resolution, can display around 85 PPI in both the horizontal and vertical directions. This figure is determined by dividing the width (or height) of the display area in pixels by the width (or height) of the display area in inches. It is possible for a display to have different horizontal and vertical PPI measurements (e.g., a typical 4:3 ratio CRT monitor showing a 1280×1024 mode computer display at maximum size, which is a 5:4 ratio, not quite the same as 4:3). The apparent PPI of a monitor depends upon the screen resolution (that is, the number of pixels) and the size of the screen in use; a monitor in 800×600 mode has a lower PPI than does the same monitor in a 1024×768 or 1280×960 mode. The dot pitch of a computer display determines the absolute limit of possible pixel density. Typical circa-2000 cathode ray tube or LCD computer displays range from 67 to 130 PPI, though desktop monitors have exceeded 200 PPI and contemporary small-screen mobile devices often exceed 300 PPI, sometimes by a wide margin. In January 2008, Kopin Corporation announced a 0.44 inch (1.12 cm) SVGA LCD with a pixel density of 2272 PPI (each pixel only 11.25μm).[1][2] In 2011 they followed this up with a 3760 DPI 0.21” diagonal VGA colour display.[3]

71 72 CHAPTER 9. DAY 9

200 x 200 pixels

The outside of the square shown above is 200 pixels by 200 pixels at zoom 100%. To determine a monitor’s ppi, set the browser’s zoom at 100%, then measure the width and height, in inches, of the square as displayed on a given monitor. Dividing 200 by the measured width or height gives the monitor’s horizontal or vertical ppi, respectively, at the current screen resolution.

The manufacturer says they designed the LCD to be optically magnified, as in high-resolution eyewear devices. applications demand even greater pixel density, as higher pixel density produces a larger image size and wider viewing angle. Spatial light modulators can reduce pixel pitch to 2.5 μm, giving a pixel density of 10,160 PPI.[4] Some observations indicate that the unaided human generally can't differentiate detail beyond 300 PPI.[5] However, this figure depends both on the distance between viewer and image, and the viewer’s visual acuity. The human eye also responds in a different way to a bright, evenly lit interactive display than to prints on paper. High pixel density display technologies would make supersampled antialiasing obsolete, enable true WYSIWYG graphics and, potentially enable a practical “paperless office” era.[6] For perspective, such a device at 15 inch (38 cm) screen size would have to display more than four Full HD screens (or WQUXGA resolution). Development of a display with ~900 ppi allows for three pixels with 16-bit color to act as sub-pixels to form a pixel cluster. These pixel clusters act as regular pixels at ~300 ppi to produce a 48-bit color display. The PPI pixel density specification of a display is also useful for calibrating a monitor with a printer. Software can use the PPI measurement to display a document at “actual size” on the screen. 9.1. PIXEL DENSITY 73

Calculation of monitor PPI

Theoretically, PPI can be calculated from knowing the diagonal size of the screen in inches and the resolution in pixels (width and height). This can be done in two steps: 1. Calculate diagonal resolution in pixels using the Pythagorean theorem:

√ 2 2 dp = wp + hp

2. Calculate PPI:

d PPI = p di where

• dp is diagonal resolution in pixels

• wp is width resolution in pixels 74 CHAPTER 9. DAY 9

• hp is height resolution in pixels

• di is diagonal size in inches (this is the number advertised as the size of the display).

For example, :

• For a 21.5 inch (54.61 cm) screen with a 1920×1080 resolution (in which wp = 1920, hp = 1080 and di = 21.5), we get 102.46 PPI;

• For a typical 10.1 inch netbook screen with a 1024×600 resolution (in which wp = 1024, hp = 600 and di = 10.1), we get 117.5 PPI.

• For 27 inch screen with a 2560x1440 resolution we get = sqrt(2560^2+1440^2)/27 = 109 PPI

Note that these calculations may not be very precise. Frequently, screens advertised as “X inch screen” can have their real physical dimensions of viewable area differ, for example:

• Apple Inc. advertized their mid-2011 iMac as a “21.5 inch (viewable) [...] display,”[7] but its actual viewable area is 545.22 mm or 21.465 inches.[8] The more precise figure increases the calculated PPI from 102.46 (using 21.5) to 102.63.

• The HP LP2065 20 inch (50.8 cm) monitor has an actual viewable area of 20.1 inch (51 cm).[9]

Calculating PPI of camera view screens

Camera manufacturers often quote view screens in 'number of dots’. This is not the same as the number of pixels, because there are 3 'dots’ per pixel – red, green and blue. For example, the Canon 50d is quoted as having 920,000 dots.[10] This translates as 307,200 pixels (x3 = 921,600 dots). Thus the screen is 640×480 pixels.[11] This must be taken into account when working out the PPI. Using the above calculations requires the screen’s dimen- sions, but other methods require the total pixels, not total dots. 'Dots’ and 'pixels’ are often confused in reviews and specs when viewing information about digital cameras specifically.

9.1.2 Scanners and cameras

“PPI” or “pixel density” may also describe image scanner resolution. In this context, PPI is synonymous with samples per inch. In digital photography, pixel density is the number of pixels divided by the area of the sensor. A typical DSLR, circa 2013, has 1–6.2 MP/cm2; a typical compact has 20–70 MP/cm2. For example, Alpha SLT-A58 has 20.1 megapixels on an APS-C sensor having 6.2 MP/cm2 since a compact camera like Sony Cyber-shot DSC-HX50V has 20.4 megapixels on an 1/2.3” sensor having 70 MP/cm2. Interestingly, as can be seen here, the professional camera has a lower PPI than a compact camera, because it has larger photodiodes due to having far larger sensors.

9.1.3 Smartphones

Smartphones use small displays, but modern displays have a larger PPI rating, such as the Samsung Galaxy S6 Edge with a quad HD display at 577 PPI, Fujitsu F-02G with a quad HD display at 564 PPI,[12] the LG G3 with quad HD display at 534 PPI or - XHDPI or Oppo Find 7 with 534 PPI on 5.5” display - XXHDPI (see section below).[13] Sony has given out the Z5 Premium which has the largest PPI density phone on the market as of 2016, totaling to 806 PPI on a 5.5 inch phone.[14]

9.1.4 Named pixel densities

The Google Android developer documentation[15] groups displays by their approximate pixel densities[16] into the following categories: 9.1. PIXEL DENSITY 75

9.1.5 Metrication

See also: dots per centimetre

The digital publishing industry primarily uses “pixels per inch” but sometimes “pixels per centimeter” is used or a conversion factor is given.[19][20][21] The PNG image file format only allows the meter as the unit for pixel density.[22]

9.1.6 Image file format support

The following table show how pixel density is supported by often used image file formats. In the second column, length refers to horizontal and vertical size in inches, centimeters et cetera, whereas pixel refers only to the number of pixels found along the horizontal and vertical dimension. The cell colors used do not indicate how feature-rich a certain image file format is, but what density support can be expected of a certain image file format. Often-used image file formats that do not support pixel density are added for counter-example purposes. Even though image manipulation software can optionally set density for some image file formats, not many other software uses density information when displaying images. Web browsers, for example, ignore any density informa- tion. Named pixel densities is used mainly for browsers and mobile apps. As the table shows, support for density information in image file formats varies enormously and should be used with great care in a controlled context. * Support in SVG differs. The standard supports the floats pixelUnitToMillimeterX, pixelUnitToMillimeterY, screen- PixelToMillimeterX and screenPixelToMillimeterY for use in CSS2.[26] SVG supports density for PNG export only inkscape:export-xdpi and inkscape:export-ydpi.[27] Adobe stores it even differently.

9.1.7 See also

• Dots per inch

• Computer monitor DPI standards – the origins of 96 DPI/PPI as Microsoft/Windows standard and 72 DPI/PPI as (former) Apple/Macintosh standard

• Dot pitch

• Resolution independence

• Retina display Apple brand

9.1.8 References

[1] “Kopin unveils smallest color SVGA display”. optics.org. 11 January 2008. Retrieved 6 June 2008. External link in |publisher= (help)

[2] “Company Debuts World’s Smallest Color SVGA Display” (PDF). SID, Information Display magazine May 2008 Vol. 24, No. 05. 31 May 2008. Retrieved 6 June 2008. External link in |publisher= (help)

[3] “Innovations”. kopin corporation. Retrieved 22 May 2014.

[4] Horizontally scanning holography to enlarge both image size and viewing zone angle Naoya Okada and Yasuhiro Takaki, Proc. of SPIE Vol. 7233 723309-1

[5] “Apple Retina Display”. Jonesblog. 24 June 2010. Retrieved 25 September 2011. External link in |publisher= (help)

[6] “Electronic displays for information technology”. IBM Journal of Research and Development Volume 44, Number 3, 2000. 10 November 1999. Retrieved 6 June 2008. External link in |publisher= (help)

[7] Apple iMac Tech Specs, Apple Inc.. Accessed on 27 January 2012.

[8] LM215WF3 LCD Product Specification, LG Display. Accessed on 27 January 2012.

[9] HP LP2065 20-inch (50.8 cm) LCD Monitor - Specifications and Warranty (Hewlett-Packard Company official website) 76 CHAPTER 9. DAY 9

[10] dpreview.com, Canon EOS 50d

[11] Techcrunch.com, dots vs pixels

[12] (October 7, 2014). " ".

[13] Richard Lai (February 12, 2014). “Oppo’s next smartphone due in March with 2K and 1080p display options”.

[14] “Sony Xperia Z5 Premium - Full phone specifications”. www.gsmarena.com. Retrieved 2016-05-27.

[15] Providing Resources, Android Developers

[16] Material Design - Device Metrics

[17] Android reference for developers

[18] Android reference for developers

[19] “Web Graphics Basics”.

[20] “Utads.com Glossary of Terms”.

[21] “Resolution, dpi and ppi”.

[22] “PNG file format, pHYs chunk”.

[23] JPEG File Interchange Format, Version 1.02 - JPEG File Interchange Format Specification

[24] Chapter 11. PNG Options and Extensions - Physical Pixel Dimensions (pHYs)

[25] TIFF Revision 5.0 - ResolutionUnit

[26] Scalable Vector Graphics (SVG) 1.1 (Second Edition)

[27] Inkscape source files

9.1.9 External links

9.2 Dots per inch

“dpi” redirects here. For pixel density, see Pixel density. For other uses, see DPI. Monitors do not have dots, but do have pixels. The closely related concept for monitors and images is pixels per inch or PPI. Old CRT type video displays were almost universally rated in dot pitch, which refers to the spacing between the sub-pixel red, green and blue dots which made up the pixels themselves. Monitor manufacturers used the term “dot trio pitch”, the measurement of the distance between the centers of adjacent groups of three dots/rectangles/squares on the CRT screen. Monitors commonly used dot pitches of 0.39, 0.33, 0.32, 0.29, 0.27, 0.25, or 0.22 millimetres [mm] (0.0087 in). LCD monitors have a trio of sub pixels, which are more easily measured.

9.2.1 DPI measurement in printing

DPI is used to describe the resolution number of dots per inch in a digital print and the printing resolution of a hard copy print dot gain, which is the increase in the size of the halftone dots during printing. This is caused by the spreading of ink on the surface of the media. Up to a point, printers with higher DPI produce clearer and more detailed output. A printer does not necessarily have a single DPI measurement; it is dependent on print mode, which is usually influenced by driver settings. The range of DPI supported by a printer is most dependent on the print head technology it uses. A dot matrix printer, for example, applies ink via tiny rods striking an ink ribbon, and has a relatively low resolution, typically in the range of 60 to 90 DPI (420 to 280 µm). An inkjet printer sprays ink through tiny nozzles, and is typically capable of 300–720 DPI.[1] A laser printer applies toner through a controlled electrostatic charge, and may be in the range of 600 to 2,400 DPI. 9.2. DOTS PER INCH 77

A close-up of the dots produced by an inkjet printer at draft quality. Actual size is approximately 0.25 inch by 0.25 inch (0.403 cm2). Individual colored droplets of ink are visible; this sample is about 150 DPI.

The DP measurement of a printer often needs to be considerably higher than the pixels per inch (PPI) measurement of a video display in order to produce similar-quality output. This is due to the limited range of colors for each dot typically available on a printer. At each dot position, the simplest type of color printer can either print no dot, or print a dot consisting of a fixed volume of ink in each of four color channels (typically CMYK with cyan, magenta, yellow and black ink) or 24 = 16 colors on laser, wax and most inkjet printers, of which only 14 or 15 (or as few as 8 or 9) may be actually discernible depending on the strength of the black component, the strategy used for overlaying and combining it with the other colors, and whether it is in “color” mode. Higher-end inkjet printers can offer 5, 6 or 7 ink colors giving 32, 64 or 128 possible tones per dot location (and again, it can be that not all combinations will produce a unique result). Contrast this to a standard sRGB monitor where each pixel produces 256 intensities of light in each of three channels (RGB). While some color printers can produce variable drop volumes at each dot position, and may use additional ink-color channels, the number of colors is still typically less than on a monitor. Most printers must therefore produce additional colors through a halftone or dithering process, and rely on their base resolution being high enough to “fool” the human observer’s eye into perceiving a patch of a single smooth color. The exception to this rule is dye-sublimation printers, which can apply a much more variable amount of dye—close to or exceeding the number of the 256 levels per channel available on a typical monitor—to each “pixel” on the page 78 CHAPTER 9. DAY 9

without dithering, but with other limitations:

• lower spatial resolution (typically 200 to 300 dpi), which can make text and lines look somewhat rough • lower output speed (a single page requiring three or four complete passes, one for each dye color, each of which may take more than fifteen seconds—generally quicker, however, than most inkjet printers’ “photo” modes) • a wasteful (and, for confidential documents, insecure) dye-film roll cartridge system • occasional color registration errors (mainly along the long axis of the page), which necessitate recalibrating the printer to account for slippage and drift in the paper feed system.

These disadvantages mean that, despite their marked superiority in producing good photographic and non-linear diagrammatic output, dye-sublimation printers remain niche products, and devices using higher resolution, lower color depth, and patterns remain the norm. This dithered printing process could require a region of four to six dots (measured across each side) in order to faithfully reproduce the color in a single pixel. An image that is 100 pixels wide may need to be 400 to 600 dots in width in the printed output; if a 100×100-pixel image is to be printed in a one-inch square, the printer must be capable of 400 to 600 dots per inch to reproduce the image. Fittingly, 600 dpi (sometimes 720) is now the typical output resolution of entry-level laser printers and some utility inkjet printers, with 1200/1440 and 2400/2880 being common “high” resolutions. This contrasts with the 300/360 (or 240) dpi of early models, and the approximate 200 dpi of dot- matrix printers and fax machines, which gave faxed and computer-printed documents—especially those that made heavy use of graphics or colored block text—a characteristic “digitized” appearance, because of their coarse, obvious dither patterns, inaccurate colors, loss of clarity in photographs, and jagged (“aliased”) edges on some text and line art.

DPI or PPI in digital image files

In printing, DPI (dots per inch) refers to the output resolution of a printer or imagesetter, and PPI (pixels per inch) refers to the input resolution of a photograph or image. DPI refers to the physical dot density of an image when it is reproduced as a real physical entity, for example printed onto paper. A digitally stored image has no inherent physical dimensions, measured in inches or centimeters. Some digital file formats record a DPI value, or more commonly a PPI (pixels per inch) value, which is to be used when printing the image. This number lets the printer or software know the intended size of the image, or in the case of scanned images, the size of the original scanned object. For example, a bitmap image may measure 1,000 × 1,000 pixels, a resolution of 1 megapixel. If it is labeled as 250 PPI, that is an instruction to the printer to print it at a size of 4 × 4 inches. Changing the PPI to 100 in an image editing program would tell the printer to print it at a size of 10×10 inches. However, changing the PPI value would not change the size of the image in pixels which would still be 1,000 × 1,000. An image may also be resampled to change the number of pixels and therefore the size or resolution of the image, but this is quite different from simply setting a new PPI for the file. For vector images, there is no equivalent of resampling an image when it is resized, and there is no PPI in the file because it is resolution independent (prints equally well at all sizes). However, there is still a target printing size. Some image formats, such as Photoshop format, can contain both bitmap and vector data in the same file. Adjusting the PPI in a Photoshop file will change the intended printing size of the bitmap portion of the data and also change the intended printing size of the vector data to match. This way the vector and bitmap data maintain a consistent size relationship when the target printing size is changed. Text stored as outline fonts in bitmap image formats is handled in the same way. Other formats, such as PDF, are primarily vector formats which can contain images, potentially at a mixture of resolutions. In these formats the target PPI of the is adjusted to match when the target print size of the file is changed. This is the converse of how it works in a primarily bitmap format like Photoshop, but has exactly the same result of maintaining the relationship between the vector and bitmap portions of the data.

9.2.2 Computer monitor DPI standards

Since the 1980s, the Microsoft Windows has set the default display “DPI” to 96 PPI, while Apple/Macintosh computers have used a default of 72 PPI.[2] These default specifications arose out of the problems rendering standard fonts in the early display systems of the 1980s, including the IBM-based CGA, EGA, VGA and 8514 displays as well as the Macintosh displays featured in the 128K computer and its successors. The choice of 72 PPI by Macintosh 9.2. DOTS PER INCH 79

A 10 × 10-pixel image on a computer display usually requires many more than 10 × 10 printer dots to accurately reproduce, due to limitations of available ink colors in the printer; here, a 60x60 grid is used, providing 36x the original density, compensating for the printer’s lower color depth. The whole blue pixels making up the sphere are reproduced by the printer using different overlaid combinations of cyan, magenta, and black ink, and the light aqua by cyan and yellow with some “white” (ink-free) print pixels within the actual image pixel. When viewed at a more normal distance, the primary colored stippled dots appear to merge into a smoother, more richly colored image. for their displays arose from the convenient fact that the official 72 points per inch mirrored the 72 pixels per inch that appeared on their display screens. (Points are a physical unit of measure in typography, dating from the days of printing presses, where 1 point by the modern definition is 1/72 of the international inch (25.4 mm), which therefore makes 1 point approximately 0.0139 in or 352.8 µm). Thus, the 72 pixels per inch seen on the display had exactly the same physical dimensions as the 72 points per inch later seen on a printout, with 1 pt in printed text equal to 1 px on the display screen. As it is, the Macintosh 128K featured a screen measuring 512 pixels in width by 342 pixels in height, and this corresponded to the width of standard office paper (512 px ÷ 72 px/in ≈ 7.1 in, with a 0.7 in margin down each side when assuming 8.5 in × 11 in North American paper size (in Europe, it’s 21cm x 30cm - called “A4”. B5 is 176 millimeters x 250 millimeters)). A consequence of Apple’s decision was that the widely used 10-point fonts from the typewriter era had to be allotted 80 CHAPTER 9. DAY 9

10 display pixels in em height, and 5 display pixels in x-height. This is technically described as 10 pixels per em (PPEm). This made 10-point fonts be rendered crudely and made them difficult to read on the display screen, particularly the lowercase characters. Furthermore, there was the consideration that computer screens are typically viewed (at a desk) at a distance 1/3 or 33% greater than printed materials, causing a mismatch between the perceived sizes seen on the computer screen and those on the printouts. Microsoft tried to solve both problems with a hack that has had long-term consequences for the understanding of what DPI and PPI mean.[3] Microsoft began writing its software to treat the screen as though it provided a PPI 4 characteristic that is 3 of what the screen actually displayed. Because most screens at the time provided around 72 ∗ 1 PPI, Microsoft essentially wrote its software to assume that every screen provides 96 PPI (because 72 (1+ 3 ) = 96 ). The short-term gain of this trickery was twofold:

• 1 It would seem to the software that 3 more pixels were available for rendering an image, thereby allowing for bitmap fonts to be created with greater detail.

• On every screen that actually provided 72 PPI, each graphical element (such as a character of text) would be 1 rendered at a size 3 larger than it “should” be, thereby allowing a person to sit a comfortable distance from the screen. However, larger graphical elements meant less screen space was available for programs to draw; indeed, although the default 720-pixel wide mode of a Hercules mono graphics adaptor (the one-time gold standard for high resolution PC graphics) – or a “tweaked” VGA adaptor – provided an apparent 7.5-inch page width at this resolution, the more common and color-capable display adaptors of the time all provided a 640-pixel wide image in their high resolution modes, enough for a bare 6.67 inches at 100% zoom (and barely any greater visible page height – a maximum of 5 inches, versus 4.75). Consequently, the default margins in Microsoft Word were set, and still remain at 1 full inch on all sides of the page, keeping the “text width” for standard size printer paper within visible limits; despite most computer monitors now being both larger and finer-pitched, and printer paper transports having become more sophisticated, the Mac-standard half-inch remain listed in Word 2010’s page layout presets as the “narrow” option (versus the 1-inch default).

• Without using supplemental, software-provided zoom levels, the 1:1 relationship between display and print size was (deliberately) lost; the availability of different-sized, user-adjustable monitors and display adaptors with varying output resolutions exacerbated this, as it was not possible to rely on a properly-adjusted “standard” monitor and adaptor having a known PPI. For example, a 12” Hercules monitor and adaptor with a thick bezel and a little underscan may offer 90 “physical” PPI, with the displayed image appearing nearly identical to hardcopy (assuming the H-scan density was properly adjusted to give square pixels) but a thin-bezel 14” VGA monitor adjusted to give a borderless display may be closer to 60, with the same bitmap image thus appearing 50% larger; yet, someone with an 8514 (“XGA”) adaptor and the same monitor could achieve 100 DPI using its 1024-pixel wide mode and adjusting the image to be underscanned. A user who wanted to directly compare on- screen elements against those on an existing printed page by holding it up against the monitor would therefore first need to determine the correct zoom level to use, largely by trial and error, and often not be able to obtain an exact match in programs that only allowed integer percent settings, or even fixed pre-programmed zoom levels. For the examples above, they may need to use respectively 94% (precisely, 93.75) – or 95/90, 63% (62.5) – or 60/66.7; and 104% (104.167) – or 105, with the more commonly accessible 110% actually being a less precise match.

Thus, for example, a 10-point font on a Macintosh (at 72 PPI) was represented with 10 pixels (i.e., 10 PPEm), whereas a 10-point font on a Windows platform (at 96 PPI) at the same zoom level is represented with 13 pixels (i.e., Microsoft rounded 13.3333 to 13 pixels, or 13 PPEm) – and, on a typical consumer grade monitor, would have physically appeared around 15/72 to 16/72 of an inch high instead of 10/72. Likewise, a 12-point font was represented with 12 pixels on a Macintosh, and 16 pixels (or a physical display height of maybe 19/72 of an inch) on a Windows platform at the same zoom, and so on.[4] The negative consequence of this standard is that with 96 PPI displays, there is no longer a 1-to-1 relationship between the font size in pixels and the printout size in points. This difference is accentuated on more recent displays that feature higher pixel densities. This has been less of a problem with the advent of vector graphics and fonts being used in place of bitmap graphics and fonts. Moreover, many Windows software programs have been written since the 1980s which assume that the screen provides 96 PPI. Accordingly, these programs do not display properly at common alternative resolutions such as 72 PPI or 120 PPI. The solution has been to introduce two concepts:[3]

• logical PPI: The PPI that software claims a screen provides. This can be thought of as the PPI provided by a virtual screen created by the operating system. 9.2. DOTS PER INCH 81

• physical PPI: The PPI that a physical screen actually provides.

Software programs render images to the virtual screen and then the operating system renders the virtual screen onto the physical screen. With a logical PPI of 96 PPI, older programs can still run properly regardless of the actual physical PPI of the display screen, although they may exhibit some visual distortion thanks to the effective 133.3% pixel zoom level (requiring either that every third pixel be doubled in width/height, or heavy-handed smoothing be employed).

How Microsoft Windows handles DPI scaling

Displays with high pixel densities were not common up to the Windows XP era. High DPI displays became main- stream around the time was released. Display scaling by entering a custom DPI irrespective of the display resolution is a feature of Microsoft Windows since Windows 95. [5] Windows XP introduced the GDI+ library which allows resolution-independent text scaling. [6] introduced support for programs to declare themselves to the OS that they are high-DPI aware via a manifest file or using an API.[7][8] For programs that do not declare themselves as DPI-aware, Windows Vista supports a compatibility feature called DPI virtualization so system metrics and UI elements are presented to applications as if they are running at 96 DPI and the Desktop Window Manager then scales the resulting application window to match the DPI setting. Windows Vista retains the Windows XP style scaling option which when enabled turns off DPI virtualization for all applications globally. DPI virtualization is a compatibility option as application developers are all expected to update their apps to support high DPI without relying on DPI virtualization. Windows Vista also introduces Windows Presentation Foundation. WPF .NET applications are vector-based, not pixel-based and are designed to be resolution-independent. Developers using the old GDI API and Windows Forms on .NET Framework runtime need to update their apps to be DPI aware and flag their applications as DPI-aware. adds the ability to change the DPI by doing only a log off, not a full reboot and makes it a per-user setting. Additionally, Windows 7 reads the monitor DPI from the EDID and automatically sets the system DPI value to match the monitor’s physical pixel density, unless the effective resolution is less than 1024 x 768. In Windows 8, only the DPI scaling percentage is shown in the DPI changing dialog and the display of the raw DPI value has been removed.[9] In Windows 8.1, the global setting to disable DPI virtualization (only use XP-style scaling) is removed and a per-app setting added for the user to disable DPI virtualization from the Compatibility tab.[9] When the DPI scaling setting is set to be higher than 120 PPI (125%), DPI virtualization is enabled for all applications unless the application opts out of it by specifying a DPI aware flag (manifest) as “true” inside the EXE. Windows 8.1 retains a per-application option to disable DPI virtualization of an app.[9] Windows 8.1 also adds the ability for different displays to use independent DPI scaling factors, although it calculates this automatically for each display and turns on DPI virtualization for all monitors at any scaling level. adds manual control over DPI scaling for individual monitors.

9.2.3 Proposed metrication

There are some ongoing efforts to abandon the DPI Image resolution unit in favor of a metric unit, giving the inter-dot spacing in dots per centimeter ( px/cm or dpcm), as used in CSS3 media queries[10] or micrometres (µm) between dots.[11] A resolution of 72 DPI, for example, equals a resolution of about 28 dpcm or an inter-dot spacing of about 350 µm. In BMP images 2835 pixels per meter correspond to 72 DPI (rounded from 2834.6472).[12]

9.2.4 See also

• Pixel density

• Samples per inch – a related concept for image scanners

• Lines per inch

• Metric typographic units

• Display resolution 82 CHAPTER 9. DAY 9

• Mouse DPI

• Twip

9.2.5 References

[1] Ask OKI—"Inkjet Printers”

[2] Hitchcock, Greg (2005-10-08). “Where does 96 DPI come from in Windows?". Microsoft Developer Network . Microsoft. Retrieved 2009-11-07.

[3] Hitchcock, Greg (2005-09-08). “Where does 96 DPI come from in Windows?". .msdn.com. Retrieved 2010-05-09.

[4] Connare, Vincent (1998-04-06). “Microsoft Typography – Making TrueType bitmap fonts”. Microsoft. Retrieved 2009- 11-07.

[5] Where does 96 DPI come from in Windows?

[6] Why text appears different when drawn with GDIPlus versus GDI

[7] “Win32 SetProcessDPIAware Function”.

[8] “Windows Vista DPI Settings”.

[9] High DPI Settings in Windows

[10] “Media Queries”.

[11] “Class ResolutionSyntax”. Sun Microsystems. Retrieved 2007-10-12.

[12] Kateryna Yuri. “Convert dot/meter [dot/m] <—> dot/inch [dpi]". TranslatorsCafé.com. Retrieved 2015-01-27.

9.2.6 External links

• All About Digital Photos – The Myth of DPI • Monitor DPI detector

• Tool for changing the DPI of existing images Chapter 10

Day 10

10.1 Imaging technology

Imaging technology is the application of materials and methods to create, preserve, or duplicate images.

10.1.1 Examples

Imaging technology materials and methods include:

• Computer graphics • Virtual camera system used in computer and video games and virtual cinematography • Microfilm and Micrographics • Visual arts • Etching • Drawing and Technical drawing • Film • Painting • Photography • Multiple-camera setup enables and stereophotogrammetry • Light-field camera (basically refocusable photography) • Printmaking • Sculpture • Infrared • Radar imagery • Ultrasound • Multi-spectral image • Electro-optical sensor • Charge-coupled device • Ground-penetrating radar • Electron microscope • Imagery analysis

83 84 CHAPTER 10. DAY 10

• Medical radiography

• Industrial radiography

• LIDAR

• Structured-light 3D scanner

10.1.2 References

10.2 Image viewer

An image viewer or image browser is a computer program that can display stored graphical images; it can often handle various graphics file formats. Such software usually renders the image according to properties of the display such as color depth, display resolution, and color profile. Although one may use a full-featured raster graphics editor (such as Photoshop or the GIMP or the StylePix) as an image viewer, these have many editing functionalities which are not needed for just viewing images, and therefore usually start rather slowly. Also, most viewers have functionalities that editors usually lack, such as stepping through all the images in a directory (possibly as a slideshow). Image viewers give maximal flexibility to the user by providing a direct view of the directory structure available on a hard disk. Most image viewers do not provide any kind of automatic organization of pictures and therefore the burden remains on the user to create and maintain their folder structure (using tag- or folder-based methods). However, some image viewers also have features for organizing images, especially an image database, and hence can also be used as image organizers. Some image viewers, such as Windows Photo Viewer that comes with Windows operating systems, change a JPEG image if it is rotated, resulting in loss of image quality; others offer lossless rotation.

10.2.1 Features

Typical features of image viewers are:

• basic viewing operations such as zooming and rotation

• fullscreen display

• slideshow

• thumbnail display

• printing

• screen capture

• photo editor(if installed)

Advanced features are:

• decode next image in advance and keep previous decoded image in memory for fast image changes

• display (and edit) metadata such as XMP, IPTC Information Interchange Model and Exif

• batch conversion (image format, image dimensions, etc.) and renaming

• create contact sheets

• create HTML thumbnail pages

• different transition effects for slideshows 10.3. IMAGE ORGANIZER 85

10.2.2 Common image viewers

Windows

• Windows Explorer - file manager with basic built-in functionality for image viewing • Windows Picture and Fax Viewer in Windows XP • ACDSee, FastPictureViewer, FastStone Image Viewer, Imagine, IrfanView, Media Pro 1, pViewer, XnView

UNIX-like

• Eye of GNOME, feh, , Gwenview, GThumb, KuickShow, xloadimage, XnView, xv, fbida, fim

Mac

• Preview (Mac OS X)

Web-based

• Imajize HTML5-based 360° Viewer • AJAX-ZOOM Gallery AJAX-ZOOM Gallery • NeptuneLabs FSI Viewer • WebRotate 360 WebRotate 360 Product Viewer • Magic Toolbox’s Magic 360 Spin Viewer • Magic Toolbox’s Magic Zoom Plus Javascript Image Zoom

10.2.3 See also

• Comparison of image viewers • Comparison of CAD, CAM and CAE file viewers • • Image organizer • Binary file#Viewing binary files • Electronic document • Media player • Text editor • Web browser

10.3 Image organizer

An image organizer or image management application is focused on organising digital im- ages.[1][2] Image organizers represent one kind of desktop organizer software applications. Image organizer software is primarily focused on improving the user’s workflow by facilitating the handling of large numbers of images. In contrast to an image viewer, an image organizer has at least the additional ability to edit the image tags and often also an easy way to upload files to on-line hosting pages. Some programs that come with desktop environments such as gThumb (GNOME) and digiKam (KDE) were orig- inally programmed to be simple image viewers, and have since gained features to be used as image organizer as well. 86 CHAPTER 10. DAY 10

10.3.1 Common image organizers features

• Multiple thumbnail previews are viewable on a single screen and printable on a single page. (Contact Sheet)

• Images can be organized into albums

• Albums can be organized into collections

• Adding tags (also known as keywords, categories, labels or flags). Tags can be stored externally, or in industry- standard IPTC or XMP headers inside each image file or in sidecar files.[3]

• Resizing, exporting, e-mailing and printing.

10.3.2 Not so common, or differentiating features

• Pictures can be organized by one or more mechanisms

• Images can be organized into folders, which may correspond to file-system folders. • Images may be organized into albums, which may be distinct from folders or file-system folders. • Albums may be organized into collections, which may not be the same as a folder hierarchy. • Grouping or sorting by date, location, and special photographic metadata such as exposure or f-stops if that information is available. See Exif for example. • Images can appear in more than one album • Albums can appear in more than one collection • Grouped or stacking of images within an album, by date, time, and linking copies to originals. • Adding and editing titles and captions

• Simple or sophisticated search engines to find photos

• Searching by keywords, caption text, metadata, dates, location or title • Searching with logical operators and fields, such as "(Title contains birthday) and (keywords contain cake) not (date before 2007)"

• Separate backing up and exporting of metadata associated with photos.

• Retouching of images (either destructively or non-destructively)

• Editing images in third-party graphical software and then re-incorporating them into the album automatically

• Stitching to knit together panoramic or tiled photos

• Grouping of images to form a slideshow view

• Exporting of slideshows as HTML or flash presentations for web deployment

• Synchronizing of albums with web-based counterparts, either third-party (such as Flickr), or application spe- cific (such as Lightroom, Phase One Media Pro, Photo Supreme and Picasa)

• Retention of Exif, IPTC and XMP metadata already embedded in the image file itself

10.3.3 Two categories of image organizers

• Automatic image organizers. These are software packages that read data present in digital pictures and use this data to automatically create an organization structure. Each digital picture contains information about the date when the picture was taken. It is this piece of information that serves as the basis for automatic picture organization. The user usually has little or no control over the automatically created organization structure. Some tools create this structure on the hard drive (physical structure), while other tools create a virtual structure (it exists only within the tool). 10.3. IMAGE ORGANIZER 87

• Manual image organizers. This kind of software provides a direct view of the folders present on a user’s hard disk. Sometimes referred to as image viewers, they allow the user only to see the pictures but do not provide any automatic organization features. They give maximum flexibility to a user and show exactly what the user has created on his hard drive. While they provide maximum flexibility, manual organizers rely on the user to have his/her own method to organize their pictures. Currently there are two main methods for organizing pictures manually: tag and folder based methods. While not mutually exclusive, these methods are different in their methodology, outcome and purpose. Presently, many commercial image organizers offer both automatic and manual image organization features. A comparison of image viewers reveals that many packages are available that offer most of the organization features available in commercial software.

10.3.4 Future of image organization

There are several imminent advances anticipated in the image organization domain which may soon allow widespread automatic assignment of keywords or image clustering based on image content:[4]

• colour, shape and texture recognition[5] (For example, Picasa experimentally allows searching for photos with primary colour names)

• subject recognition[6]

• fully or semi-automated , torso or body recognition[7][8] (For example, FXPAL in Palo Alto experimen- tally extracts faces from images and measures the distance between each face and a template.)

• geo-temporal sorting and event clustering.[9] Many software will sort by time or place; experimental software has been used to predict special events such as birthdays based on geo-temporal clustering.

In general, these methods either:

• automatically assign keywords based on content, or

• measure the distance between an unclassified image and some template image which is associated with a key- word, and then propose that the operator apply the same keyword(s) to the unclassified images

10.3.5 Notable image organizers

10.3.6 See also

• Image viewers

• Image retrieval

• Digital asset management

• Comparison of image viewers

• Desktop organizer

• Personal wiki

10.3.7 References

[1] Cynthia Baron and Daniel Peck, The Little Digital Camera Book, July 1, 2002 pp:93

[2] Julie Adair King, Shoot Like a Pro! Digital Photography July 28, 2003 pp:21-23

[3] “Who’s got the tag? Database truth versus file truth” by Jon Udell 2007

[4] http://www.oreillynet.com/digitalmedia/blog/2007/03/lightroom_and_the_future_of_or.html Lightroom and the future of organizing photos 88 CHAPTER 10. DAY 10

[5] http://www.ctr.columbia.edu/~{}jrsmith/html/pubs/PAMI/pami_final_1.html Automated Image Retrieval Using Color and Texture (1995)

[6] http://portal.acm.org/citation.cfm?id=1232330.1232374&coll=GUIDE&dl=GUIDE Content-based object organization for efficient image retrieval in image databases (2006)

[7] http://hcil.cs.umd.edu/trs/2004-15/2004-15.pdf Semi-Automatic Image Annotation Using Event and Torso Identification

[8] http://www.ercim.org/publication/Ercim_News/enw62/wilcox.html Managing Digital Photo Collections

[9] http://portal.acm.org/citation.cfm?id=957093 Temporal event clustering for digital photo collections

10.3.8 Additional reading

• Information Retrieval and Management: Technological Fundamentals and Applications by David Feng, W.C. Siu, Hong J. Zhang

• Multimedia Networking: Technology, Management, and Applications by Syed Mahbubur Rahman

• Multimedia and Image Management by Susan Lake, Karen Bean

10.3.9 External links

• Comparison programs of organise images

10.4 Image retrieval

An image retrieval system is a computer system for browsing, searching and retrieving images from a large database of digital images. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning', keywords, or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive; to address this, there has been a large amount of research done on automatic image annotation. Additionally, the increase in social web applications and the semantic web have inspired the development of several web-based image annotation tools. The first microcomputer-based image database retrieval system was developed at MIT, in the 1990s, by Banireddy Prasaad, Amar Gupta, Hoo-min Toong, and Stuart Madnick.[1] A 2008 survey article documented progresses after 2007.[2]

10.4.1 Search methods

Image search is a specialized data search used to find images. To search for images, a user may provide query terms such as keyword, image file/link, or click on some image, and the system will return images “similar” to the query. The similarity used for search criteria could be meta tags, color distribution in images, region/shape attributes, etc.

• Image meta search - search of images based on associated metadata such as keywords, text, etc.

• Content-based image retrieval (CBIR) – the application of computer vision to the image retrieval. CBIR aims at avoiding the use of textual descriptions and instead retrieves images based on similarities in their contents (textures, colors, shapes etc.) to a user-supplied query image or user-specified image features.

• List of CBIR Engines - list of engines which search for images based image visual content such as color, texture, shape/object, etc.

Further information: Visual search engine and Reverse image search

• Image collection exploration - search of images based on the use of exploration paradigms.[3] 10.4. IMAGE RETRIEVAL 89

10.4.2 Data Scope

It is crucial to understand the scope and nature of image data in order to determine the complexity of image search system design. The design is also largely influenced by factors such as the diversity of user-base and expected user traffic for a search system. Along this dimension, search data can be classified into the following categories:

• Archives - usually contain large volumes of structured or semi-structured homogeneous data pertaining to specific topics. • Domain-Specific Collection - this is a homogeneous collection providing access to controlled users with very specific objectives. Examples of such a collection are biomedical and satellite image databases. • Enterprise Collection - a heterogeneous collection of images that is accessible to users within an organization’s intranet. Pictures may be stored in many different locations. • Personal Collection - usually consists of a largely homogeneous collection and is generally small in size, acces- sible primarily to its owner, and usually stored on a local storage media. • Web - World Wide Web images are accessible to everyone with an Internet connection. These image collections are semi-structured, non-homogeneous and massive in volume, and are usually stored in large disk arrays.

10.4.3 Evaluations

There are evaluation workshops for image retrieval systems aiming to investigate and improve the performance of such systems.

• ImageCLEF - a continuing track of the Cross Language Evaluation Forum that evaluates systems using both textual and pure-image retrieval methods. • Content-based Access of Image and Video Libraries - a series of IEEE workshops from 1998 to 2001.

10.4.4 See also

• Booru • Computer vision • Concept-based image indexing • Content-based image retrieval (CBIR) • Digital asset management • Digital image editing • Image organizer • Image processing • Information retrieval • Multimedia information retrieval • VisualRank

10.4.5 References

[1] B E Prasad; A Gupta; H-M Toong; S.E. Madnick (February 1987). “A microcomputer-based image database management system”. IEEE Transactions on Industrial Electronics. IE-34 (1): 83–8. doi:10.1109/TIE.1987.350929. [2] Datta, Ritendra; Dhiraj Joshi; Jia Li; James Z. Wang (April 2008). “Image Retrieval: Ideas, Influences, and Trends of the New Age”. ACM Computing Surveys. 40 (2): 1–60. doi:10.1145/1348246.1348248. [3] Camargo, Jorge E.; Caicedo, Juan C.; Gonzalez, Fabio A. “A kernel-based framework for image collection exploration”. Journal of Visual Languages & Computing. 24 (1): 53–57. doi:10.1016/j.jvlc.2012.10.008. 90 CHAPTER 10. DAY 10

10.4.6 External links

• Image-Net.org Chapter 11

Text and image sources, contributors, and licenses

11.1 Text

• Visual arts Source: https://en.wikipedia.org/wiki/Visual_arts?oldid=763648059 Contributors: Mav, Robert Merkel, Koyaanis Qatsi, Andre Engels, Deb, Karen Johnson, SimonP, Merphant, Caltrop, Danielcboyer, Daniel C. Boyer, Camembert, Ichimunki, Montrealais, Quintessent, Michael Hardy, Minesweeper, Pagingmrherman, Haakon, Docu, Jiang, Grin, Alex756, Raven in Orbit, Pema~enwiki, Alex S, Ike9898, JCarriker, Aion, Maximus Rex, Hyacinth, Sushimatsuda, Spinster, Wetman, Robbot, AlainV, Mayooranathan, Gidonb, Bri- anshapiro, Borislav, Michael Snow, Kenny sh, Alison, Varlaam, Mboverload, Solipsist, Gerd Richter, Knutux, Quadell, MattDal, Mike Rosoft, Ham II, Rich Farmbrough, Guanabot, Ahkond, Dbachmann, STLEric, Walden, Gorn~enwiki, Viriditas, Maurreen, Physicist- jedi, Nsaa, Mdd, Ranveig, Jumbuck, BanyanTree, Clubmarx, Nex O-Slash, RainbowOfLight, Brunberg, Scriberius, Robert K S, Ste- fanomione, Mandarax, DavidParfitt, Graham87, Sparkit, WBardwin, BD2412, Shadowhillway, Tarnas, Adjusting, Allen Moore, FlaBot, RexNL, Srleffler, Chobot, Visor, PKM, YurikBot, Wavelength, RobotE, Bhny, Stephenb, Jenblower, Zwobot, BOT-Superzerocool, Tomisti, Robertbyrne, User27091, GraemeL, Tyrenius, That Guy, From That Show!, Whyaduck, SmackBot, Bobet, Mister X, Sar- casticDwarf, HalfShadow, Ohnoitsjamie, Rlevse, Thx2005, Cobain, Grhabyt, Artistpres, Radagast83, JGS, Dogears, SingCal, Bjanku- loski06en~enwiki, Filippowiki, The Man in Question, Johnmc, TastyPoutine, Dl2000, JeffW, OnBeyondZebrax, Wizard191, Iridescent, Linkspamremover, MarylandArtLover, Cryptic C62, Lile, GangstaEB, Omicronpersei8, TAG.Odessa, Trev M, PKT, Thijs!bot, Barti- cus88, Cosmopolitancats, Edal, P.gibellini, Jack Bethune, RoboServien, Escarbot, AntiVandalBot, Ericpfund, Modernist, Alphachimp- bot, JAnDbot, MER-C, The Transhumanist, Grégory Leclair, Xeno, Freshacconci, Mikepanhu, Mrs Scarborough, Father Goose, Swpb, Odonata, Teapotgeorge, Edward321, Oicumayberight, WriterArtistDC, S3000, PhantomS, Jim.henderson, Bus stop, CommonsDelinker, Cyrus Andiron, Penguinwithin, Johnbod, Apostle12, Ipigott, The Transhumanist (AWB), Juliancolton, Black Shoop, Treisijs, Bonadea, Idioma-bot, Funandtrvl, Gwazda, Jeff G., Philip Trueman, Argusmom, TXiKiBoT, Oshwah, Berthold Werner, Broadbot, BwDraco, Ar- taxerex, Synthebot, Freiwilliger, AlleborgoBot, Symane, D. Recorder, Artincontext, SieBot, Show007, Jdaloner, OKBot, Laurghita, Prof saxx, Gantuya eng, Thais1, Simenzo, ImageRemovalBot, ClueBot, Nb99, LAgurl, Niceguyedc, LizardJr8, I8munkies, Excirial, Residue- OfDesign, 123pecca, Arjayay, Cowboy456, Melaniesharrison, Versus22, CurtisNeeley, Apparition11, XLinkBot, WikHead, Necz0r, Addbot, Lithoderm, Fgnievinski, Reidlophile, Ronhjones, Fluffernutter, Sara USA, LaaknorBot, CarsracBot, ChenzwBot, Tassedethe, Tide rolls, Luckas-bot, Yobot, Taxisfolder, THEN WHO WAS PHONE?, AnakngAraw, Evaders99, Eric-Wester, Noq, Philip1966, Ru- binbot, Kingpin13, Materialscientist, Citation bot, JBirken, Dingbusan, Birchcliff, Xqbot, Jyusin, Research Method, HN45, 4twenty42o, J04n, Armbrust, Omnipaedista, Reveller, Velblod, FrescoBot, Avoris, Dana smk, Cannolis, Mantanera, Gnostril, Jean-François Clet, Skol fir, RedBot, MastiBot, Enriquevi16, Jauhienij, FoxBot, Notpietru, Dinamik-bot, DrGML, DARTH SIDIOUS 2, EmausBot, Wikitanvir- Bot, Look2See1, Dewritech, Faolin42, Dasari srinath, K6ka, HiW-Bot, Allforrous, Aeonx, Averaver, Erianna, Sahimrobot, L Kensington, Donner60, ChuispastonBot, Ghgugiff, Xanchester, ClueBot NG, This lousy T-shirt, Millionwampumbaby, Delusion23, Artsconnections, Markataxis, Widr, Gob Lofa, Zisette, BG19bot, Vagobot, Mark Arsten, Casa5tavira, Crh23, Heatherawalls, Bradbushfield, WhiteNebula, Pratyya Ghosh, Goldesign, Vibhabamba, ZappaOMati, Rinkle gorge, Gomez09023, Isarra (HG), Msjgp06, CsDix, Backendgaming, Plan- etKev, Duckduckstop, Ugog Nizdast, Vinny Lam, Dctrzl, Vivekt9, Hejkwkeek, SlavaBest, Bama Dissy, Ranmarublazit420, Trackteur, ProfessionalArt1979, KH-1, Crystallizedcarbon, Spylass, Elyciaa, TaqPol, D'PRINCE (Omoba), Hanirich, Bayu Angora, King muh, SolutionsIndata, InternetArchiveBot, Yasith Silva, Llcoolj89, GreenC bot, Kgroot, Wardiverde, Epsfamily, Philhistory08, ShanePEC, Justeditingtoday and : 293 • Image Source: https://en.wikipedia.org/wiki/Image?oldid=761619544 Contributors: AxelBoldt, Magnus Manske, Derek Ross, Toby Bartels, William Avery, SimonP, GrahamN, Stevertigo, DennisDaniels, Patrick, JohnOwens, Michael Hardy, Wshun, Grape~enwiki, Wapcaplet, Ellywa, Ahoerstemeier, Haakon, Ronz, Kwekubo, Smack, Conti, Alex S, Dysprosia, Haukurth, Phys, Wernher, Bevo, Wet- man, Jeffq, Skeetch, Robbot, Altenmann, Phatsphere, Academic Challenger, Caknuck, Hadal, Mattflaschen, Carnildo, Chronomantic, DocWatson42, Fennec, Mintleaf~enwiki, Everyking, Perl, Michael Devore, Yekrats, Jorge Stolfi, Kainaw, Wikiwiki~enwiki, Andycjp, Antandrus, Jossi, Elroch, Pgreenfinch, Gscshoyru, Anirvan, Demiurge, Mike Rosoft, Discospinster, Bumphoney, Vsmith, ArnoldRein- hold, Dave souza, Kndiaye, Pavel Vozenilek, Harriv, MarkS, BenjBot, *drew, Shanes, Whosyourjudas, Reinyday, Adrian~enwiki, Gi- raffedata, La goutte de pluie, Mdd, Grutness, Alansohn, SnowFire, Riana, PAR, Olaf Simons, Malo, Snowolf, Evil Monkey, SmthManly, Stephen, Woohookitty, Mindmatrix, Sburke, Pol098, Before My Ken, WadeSimMiser, JeremyA, Thruston, Isnow, Haunti, Braindusted, King of Hearts (old account 2), Magister Mathematicae, DavidCane, FreplySpang, Jshadias, Jdcooper, Sjö, Wikibofh, CQJ, ScottJ, MarnetteD, Aapo Laitinen, Yamamoto Ichiro, Norvy, Gurch, Srleffler, Sairen42, DVdm, Adoniscik, The Rambling Man, YurikBot, TexasAndroid, Sundaramsahnee, WAvegetarian, Anonymous editor, Hede2000, Tenebrae, Cryptic, Wimt, NawlinWiki, Wiki alf, Cryp- toid, LordMooCow, Grafikm fr, Wknight94, Igiffin, Deville, Chase me ladies, I'm the Cavalry, Arthur Rubin, KGasso, OEMCUST,

91 92 CHAPTER 11. TEXT AND IMAGE SOURCES, CONTRIBUTORS, AND LICENSES

GraemeL, Tyrenius, Anclation~enwiki, NeilN, Paul Erik, Samuel Blanning, DVD R W, Luk, A bit iffy, SmackBot, Prodego, McGeddon, Bigbluefish, Eskimbot, Edgar181, Zephyris, Gilliam, Jprg1966, SchfiftyThree, Royboycrashfan, Can't sleep, clown will eat me, Yidish- eryid, TheKMan, Francisx, Cybercobra, Jiddisch~enwiki, James.kendall, RolandR, Shadow1, Mikebrands, Astroview120mm, BryanG, DMacks, WoodyWerm, Salamurai, Pilotguy, G-Bot~enwiki, Howdoesthiswo, Sunroof, Pile3d, J 1982, Lazylaces, Accurizer, Mattiuscn, IronGargoyle, Munita Prasad, Rybioko~enwiki, Feureau, Dicklyon, Hera1187, Ryulong, Levineps, Hetar, BranStark, Iridescent, Aaron DT, Tawkerbot2, Disambiguator, Aitsukai, JohnCD, Umerfaroo q, Chanman, Rachdi, Werratal, Richard Keatinge, TJDay, Jane023, Doomed Rasher, Gogo Dodo, Bardak, Thijs!bot, Epbr123, Btaysisson, Kablammo, N5iln, Vertium, E. Ripley, JuWiki, Escarbot, I rox @ 13, Dainis, AntiVandalBot, Luna Santin, Guy Macon, Modernist, Wayiran, Storkk, JAnDbot, D99figge, Leuko, MER-C, Plantsurfer, Db099221, Roleplayer, Hut 8.5, Kerotan, LittleOldMe, Ariaconditzione, Freshacconci, Bongwarrior, VoABot II, Wikidudeman, Ka- jasudhakarababu, Roxyluver, Skew-t, Indon, Robotman1974, Styrofoam1994, Lelkesa, Oicumayberight, Stephenchou0722, MartinBot, STBot, Arjun01, Rettetast, Anaxial, TheEgyptian, KTo288, Nono64, Pekaje, Deathgecko, J.delanoy, Quailman, Anas Salloum, Uncle , Maurice Carbonaro, Jseamen, UdovdM, Bbb00723, McSly, Jeepday, Bhteam, AntiSpamBot, NewEnglandYankee, SJP, Shoessss, $dollarz$, Cometstyles, Treisijs, Huzefahamid, Bonadea, S (usurped also), Patlittle, Idioma-bot, Pgarwood, Mastrchf91, Anamc60, Jrugordon, Cdimage, Polysillycon, Thisisborin9, Jeff G., TheMindsEye, Soliloquial, Sabarish knms, WarddrBOT, Reinhardheydt, PG- SONIC, Oshwah, Nickcasey10, Vipinhari, Pojanji, GDonato, Gavroche42, Ann Stouter, GcSwRhIc, Charlesdrakew, Qxz, The New iPod., Shulian, Lradrama, Martin451, Leafyplant, Amog, Gotox, Leonardmrija82, Billinghurst, Meters, Altermike, Curtis~enwiki, Guess- whosback121, Gary134, HiDrNick, Deconstructhis, Daediter12345, Arachnidkid123, Dusti, Calliopejen1, Nihil novi, Weeliljimmy, Nestea Zen, Caltas, RJaguar3, MarcellRowe, Flyer22 Reborn, Radon210, The Evil Spartan, JetLover, Daniil Maslyuk, Bob98133, The V.A.N.D.A.L. Man, Faradayplank, HaboFreakNumber2, BillyMInScience, Hobartimus, Tao of tyler, Songsmail, Maralia, Denisarona, Indianofficebuildings, Someoneddd, Martarius, ClueBot, Foxj, The Thing That Should Not Be, Kleptosquirrel, FieldMarine, Erudecorp, Blanchardb, Billbouchard, Excirial, Kjramesh, Thunderhippo, Ykhwong, NuclearWarfare, LoggedInWiki, PEPSI2K7, LobStoR, Aitias, Mattbl34, SoxBot III, NERIC-Security, Against the current, XLinkBot, Spitfire, M0lliEEM, Qwerqewrqew, Dthomsen8, Arslion, Gaped, Mifter, Alexius08, Noctibus, Addbot, Poco a poco, Tomg-unit, Lithoderm, SunDragon34, PatrickFlaherty, Ronhjones, TutterMouse, Ezekiel 7:19, Singboyb16, Mr. Wheely Guy, Fluffernutter, MrOllie, Ccacsmss, Chzz, LinkFA-Bot, Jasper Deng, West.andrew.g, 5 al- bert square, Nordanjordan, Tide rolls, OlEnglish, WikiDreamer Bot, BluesD, Tom scaret1284564, Punckitty, Yobot, Worldbruce, Gyro Copter, Mmxx, THEN WHO WAS PHONE?, Drake the dragon, Happyhug1, AnomieBOT, Jim1138, IRP, Piano non troppo, Adjust- Shift, Ufim, Kingpin13, Conobot13, Materialscientist, Maxis ftw, Capsal, GB fan, LilHelpa, Aek3755, Trampledtown, Nathaliecat- cats, Cureden, Research Method, Yosufimas229, NFD9001, Lycurgus1920, J04n, DaleDe, IShadowed, Schekinov Alexey Victorovich, A.amitkumar, Captain Weirdo the Great, Purplecows123, Mario12184, Jicard, Linjr32, Valerian456, DivineAlpha, MusicCyborg23, Theonenow, Gioj50, Pinethicket, ICGC software technologies, Baboshed, Lars Washington, Baloonga, Niri.M, Lotje, Thudrs1, Zvn, An- tipastor, Fastilysock, Brian the Editor, Mean as custard, Bubblesroo, Ryanrocker, Gio717, Cheyennedoesntpickernose, K6ka, Ὁ οἶστρος, Wayne Slam, Tolly4bolly, Sniper777~enwiki, Ur74, GeorgeBarnick, Donner60, EddyDaDestroyer1995, ClueBot NG, Pallap, Alexhat, Satellizer, Gabejs1999, Cntras, Dashingfire99, Nicoleontour, Spannerjam, Prettygirl4mj, Sevbanyeni, Theopolisme, Titodutta, Ilthepil, Paperpoop, DaaajM, MusikAnimal, Mark Arsten, Topboy 111, Fylbecatulous, Emeraldodge, Soregre, Bakermanboy199998, Dexbot, Webclient101, Increment0, TwoTwoHello, Tony Mach, Doctorwhoawesome, 11allan.speedy, Emmacurran, Plurofuturo, Tentinator, Ev- erymorning, SamoaBot, ElHef, DavidLeighEllis, Sibekoe, Finnusertop, FDMS4, .js, Binh Duong Friendly, Bballer123456789321, Suelru, 1996chuckles, PrestonBerry, Wailord2, ChamithN, Gladamas, GrimRaymond, CyanoTex, Deez nuts is illuminati, Raviyadav228145, Re- nie sagun, ManlyBoys, CLCStudent, Namate4283, EumlinStumlinStan, Naruto bhagat, Ember1989, Thnnguyen, StephanoCurry55575 and Anonymous: 578 • Digital image Source: https://en.wikipedia.org/wiki/Digital_image?oldid=752625924 Contributors: Patrick, Kku, Delirium, Ahoerste- meier, Andrewman327, Altenmann, Seabhcan, Jmoliver, Zigger, Jonathan O'Donnell, Jorge Stolfi, Chowbok, LiDaobing, Elwell, Dis- cospinster, Minghong, Rick Sidwell, Forderud, Woohookitty, Phillipsacp, MattGiuca, Mandarax, BD2412, Bubba73, Wars, Jfriedl, Adoniscik, AVM, Manop, SmackBot, Eskimbot, Betacommand, Bluebot, Thumperward, MalafayaBot, Xx236, Nbarth, Audriusa, Nei1, GeorgeMoney, Mitrius, Richard001, SashatoBot, Loadmaster, EricR, Emx~enwiki, MIckStephenson, Belginusanl, Bobblehead, VoABot II, Soulbot, Jim.henderson, Speck-Made, R'n'B, Absinthe88, Juliancolton, VolkovBot, Jamelan, Billinghurst, Enyberg, Monty845, Darxus, Dhatfield, Daniel73480, Bob1960evens, Hs4pratt, Ktr101, Gianola, Avoided, Thatguyflint, Addbot, Poco a poco, Fgnievinski, Ethanpet113, Morning277, Luckas-bot, Yobot, Maxí, AnomieBOT, Ciphers, Jlglover, ArthurBot, Earlypsychosis, FrescoBot, Tom.Reding, Calmer Wa- ters, A8UDI, RedBot, Dinamik-bot, LilyKitty, Barry Pearson, Sinuhet, Charlie Podvin, Tommy2010, Mz7, Biel Bestué, ClueBot NG, Helpful Pixie Bot, MusikAnimal, CitationCleanerBot, Toccata quarta, Monkbot, Amir2901, Bender the Bot and Anonymous: 50 • Two-dimensional space Source: https://en.wikipedia.org/wiki/Two-dimensional_space?oldid=736032885 Contributors: Dino, Bevo, Tea2min, Mporter, BD2412, Kri, Wavelength, RDBury, Incnis Mrsi, Melchoir, Kjkjava, Newone, JAnDbot, Magioladitis, Swpb, R'n'B, JohnBlackburne, Dmcq, Mitch Ames, Addbot, Fgnievinski, Luckas-bot, Yobot, AnomieBOT, LilHelpa, Xqbot, GrouchoBot, Frosted14, Gire 3pich2005, Double sharp, 4, EmausBot, Dcirovic, Jpvandijk, Tijfo098, Matthiaspaul, Rezabot, மதனாஹரன், Microextruders, Ilu- vatarBot, JYBot, Aymankamelwiki, Saehry, Brirush, Biogeographist, Suelru, Monmonmon098, Loraof and Anonymous: 15 • Image resolution Source: https://en.wikipedia.org/wiki/Image_resolution?oldid=749903195 Contributors: The Anome, Fnielsen, Si- monP, Kku, CesarB, MilkMiruku, DocWatson42, BenFrantzDale, Ans, Ehusman, Vadmium, MrMambo, Bodnotbod, DmitryKo, Blue- mask, Vsmith, .:Ajvol:., Coma28, Pion, Suruena, Richard Arthur Norton (1958- ), Mindmatrix, Raguks, Graham87, XP1, Arnero, Gurch, Nehalem, YurikBot, Diliff, David Koebel~enwiki, Bhny, Stephenb, Gaius Cornelius, Jenblower, Pseudomonas, David R. Ing- ham, Lemon-s, Grafen, Gergis, Jack Nastyface, Btrujill, Kookykman, Planemad, Myrabella, SmackBot, Reedy, Zyxw, Pasajero, Gilliam, Eric00000007, Chris the speller, Landen99, JDCMAN, Can't sleep, clown will eat me, TKD, Cybercobra, VegaDark, SashatoBot, Kuru, JorisvS, Adamgeek, 16@r, Dicklyon, Hu12, JoeBot, RekishiEJ, Tawkerbot2, JForget, Thermochap, CmdrObot, DShantz, Thijs!bot, Epbr123, West Brom 4ever, Rosuna, Jaxelrod, AntiVandalBot, Seaphoto, Tangerines, MER-C, .anacondabot, Kinston eagle, Little Jimmy, DerHexer, CliffC, Anaxial, ScorpO, UdovdM, Acalamari, Mikael Häggström, Llorenzi, Oxcorp, CWii, Alexandria, DrSlony, Loki~enwiki, Katimawan2005, Lamro, MuzikJunky, Flyer22 Reborn, Theaveng, Cpoynton, Yerpo, Fratrep, Anchor Link Bot, Denis- arona, Martarius, Razimantv, Uncle Milty, Excirial, Kanza.mukhtar, Zwilliam, Msltul, Addbot, Micahmedia, Older and ... well older, Bassbonerocks, Lightbot, Willondon, Megaman en m, Allo002, KBurchfiel, AnomieBOT, The Lamb of God, Piano non troppo, Tv- fan01nine, Dinesh smita, Mahmudmasri, Alchemista2, Sketchmoose, Ohspite, FrescoBot, Wissle, Sae1962, KennanLau, Serols, Edinwiki, VernoWhitney, EmausBot, RA0808, Herophilos, Wayne Slam, TyA, Rocketrod1960, Xanchester, ClueBot NG, Vance&lance, MerlIw- Bot, Helpful Pixie Bot, HMSSolent, BG19bot, MusikAnimal, Atomician, Soerfm, Losine, Wannabemodel, BattyBot, Sadsaque, ~riley, Shahin 3000 v, Skigh79, EagerToddler39, Zeeyanwiki, Allsworthj, Tentinator, Rafaoc, JustBerry, Saadat2930, Boyhominid, Filedelink- erbot, Jayanth5566, Starrysky666 and Anonymous: 222 • Photograph Source: https://en.wikipedia.org/wiki/Photograph?oldid=760388675 Contributors: Bryan Derksen, Andre Engels, Karen 11.1. TEXT 93

Johnson, Merphant, Maury Markowitz, Heron, Olivier, Ericd, Patrick, Bbtommy, Ixfd64, Chmouel, Zanimum, Arpingstone, Michael- Janich, Gaz~enwiki, Vzbs34, Andres, Samw, Peregrine981, Maximus Rex, Bevo, Raul654, Spinster, Pollinator, Jni, Chuunen Baka, Rob- bot, Blades, Icebox, Mayooranathan, Academic Challenger, Hadal, Umlautbob, Marc Venot, Lupin, Everyking, Quinwound, Dmmaus, Quadell, Lesgles, Zfr, P G Henning, Muijz, Squash, Imroy, DanielCD, Discospinster, Rich Farmbrough, Vsmith, Aardark, Hapsiainen, Dkroll2, Aude, Shanes, EurekaLott, Bobo192, Jeffmedkeff, BarkingFish, Nich148 9, Twobells, Sam Korn, Mdd, Alansohn, Guy Har- ris, Sade, Lightdarkness, Mac Davis, Hu, Rwendland, Wtshymanski, Rick Sidwell, Evil Monkey, Sakus, Versageek, Recury, Tournesol, Angr, Richard Arthur Norton (1958- ), OwenX, RHaworth, Camw, Madchester, Sdgjake, Neftin, Nikiforov, BD2412, T0ny, Quiddity, DirkvdM, Titoxd, Old Moonraker, GünniX, SouthernNights, Nivix, SportsMaster, Elmer Clark, Ewlyahoocom, Arctic.gnome, TeaD- rinker, Srleffler, Acefitt, King of Hearts, Chobot, DVdm, Bgwhite, Sceptre, WAvegetarian, Anonymous editor, Zafiroblue05, Stephenb, Manop, CambridgeBayWeather, Aburad, Ugur Basak, Gvbi, Shanel, NawlinWiki, Spike Wilbury, Megapixie, Howcheng, Irishguy, Nebby~enwiki, Nephron, Trollderella, Retarded hamster, Jim jim, Hakeem.gadi, Wknight94, LifeStar, Sandstein, Bidiot, Theda, Jwissick, Reyk, Staxringold, Smurfy, Katieh5584, Kungfuadam, Moomoomoo, GrinBot~enwiki, DVD R W, Quadpus, SG, A bit iffy, Myrabella, SmackBot, Prodego, FloNight, Pgk, Ohnoitsjamie, Hmains, Persian Poet Gal, NCurse, Ctbolt, John Reaves, George Ho, Can't sleep, clown will eat me, Shalom Yechiel, Yidisheryid, VMS Mosaic, Addshore, Radagast83, Шизомби, Makemi, Nakon, Kalathalan, Richard0612, Pilotguy, Sunroof, Pile3d, J 1982, Heimstern, LSD, Accurizer, Bendzh, Arkrishna, Peyre, CapitalR, Tawkerbot2, Van helsing, Charvex, Jac16888, Ameyjw, Cydebot, Soetermans, Dynaflow, Nikopoley, Lee, Ward3001, Omicronpersei8, Elmeri B. Suokirahvi, Epbr123, Mactographer, O, Kablammo, Relmi, Tellyaddict, Cool Blue, Hoof38, Visik, KrakatoaKatie, AntiVandalBot, Yonatan, Followeroffash- ion, Jj137, Alphachimpbot, MER-C, Matthew Fennell, Freddicus, Kirrages, Magioladitis, VoABot II, Mapetite526, DXRAW, Filippos Greece, PtPrinter, Steve37, DerHexer, Gjd001, Mmustafa~enwiki, MartinBot, Physicists, Poeloq, Ash, Manticore, Svetovid, Thaurisil, Good-afternun!, Bcartolo, AntiSpamBot, Floaterfluss, Joshafina, Fountains of Bryn Mawr, Brian Pearson, Bigdumbdinosaur, Omen666, 2help, KylieTastic, SBKT, Huzefahamid, Macedonian, Jeff G., TheMindsEye, Smart-pari, Ryan032, Philip Trueman, Mercurywoodrose, Rebornsoldier, Calineed, Ghighi31~enwiki, Elphion, BwDraco, Joesaphier, Saturn star, Miwanya, WikiCantona, Johndouglascimfl2, Smilegood, Meters, Falcon8765, My favourite teddy bear, Hemia, Bo, B. Nuhanen, Nihil novi, Parhamr, GlassCobra, Dataneger~enwiki, Oxymoron83, Bagatelle, Lightmouse, Hyker, Correogsk, Behtis, Globulinapr1966, Slapazoid, Vonones, Fm19, Martarius, Rachelf8582, ClueBot, The Thing That Should Not Be, Rodhullandemu, Wysprgr2005, Piledhigheranddeeper, Cherylpok, Excirial, Ottawa4ever, Thehelpfulone, Aitias, XLinkBot, Cmr08, Coewiddabikdik, Addbot, Aelkris, Rummy9000, CanadianLinuxUser, MrOllie, 3193wct, Favonian, LittlePrincess23, Tide rolls, Lightbot, Krano, Raymond W Chan, Erinn1234, Yobot, Backdropsmadhu, The Iron Rooster, Ripper0607, GaiusStat, Marshall Williams2, Robert Treat, Blackwellmas229, Killiondude, Jim1138, Galoubet, Piano non troppo, Ad- justShift, Paterson229, Ulric1313, Capricorn42, WilsonGMas229, FergusonMAS229, Zuluetamas229, LATTINMAS229, Gbchaosmas- ter, Omnipaedista, Kekela, IcedNut, Shadowjams, Historygoof, Nagualdesign, FrescoBot, Liquidluck, 123spot, Stsnoopy, Arte&Ficção, Pinethicket, Serols, Primaler, Nslimak, The WikiProwler, Caribibble, Lotje, Begoon, Miracle Pen, Aoidh, Nn4beatbox, Smartgod, As- ddassda, Mean as custard, Dalba, Lopifalko, WikitanvirBot, Jonosrummerboy, Gfoley4, Look2See1, Ibbn, Saad Tarik, Jmencisom, Wikipelli, Dcirovic, AsceticRose, Fæ, Hirumon, Natvh4, WeRon, EricWesBrown, JoeSperrazza, Pula123, Gray eyes, AVarchaeolo- gist, Tomer Gott, Targaryen, Katemo5, 28bot, ClueBot NG, PhotoShoot19, Satellizer, ConcernedPhotographer, Hiuby, Irpm, Darafsh, ChrisGualtieri, Garion Mywaywood, Sariys, Asisman, Barriebain, Mogism, Adamdarain, Tima1234554, Grizzly853741, PinkAmper- sand, Seren4219, Henryonus17, Rybec, Muricaaa, Drchriswilliams, UY Scuti, Racer Omega, Dark Mistress, Fixuture, Conaugh12, Karimmundere, Boboboboblol, Nischal bhandari, Dilkeshvar.wiki, Zirkusfan, Swayum mishra, Kangerzuur, TeaLover1996, Some Gad- get Geek, Abdulla181293, Jfjjfhffnfnfnfndnd, Brendan Anapoell, Ali Hàmmadh, Kral3463, Piglander, BBC mort, Adam9007, Inayath innu'z, Talosm03, Davedumbell, Drewdahle, DatGuy, DBZFan30, Joeman Empire, ILoveMyselfandOthers and Anonymous: 404 • Image file formats Source: https://en.wikipedia.org/wiki/Image_file_formats?oldid=762059389 Contributors: Zundark, Dcljr, Kier- ant, Samsara, AnonMoos, RickBeton, Chris 73, DavidCary, JamesHoadley, Utcursch, Keoniphoenix, GreenReaper, Rich Farmbrough, Hhielscher, Koenige, Mancomb, Dystopos, Shenme, L.Willms, Teeks99, Varuna, Alansohn, Hu, Ronark, Rick Sidwell, Mikeo, Zshzn, Trevie, Phillipsacp, AlbertCahalan~enwiki, Waldir, Magister Mathematicae, Jijinmachina, Rjwilmsi, Wikibofh, Jdowland, Bubba73, DoubleBlue, Nihiltres, Gurch, DEIDATVM, DVdm, YurikBot, Wavelength, StuffOfInterest, Hydrargyrum, Cpuwhiz11, Wiki alf, Welsh, Aaron Schulz, Shotgunlee, Rwalker, Xpclient, FF2010, Closedmouth, Ketsuekigata, SmackBot, KnowledgeOfSelf, Hydrogen Iodide, KaiUwe, TFMcQ, Thunderboltz, Fitch, Pandion auk, MindlessXD, Kslays, Yamaguchi, Gilliam, Brianski, Bluebot, Oli Filth, TheS- pectator, Jerome Charles Potts, ERobson, Kitteh~enwiki, Frap, Jennica, Reclarke, Rrburke, Aldaron, Ne0Freedom, AdmiralMemo, A5b, Bostwickenator, Spiritia, LSD, Peterlewis, CyrilB, Beetstra, Dicklyon, Kvng, Ojan, UncleDouggie, GDallimore, RekishiEJ, Thats- fearagain, Davidbspalding, Ludwig Boltzmann, CmdrObot, Spankman, KyraVixen, Yaris678, Mdc299f, Crossmr, ST47, Hounddog32, Vanished User jdksfajlasd, Ebichu63, Epbr123, Ultimus, N5iln, Bobblehead, Dfrg.msc, Icep, Jtmoon, MER-C, Instinct, LittleOldMe, SteveSims, VoABot II, BucsWeb, Catgut, LaVieEntiere, Esanchez7587, Jackson Peebles, HotXRock, Drewmutt, Shellwood, Shred- der46, Petrwiki, Hodlipson, Mikael Häggström, Jorfer, KylieTastic, Stanqo, Tweisbach, Vanished user 39948282, Bonadea, Martial75, Tkgd2007, SoCalSuperEagle, Ryan032, Anna Lincoln, Elphion, J0EY Bukkake, Programcpp, Madhero88, Billinghurst, PeterEast- hope, Dirkbb, Meters, Skarz, Jungegift, Darxus, Ben Boldt, Tresiden, Baltimark, Hebisddave, PookeyMaster, Oxymoron83, Jdaloner, Johnakinjr01, Bencherlite (AWB), Dillard421, Paiev, Paulinho28, Hebbster, Kauai68, TheDooD111, HairyWombat, Loren.wilton, Clue- Bot, Hippo99, Digitalkiller, Senderovich, Drmies, Mild Bill Hiccup, Robmontagna, Paulcmnt, Excirial, Anon lynx, Vanisheduser12345, Lartoven, Sun Creator, Sonicdrewdriver, NuclearWarfare, Matty0wnz, Swindbot, ZX787, BarretB, XLinkBot, Spitfire, Dlpkbr, Little Mountain 5, Skarebo, WikHead, Addbot, Legaladvantagellc, Ghettoblaster, Some jerk on the Internet, Mabdul, Non-dropframe, Ateth- nekos, Blethering Scot, Jncraton, Charstiny, Chamal N, Tassedethe, Alfie66, Math Champion, Yobot, Kpharshan, AnomieBOT, Ciphers, 1exec1, Jim1138, Galoubet, Piano non troppo, Kingpin13, Halfs, Materialscientist, Gsmgm, Andrew-916, Ched, China cobra, Uber- mensk, Nantucketnoon, A.amitkumar, Dougofborg, FrescoBot, Michael93555, Mfwitten, Outback the koala, Davydoo, I dream of horses, Quantumsilverfish, Lotje, Rio98765, Jpcha2, Barry Pearson, Jeffrd10, EmausBot, John of Reading, T3dkjn89q00vl02Cxp1kqs3x7, Im- munize, Dewritech, Mo ainm, Dcirovic, K6ka, Checkingfax, MorbidEntree, Walter.Arrighetti, AOC25, Tolly4bolly, Bomazi, David- carroll123456, 28bot, ClueBot NG, Matthiaspaul, MelbourneStar, This lousy T-shirt, Satellizer, Shaddim, 123Hedgehog456, Very triv- ial, Widr, Antiqueight, Treyofdenmark, Be..anyone, Ankamsarav, Hza a 9, Nospildoh, Mark Arsten, Petejbell, Designer4u, Glacialfox, Klilidiplomus, JGM73, Murughendra, Pratyya Ghosh, SergeantHippyZombie, Basemetal, TwoTwoHello, Stewwie, Faizan, Bughuntr, Fumiko Take, Jodosma, Tentinator, Airsynth, Serpinium, Sahya04, Makkachin, Nikmolnar, Eduardo Leal 20, XavierXerxes, Cousteau, Lovepercy4ever, Kisses888, Horseless Headman, WikiWisePowder, BethNaught, Avi0307, Domsacraft, Narky Blert, Patchoulol~enwiki, Oleaster, DiscantX, My Chemistry romantic, CAPTAIN RAJU, EvanStathem, Simplexity22, DatGuy, Hanscoil, 123nav, Fmadd, Game- zone123, Monkeyman123 PRO and Anonymous: 468 • Pixel Source: https://en.wikipedia.org/wiki/Pixel?oldid=763610304 Contributors: Kpjas, Zundark, Tarquin, Ap, Seb, Aldie, Ghakko, Si- monP, Merphant, Ellmist, Heron, Hephaestos, Lumpbucket, Hfastedge, Frecklefoot, Edward, Patrick, Ioapetraka, Michael Hardy, Dante Alighieri, Georgec, Liftarn, Wapcaplet, Ixfd64, Zanimum, Dcljr, Karada, Oyd11, Arpingstone, Ellywa, Ahoerstemeier, DavidWBrooks, 94 CHAPTER 11. TEXT AND IMAGE SOURCES, CONTRIBUTORS, AND LICENSES

Haakon, Andres, Ghewgill, Pizza Puzzle, Crissov, Andrevan, Wik, Omegatron, Ed g2s, Wernher, Fvw, Guppy, Donarreiskoffer, Bearcat, Robbot, Scott McNay, Mlaine, Hadal, Pengo, Centrx, Giftlite, Dbenbenn, DocWatson42, Andries, Kim Bruning, Seabhcan, Fastfission, Enos Shenk, Fleminra, LarryGilbert, Yekrats, Bit~enwiki, Dmmaus, Nayuki, Brockert, Utcursch, MrMambo, LucasVB, Secfan, Hk- pawn~enwiki, Askewchan, Quota, Andreas Kaufmann, Moxfyre, Astronouth7303, Imroy, DanielCD, Discospinster, FiP, Bender235, ESkog, ZeroOne, Sum0, Slokunshialgo, Violetriga, Miraceti, Bookofjude, Bobo192, Smalljim, Dennis Valeev, Kjkolb, Sam Korn, Ha- ham hanuka, Pearle, Mdd, Lysdexia, Blahma, Polarscribe, Arthena, Pforret, Keenan Pepper, Andrewpmk, Ricky81682, Water Bottle, Malo, Snowolf, Wtshymanski, Dosnerd90, Forderud, Marasmusine, Firsfron, Jeffrey O. Gustafson, LOL, Pinball22, Merlinme, Miaow Miaow, Zdog18, Graham87, TAKASUGI Shinji, David Levy, Kbdank71, JIP, Sjö, Basu, Feydey, The wub, Reedbeta, FlaBot, Mark83, RexNL, Gurch, Preslethe, M7bot, Srleffler, Gurubrahma, Atchius, Chobot, Bornhj, DVdm, Kjlewis, Spike91~enwiki, Roboto de Ajvol, YurikBot, Wavelength, Angus Lepper, Themepark, Sceptre, Bhny, Hondurazian, Kirill Lokshin, Chensiyuan, Gaius Cornelius, Cryptic, NawlinWiki, Wiki alf, Janke, ZacBowling, Tony1, Alex43223, Bota47, Sebleblanc, Elkman, Yudiweb, Closedmouth, KGasso, Reyk, Pursin1, Anclation~enwiki, AGToth, Rob215, AndrewWTaylor, Planemad, SmackBot, Davepape, InverseHypercube, CelticJobber, Ariedartin, Unyoyega, Midway, Arny, Agentbla, BiT, Gilliam, Ohnoitsjamie, Betacommand, Mwhiteguy, Chris the speller, Agateller, Dreg743, Trekphiler, Can't sleep, clown will eat me, Frap, Rrburke, VMS Mosaic, RedHillian, Aggsal, Fuhghettaboutit, Jfingers88, Luigi.a.cruz, Pilotguy, Rory096, Doug Bell, Soumyasch, Minna Sora no Shita, Makyen, Dicklyon, AxG, Tuspm, Squirepants101, Pre- witt81, OnBeyondZebrax, Bricevdm, Tawkerbot2, Amniarix, Hamish2k, Zarex, Ruslik0, Jesse Viviano, CuriousEric, Simeon, Nmacu, Samuell, Gogo Dodo, Doug Weller, Vanished User jdksfajlasd, Localdara, Rocket000, Mattisse, Epbr123, Wikid77, Sergeyy, Urdna, Headbomb, Santrobi, Marek69, Electron9, Philippe, Silver Edge, Escarbot, AntiVandalBot, Михајло Анђелковић, Indrek, Fritz Jörn, Ran4, Sluzzelin, MER-C, Kprateek88, Robert Buzink, Magioladitis, Bongwarrior, VoABot II, Fusionmix, Wikidudeman, Brandt Luke Zorn, Dogsthatchasecars, Sodabottle, TimMagic, SimonPowell, Bubba hotep, Catgut, Sgr927, 28421u2232nfenfcenc, David Eppstein, DerHexer, Kim0kim0, ForestJay, DukeTwicep, MartinBot, Poeloq, JayJayKay~enwiki, Jim.henderson, Speck-Made, R'n'B, Mikeipedia, ElGordo, J.delanoy, Ali, Lordofthe9, Eliz81, Xenoranger, Cpiral, Nothingofwater, Shawn in Montreal, Goingstuckey, SJP, Handy helper, Binba, Juliancolton, DorganBot, Bonadea, Chsimps, Funandtrvl, VolkovBot, SmartAVI, Jeff G., TheMindsEye, NathanHagen, Oshwah, Rebornsoldier, ScriptedGhost, Qxz, Ferengi, Raymondwinn, Maxim, Punch, Drunk, Love, WinTakeAll, Vladsinger, Haseo9999, Fal- con8765, Ethaniscool1, AlleborgoBot, Thw1309, SieBot, Bedelato, Sjanuary, BotMultichill, ToePeu.bot, Caltas, Matthew Yeager, This, that and the other, Keilana, Tiptoety, Dhatfield, JuanFox, Oxymoron83, Tombomp, Alex.muller, Svick, Maelgwnbot, Nimbusania, Van- ished User 8902317830, Fishnet37222, Pinkadelica, Denisarona, C0nanPayne, Atif.t2, Martarius, ClueBot, Traveler100, Gits (Neo), Gbrugman, Necronomicronistic, N1ckFG, Puchiko, Auntof6, DragonBot, Alexbot, Jotterbot, Gciriani, A9gould1, BarretB, XLinkBot, Avoided, SilvonenBot, Mifter, WikiDao, EEng, Addbot, SouthernMyst, Barsoomian, M.nelson, Pete Peterson, Samus3456, Bazza1971, Ld100, Roux, West.andrew.g, 5 albert square, Mps, Legobot, Luckas-bot, Yobot, Morken, Bryan.burgers, Squish7, AnomieBOT, Jim1138, Galoubet, AdjustShift, Aditya, Flewis, Materialscientist, Dontknowhow, Xqbot, Akhilhello6, Sionus, MARKYSPARKY19, Omnipaedista, Shirik, RibotBOT, Erik9, Dave3457, Moloch09, Fcjefe, Citation bot 1, Pshent, Pinethicket, HRoestBot, Hamtechperson, RedBot, Fu- mitol, Jerodast, Calle Cool, Vrenator, Miracle Pen, Sabisteven, Tbhotch, DARTH SIDIOUS 2, Mean as custard, Noommos, DASHBot, Heracles31, Super48paul, Thecheesykid, Shuipzv3, R. J. Mathar, −19S.137.93.171, OnePt618, Thurs Day, Dkstraw, Gsarwa, Donner60, Purpledramallama, 28bot, Timothy1808, Samarthwiz, Locador, ClueBot NG, Cgbuff, Verpies, Adwiii, Rezabot, Widr, Douglasvburge- son, Jk2q3jrklse, Helpful Pixie Bot, Wbm1058, BZTMPS, BG19bot, LuckyIMstar, Registreernu, Zigzagfes, MusikAnimal, Rsama- hamed, 220 of Borg, StormWar0001, Tutelary, Mrt3366, ChrisGualtieri, Drkamilz, Lugia2453, Frosty, Mejbp, Hardik1991, François Robere, JamesMoose, Tentinator, Vinay.sah68, Tdonlin21, Nakitu, Enderchestfrantic, Thekoolguy287, PapiDimmi, ConnorAlex1997, Hhnv.vbbn.gb.gbcgh, Monkbot, Acvideo1, Joeleoj123, Madsausage13, Roollerr, Olliverandshiahsucks, KurodaSho, Bill Gates10, Jonny775, Izkala, Gamingforfun365, CAPTAIN RAJU, CyberWarfare, JoshBM16, Yakshinder, Darshank641, Troller Trolled, Fmadd, Felix The Cat 12345678910, Hamza Liaqat, Bender the Bot, EurIng, Tompop888, Toby991 and Anonymous: 493 • Color histogram Source: https://en.wikipedia.org/wiki/Color_histogram?oldid=758489260 Contributors: Zundark, Charles Matthews, Saltine, Samsara, Asc99c, Jorge Stolfi, Rich Farmbrough, Lkinkade, Jeff3000, Sjakkalle, Strait, Adoniscik, UkPaolo, YurikBot, Petiatil, Wiki alf, Grafen, Jhinman, So two Willys walk into a bar..., SmackBot, KYN, Bluebot, Kostmo, Beetstra, Dicklyon, StanfordProgrammer, HenningThielemann, Thijs!bot, JAnDbot, Jasonwjones, R'n'B, Althepal, Rebornsoldier, Valentein, Wiae, Chenzw, Petr.noha, Seaniedan, Addbot, Ollydbg, Codepete, Yobot, AnomieBOT, GB fan, Neurolysis, Xqbot, Alexander Anoprienko, FrescoBot, Pinethicket, ClueBot NG, Helpful Pixie Bot, MaxPlank111, Glabrador2013, GinAndChronically, Ymshang, Muenteroman, WClarke and Anonymous: 41 • Image histogram Source: https://en.wikipedia.org/wiki/Image_histogram?oldid=742341739 Contributors: Samsara, Abdull, Mdd, Kvaks, Axeman89, Mindmatrix, Josh Parris, Bubba73, Thejapanesegeek, ONEder Boy, IronTek, SmackBot, InverseHypercube, Beetstra, Dick- lyon, Artoonie, Rehno Lindeque, Cydebot, Marcuscalabresus, LivingShadow, Llorenzi, Cfolson, Mihirgokani007, Lara bran, Gkay1500, HairyWombat, ClueBot, Manamarak, Dhulme, Dekart, Cewvero, Addbot, Lolicious, RoninChris64, Jannic86, AnomieBOT, Materialsci- entist, Xqbot, Fatheroftebride, FrescoBot, Pinethicket, KarthikeyanKC, Andfriend, John of Reading, Tuankiet65, Wikipelli, Macphesa, Grandphuba, Mentibot, JaffaMan, Helpful Pixie Bot, MusikAnimal, Silvrous, Purest1, Cqwi, MaxPlank111, Normantg, OzzyRameses, Thatguyfromspace, Bender the Bot and Anonymous: 26 • Pixel density Source: https://en.wikipedia.org/wiki/Pixel_density?oldid=761538110 Contributors: AxelBoldt, Edward, Wapcaplet, Karada, Glueball, Scarfboy, Blainster, David Gerard, Jao, Dmmaus, Chowbok, Mproud, Chmod007, Adah1972, Bender235, Polluks, Railgun, Guy Harris, Wtmitchell, Rick Sidwell, Stephan Leeds, MONGO, GregorB, FlaBot, GreyCat, Peter S., Phlip, Rada, WulfTheSaxon, Arich- nad, Kjmathew, Glenn W, X-mass, AGToth, Cmglee, Myrabella, SmackBot, Adam majewski, Agentbla, Chris the speller, Thumper- ward, Deepakaaa, Greatgavini, Wizard04, Weierstraß, Poobarb, Frap, Weirdy, Aldaron, A5b, RomanSpa, 16@r, Yarnalgo, WeggeBot, CieloEstrellado, Mattisse, Edupedro, Al Lemos, Laportechicago, Widefox, JAnDbot, Dream Focus, Websterwebfoot, Bwagstaff, Zivha, Plasticup, Rdfr, Vicpro, Luiswtc73, G00nsf, Cryonic07, Tresiden, Lennartgoosens, Jvs, Jimthing, Faradayplank, BLLuten, Dagorath, Keraunoscopia, DragonBot, Ykhwong, Mlaffs, Theking2, CapnZapp, Addbot, Kjay227, Fgnievinski, MrOllie, Dedsmith, FronteiraFinal, ,Raffamaiden, Omnipaedista, Travürsa ,عبد الناصر سعيد ,Fraggle81, AnomieBOT, Archon 2488, Asdfjkl5999, Joel Amos, Jvg456 Koss x treeme, Krukrus, FrescoBot, Remotelysensed, Pristino, Czhanacek, Dewritech, GoingBatty, Ponydepression, Ὁ οἶστρος, Sim- plesimonsez, WalterTross, Gsarwa, John Smith 104668, 123GhostMonkey, Sjoerddebruin, ClueBot NG, Giggett, PanderMusubi, Help- ful Pixie Bot, BG19bot, Connectsreekanth, Samymarboy, Maarten Rail, Jimw338, Quant18, Mogism, Wywin, Cytex, Mike Mounier, Tentinator, DavidLeighEllis, Iopjklohyeah, Comp.arch, Ariakepeoples, My name is not dave, YiFeiBot, Dave Zember, RedPandaCub, Aplayer12345, Mk197, Squinge, Kyrhon, Rehmanpk22, Neculai Daniel, Pander, Jackbirda, Orangeapple21 and Anonymous: 110 • Dots per inch Source: https://en.wikipedia.org/wiki/Dots_per_inch?oldid=748813740 Contributors: AxelBoldt, Wapcaplet, Alfio, Kimiko, Cherkash, Crissov, Bevo, Robbot, RossA, Rasmus Faber, Wereon, Jleedev, Clementi, JamesMLane, Jao, SWAdair, Bobblewik, Ary29, RandalSchwartz, Guanabot, Qutezuce, Notinasnaid, Spoon!, Smalljim, Polluks, Shlomital, Japsu, M7, Burn, Wtmitchell, Rebroad, Rick Sidwell, Gene Nygaard, Vijayan, Daira Hopwood, Jacobolus, Ses4j, Joe Decker, Pako, Mecandes, FlaBot, Margosbot~enwiki, KFP, 11.2. IMAGES 95

Preslethe, DVdm, UkPaolo, YurikBot, RobotE, Adam1213, Hede2000, Arichnad, Silverdaemonskye, Xaje, Zwobot, Oliverdl, IceCrea- mAntisocial, User27091, Daniel G., SmackBot, Fireworks, Agentbla, Gilliam, Bluebot, AaronRosenberg, RoboDick~enwiki, Morshem, KittensOnToast, Dicklyon, Tawkerbot2, Vanisaac, CmdrObot, USAOwnz, Zginder, Heidijane, Thijs!bot, Wikid77, Electron9, Mixsynth, Laportechicago, AntiVandalBot, Scepia, Gdo01, ClassicSC, JAnDbot, Gatemansgc, SteveSims, Freshacconci, VoABot II, Xenogyst, Nikevich, Objectivesea, Zivha, Mschel, Beijing 888, RenniePet, DMCer, .i.huvudet, Rocketmagnet, Kkemp, Eddiehimself, Tem- poraluser, UnneededAplomb, Parhamr, Ken123BOT, Fishnet37222, Martarius, ClueBot, Robenel, ImperfectlyInformed, DragonBot, Alexbot, M4gnum0n, Anti Virus 777, Naleh, DanielPharos, Nepenthes, NellieBly, MystBot, Addbot, Onmywaybackhome, Favonian, Exor674, Yobot, Ptbotgourou, SwisterTwister, AnomieBOT, GrouchoBot, GliderMaven, Tangent747, TomC45, Idyllic press, Mfwitten, Pinethicket, Tition1, OldGeazer, Lukamus, Jonkerz, Vrenator, Reach Out to the Truth, The Utahraptor, EmausBot, WikitanvirBot, Bdi- jkstra, Dewritech, GoingBatty, Max theprintspace, Pacheconha, Evanh2008, AvicBot, L Kensington, Ollytheninja, ClueBot NG, Jack Greenmaven, Okthatsnice, Be..anyone, Vcohen, EnergyDome, Jimw338, Hngjms, Andybdesign, Iamsorandom, Jodosma, Jacedc, Kind Tennis Fan, WonderfulRetard, Sam Hnri, Narky Blert, Crystallizedcarbon, PiotrGrochowski000, Bushbaby234, Burnedsausage101 and Anonymous: 176 • Imaging technology Source: https://en.wikipedia.org/wiki/Imaging_technology?oldid=691076455 Contributors: Mkmcconn, Justany- one, Michael Snow, CanisRufus, Jeffrey O. Gustafson, Mitsukai, Theda, JHunterJ, Sthomson06, Dtgriscom, Husond, R'n'B, Funandtrvl, Radical man 7, Biscuittin, Boleyn, AnomieBOT, Helpful Pixie Bot, Redress perhaps and Anonymous: 2 • Image viewer Source: https://en.wikipedia.org/wiki/Image_viewer?oldid=737640810 Contributors: Stevertigo, Patrick, Voidvector, Dys- prosia, Samsara, Francs2000, Naddy, Honta, Taufito~enwiki, HorsePunchKid, Gronky, Neko-chan, Kwamikagami, Maurreen, Minghong, TheParanoidOne, Jkl sem, Karnesky, Phillipsacp, Santiago Roza (Kq), DirkvdM, FlaBot, YurikBot, LauriO~enwiki, Rwxrwxrwx, Smack- Bot, Adam majewski, Frap, Zazpot, Radagast83, Wizardman, Beetstra, Hu12, Paul Foxworthy, ThefirstM, Yaris678, Michaelas10, Thijs!bot, Alphachimpbot, Tedickey, AVRS, Cyrus abdi, KylieTastic, VolkovBot, Fences and windows, TXiKiBoT, X-Bert, Jamelan, Soler97, WikiLaurent, Ananth126, Mypaluga, PixelBot, SF007, Axelriet, Addbot, Ghettoblaster, Menschenfressender Riese, CarsracBot, Numbo3-bot, Rubinbot, Xqbot, Jordiferrer, JCrue, Erik9bot, LittleWink, RedBot, MastiBot, Adam bradly, Nekohan, MrX, 777sms, A.alexe, ZéroBot, Josve05a, ClueBot NG, Broskov-nielsen, Cleverwikiname, Webclient101, Lugia2453, Aldunlap, SeanK842, Wedepe001 and Anonymous: 37 • Image organizer Source: https://en.wikipedia.org/wiki/Image_organizer?oldid=752786532 Contributors: Zundark, Samsara, Danhuby, David Edgar, Seano1, Chowbok, Davidshq, EurekaLott, Marasmusine, Dandv, RzR~enwiki, OMouse, Bubuka, RussBot, LauriO~enwiki, Rwxrwxrwx, GraemeL, SmackBot, Chris the speller, Jethero, Thumperward, Djdole, Frap, OSborn, Drono, Kundansen, Jaqian, Dicklyon, Hu12, RekishiEJ, The Letter J, Dan1679, Medovina, Mkmobil, TimVickers, Tedickey, EagleFan, Oicumayberight, Vagn30~enwiki, J admo, Sagabot, R'n'B, Dukat74, 28bytes, Travelster, Wiae, Jaqen, Billinghurst, Lucyhill, Mike ruar, SheepNotGoats, Faradayplank, Sitush, Mypaluga, Canis Lupus, Jayger, Rradu, Addbot, Andreas DE, Pietrow, Yobot, AnomieBOT, LilHelpa, Foolishmute, Noughmad, FrescoBot, Theowoo, DrilBot, Jamouse, Jujutacular, Nslimak, Nekohan, Starforever, Ronlinenet, Dj5619, The Dark Melon, Marlgryd, AutoGeek, Mentibot, Pierricklegall, Moritz37, Wbm1058, BG19bot, Dylanscott3, Broskov-nielsen, Crazy Jake88, Megadino, Codename Lisa, Rmondo2006, Wikicodeman, E4hg7j, Altered Walter, Colin Nolan, PhotoSoftwareReviewer, Mees Dekker, ScotXW, DariusBanks, Phil was here, Mmbwwp, Fanivek, Wedepe001, Fmadd and Anonymous: 82 • Image retrieval Source: https://en.wikipedia.org/wiki/Image_retrieval?oldid=753126362 Contributors: Patrick, DopefishJustin, Kku, Docu, ZimZalaBim, Remuel, Apoc2400, Rjwilmsi, Adoniscik, Msbmsb, ENeville, Shyam, DVD R W, SmackBot, Klod, KYN, Ohnoits- jamie, Whispering, Hu12, Bepube~enwiki, Pjvpjv, Barek, Webtechscout, Jodi.a.schneider, Mr Randy Moss, Kiore, Jim.henderson, Kyli- eTastic, Jeremykrey, Rnc000, Cfolson, Noteremote, Duffbeerforme, Deselaers, DOI bot, MrOllie, Download, Americanfreedom, Avi Ravner, JackCoke, Citation bot, Xyhcn, Devantheryv, LilHelpa, Allancass, Lotje, EmausBot, John of Reading, Sunilgaral, GoingBatty, Dimaryz, Ericflint, BG19bot, MusikAnimal, Eidenberger, Camargoj, Me, Myself, and I are Here, Hillbillyholiday, BoltonSM3, Fixuture, Mahnunchik, Monkbot, Sarahwilliams25, Jm107, Ava6969, NoToleranceForIntolerance, Marvellous Spider-Man, Ramkumar nunna and Anonymous: 33

11.2 Images

• File:5-cell. Source: https://upload.wikimedia.org/wikipedia/commons/d/d8/5-cell.gif License: Public domain Contributors: Trans- ferred from en.wikipedia to Commons. Original artist: JasonHise at English Wikipedia • File:A_Stream_of_Stars_over_Paranal.jpg Source: https://upload.wikimedia.org/wikipedia/commons/b/bd/A_Stream_of_Stars_over_ Paranal.jpg License: CC BY 4.0 Contributors: http://www.eso.org/public/images/potw1421a/ Original artist: ESO/G. Lombardi • File:Ancientchineseinstrumentalists.jpg Source: https://upload.wikimedia.org/wikipedia/commons/9/9b/Ancientchineseinstrumentalists. jpg License: Public domain Contributors: ? Original artist: ? • File:Ankh_isis_nefertari.jpg Source: https://upload.wikimedia.org/wikipedia/commons/1/1d/Ankh_isis_nefertari.jpg License: Public domain Contributors: public domain Original artist: died 4000 years ago • File:Bayer_matrix.svg Source: https://upload.wikimedia.org/wikipedia/commons/e/ef/Bayer_matrix.svg License: Public domain Con- tributors: Own work Original artist: Amada44 • File:CIRCLE_1.svg Source: https://upload.wikimedia.org/wikipedia/commons/1/1d/CIRCLE_1.svg License: CC-BY-SA-3.0 Contrib- utors: Own work Original artist: en:User:Optimager • File:Cartesian-coordinate-system.svg Source: https://upload.wikimedia.org/wikipedia/commons/0/0e/Cartesian-coordinate-system. svg License: Public domain Contributors: Made by K. Bolino (Kbolino), based upon earlier versions. Original artist: K. Bolino • File:Closeup_of_pixels.JPG Source: https://upload.wikimedia.org/wikipedia/commons/d/de/Closeup_of_pixels.JPG License: GFDL Contributors: Own work Original artist: Kprateek88 • File:Commons-logo.svg Source: https://upload.wikimedia.org/wikipedia/en/4/4a/Commons-logo.svg License: PD Contributors: ? Orig- inal artist: ? • File:Coord_Circular.svg Source: https://upload.wikimedia.org/wikipedia/commons/0/04/Coord_Circular.svg License: Public domain Contributors: Own work Original artist: Andeggs 96 CHAPTER 11. TEXT AND IMAGE SOURCES, CONTRIBUTORS, AND LICENSES

• File:Coord_XY.svg Source: https://upload.wikimedia.org/wikipedia/commons/4/49/Coord_XY.svg License: Public domain Contribu- tors: Own work Original artist: Andeggs • File:Crystal_Clear_device_camera.png Source: https://upload.wikimedia.org/wikipedia/commons/9/99/Crystal_Clear_device_camera. png License: LGPL Contributors: All Crystal Clear icons were posted by the author as LGPL on -look; Original artist: Everaldo Coelho and YellowIcon; • File:DPI_and_PPI.png Source: https://upload.wikimedia.org/wikipedia/commons/3/3d/DPI_and_PPI.png License: CC-BY-SA-3.0 Con- tributors: Own work Original artist: Wapcaplet, improved anonymously (2014) • File:Digon.svg Source: https://upload.wikimedia.org/wikipedia/commons/f/f1/Digon.svg License: Public domain Contributors: Own work Original artist: DanPMK • File:Display_resolution_and_pixel_density.png Source: https://upload.wikimedia.org/wikipedia/commons/7/74/Display_resolution_ and_pixel_density.png License: CC BY-SA 3.0 Contributors: Own work Original artist: Ariakepeoples • File:Dürer_Melancholia_I.jpg Source: https://upload.wikimedia.org/wikipedia/commons/1/18/D%C3%BCrer_Melancholia_I.jpg Li- cense: Public domain Contributors: [1] Original artist: Albrecht Dürer • File:Edit-clear.svg Source: https://upload.wikimedia.org/wikipedia/en/f/f2/Edit-clear.svg License: Public domain Contributors: The Tango! Desktop Project. Original artist: The people from the Tango! project. And according to the meta-data in the file, specifically: “Andreas Nilsson, and Jakub Steiner (although minimally).” • File:Faust_bei_der_Arbeit.JPG Source: https://upload.wikimedia.org/wikipedia/commons/c/c2/Faust_bei_der_Arbeit.JPG License: CC BY 3.0 Contributors: Own work Original artist: Heinzenwies2011 • File:Folder_Hexagonal_Icon.svg Source: https://upload.wikimedia.org/wikipedia/en/4/48/Folder_Hexagonal_Icon.svg License: Cc- by-sa-3.0 Contributors: ? Original artist: ? • File:Henagon.svg Source: https://upload.wikimedia.org/wikipedia/commons/f/fd/Henagon.svg License: Public domain Contributors: Own work Original artist: DanPMK • File:Image_created_with_a_mobile_phone.png Source: https://upload.wikimedia.org/wikipedia/commons/b/b6/Image_created_with_ a_mobile_phone.png License: CC BY-SA 3.0 Contributors: Own work Original artist: Olaf Simons • File:Image_in_Glossographia.png Source: https://upload.wikimedia.org/wikipedia/en/7/7b/Image_in_Glossographia.png License: Pub- lic domain Contributors: ? Original artist: ? • File:Jingangjing.jpg Source: https://upload.wikimedia.org/wikipedia/commons/d/d2/Jingangjing.jpg License: Public domain Contrib- utors: Zoomable image from the British Library’s Online Gallery. Originally uploaded to en:Wikipedia (log) in January 2008 by Fconaway (talk) and in November 2009 by Earthsound (talk). Original artist: The colophon, at the inner end, reads: Reverently [caused to be] made for universal free distribution by Wang Jie on behalf of his two parents on the 13th of the 4th moon of the 9th year of Xiantong [i.e. 11th May, CE 868 ]. • File:Lcd_display_dead_pixel.jpg Source: https://upload.wikimedia.org/wikipedia/commons/2/20/Lcd_display_dead_pixel.jpg License: CC-BY-SA-3.0 Contributors: Own work Original artist: Selçuk Oral (drumex) • File:MARTAKIS1.jpg Source: https://upload.wikimedia.org/wikipedia/commons/1/17/MARTAKIS1.jpg License: Public domain Con- tributors: Universal Music Greece Original artist: Universal Music Greece, uploaded on their behalf by Bratopoulosm • File:Matakis_-_blurred.jpg Source: https://upload.wikimedia.org/wikipedia/commons/f/ff/Matakis_-_blurred.jpg License: CC0 Con- tributors: Original artist: Mikael Häggström • File:Megapixels.JPG Source: https://upload.wikimedia.org/wikipedia/en/a/a0/Megapixels.JPG License: CC-BY-SA-3.0 Contributors: I (skoolboyz) created this work entirely by myself. Original artist: skoolboyz • File:Merge-arrow.svg Source: https://upload.wikimedia.org/wikipedia/commons/a/aa/Merge-arrow.svg License: Public domain Con- tributors: ? Original artist: ? • File:Monet_dejeunersurlherbe.jpg Source: https://upload.wikimedia.org/wikipedia/commons/7/74/Monet_dejeunersurlherbe.jpg Li- cense: Public domain Contributors: ? Original artist: Claude Monet • File:NBSFirstScanImage.jpg Source: https://upload.wikimedia.org/wikipedia/commons/1/1a/NBSFirstScanImage.jpg License: Pub- lic domain Contributors: National Institute of Standards and Technology Original artist: Russell A. Kirsch • File:Napoli_BW_2013-05-16_16-25-06_1_DxO.jpg Source: https://upload.wikimedia.org/wikipedia/commons/5/59/Napoli_BW_2013-05-16_ 16-25-06_1_DxO.jpg License: CC BY-SA 3.0 Contributors: Own work Original artist: Berthold Werner • File:Nicéphore_Niépce_Oldest_Photograph_1825.jpg Source: https://upload.wikimedia.org/wikipedia/commons/e/eb/Nic%C3%A9phore_ Ni%C3%A9pce_Oldest_Photograph_1825.jpg License: Public domain Contributors: Bibliothèque nationale de France Original artist: Nicéphore Niépce (1765-1833) • File:Nuvola_apps_package_graphics.png Source: https://upload.wikimedia.org/wikipedia/commons/2/22/Nuvola_apps_package_graphics. png License: LGPL Contributors: http://icon-king.com Original artist: David Vignoni / ICON KING • File:Odd-eyed_cat_by_ihasb33r-2.gif Source: https://upload.wikimedia.org/wikipedia/commons/9/9f/Odd-eyed_cat_by_ihasb33r-2. gif License: CC BY-SA 2.0 Contributors: • Odd-eyed_cat_by_ihasb33r.jpg Original artist: Odd-eyed_cat_by_ihasb33r.jpg: ihasb33r • File:Odd-eyed_cat_by_ihasb33r.jpg Source: https://upload.wikimedia.org/wikipedia/commons/0/05/Odd-eyed_cat_by_ihasb33r.jpg License: CC BY-SA 2.0 Contributors: http://www.flickr.com/photos/ihasb33r/2573196546/ Original artist: ihasb33r • File:Odd-eyed_cat_histogram.png Source: https://upload.wikimedia.org/wikipedia/commons/f/f5/Odd-eyed_cat_histogram.png Li- cense: CC BY-SA 4.0 Contributors: Own work, File:Odd-eyed cat by ihasb33r.jpg Original artist: Muenteroman 11.2. IMAGES 97

• File:Paul_Gauguin_137.jpg Source: https://upload.wikimedia.org/wikipedia/commons/5/5e/Paul_Gauguin_137.jpg License: Public domain Contributors: The Yorck Project: 10.000 Meisterwerke der Malerei. DVD-ROM, 2002. ISBN 3936122202. Distributed by DIRECTMEDIA Publishing GmbH. Original artist: Paul Gauguin • File:Photograph_of_18th_century_woman.jpg Source: https://upload.wikimedia.org/wikipedia/commons/3/35/Photograph_of_18th_ century_woman.jpg License: Public domain Contributors: ? Original artist: ? • File:Pixel-example.png Source: https://upload.wikimedia.org/wikipedia/commons/2/2b/Pixel-example.png License: CC-BY-SA-3.0 Contributors: Example image is a rendering of Image:Personal computer, exploded 5.svg. Original artist: ed g2s • talk • File:Pixel_geometry_01_Pengo.jpg Source: https://upload.wikimedia.org/wikipedia/commons/4/4d/Pixel_geometry_01_Pengo.jpg Li- cense: CC BY-SA 3.0 Contributors: Own work Original artist: Pengo • File:Portal-puzzle.svg Source: https://upload.wikimedia.org/wikipedia/en/f/fd/Portal-puzzle.svg License: Public domain Contributors: ? Original artist: ? • File:PrinterDots.jpg Source: https://upload.wikimedia.org/wikipedia/commons/c/c7/PrinterDots.jpg License: CC-BY-SA-3.0 Contrib- utors: Transferred from en.wikipedia to Commons. Original artist: The original uploader was Wapcaplet at English Wikipedia • File:Question_book-new.svg Source: https://upload.wikimedia.org/wikipedia/en/9/99/Question_book-new.svg License: Cc-by-sa-3.0 Contributors: Created from scratch in Adobe Illustrator. Based on Image:Question book.png created by User:Equazcion Original artist: Tkgd2007 • File:Raphael_Spasimo.jpg Source: https://upload.wikimedia.org/wikipedia/commons/7/70/Raphael_Spasimo.jpg License: Public do- main Contributors: Transferred from en.wikipedia; transferred to Commons by User:Mutter Erde using CommonsHelper. Original artist: Raphael • File:ReconstructionsFromPixels.png Source: https://upload.wikimedia.org/wikipedia/commons/c/c4/ReconstructionsFromPixels.png License: Public domain Contributors: Transferred from en.wikipedia to Commons. Original artist: Dicklyon at English Wikipedia • File:Red_Fuji_southern_wind_clear_morning.jpg Source: https://upload.wikimedia.org/wikipedia/commons/5/57/Red_Fuji_southern_ wind_clear_morning.jpg License: Public domain Contributors: http://visipix.dynalias.com/cgi-bin/view?s=5&userid=1025493065&q= red%20fuji&u=2&k=0&l=en&n=1 Original artist: Katsushika Hokusai () • File:Regular_decagon.svg Source: https://upload.wikimedia.org/wikipedia/commons/8/8c/Regular_decagon.svg License: Public do- main Contributors: ? Original artist: ? • File:Regular_dodecagon.svg Source: https://upload.wikimedia.org/wikipedia/commons/8/8d/Regular_dodecagon.svg License: Public domain Contributors: ? Original artist: ? • File:Regular_enneadecagon.svg Source: https://upload.wikimedia.org/wikipedia/commons/a/ad/Regular_enneadecagon.svg License: Public domain Contributors: ? Original artist: ? • File:Regular_hendecagon.svg Source: https://upload.wikimedia.org/wikipedia/commons/8/83/Regular_hendecagon.svg License: Pub- lic domain Contributors: ? Original artist: ? • File:Regular_heptadecagon.svg Source: https://upload.wikimedia.org/wikipedia/commons/e/ef/Regular_heptadecagon.svg License: Pub- lic domain Contributors: Own work User:Gustavb/regular_polygon.pl Original artist: Gustavb • File:Regular_heptagon.svg Source: https://upload.wikimedia.org/wikipedia/commons/9/93/Regular_heptagon.svg License: Public do- main Contributors: ? Original artist: ? • File:Regular_hexadecagon.svg Source: https://upload.wikimedia.org/wikipedia/commons/9/9d/Regular_hexadecagon.svg License: Pub- lic domain Contributors: ? Original artist: ? • File:Regular_hexagon.svg Source: https://upload.wikimedia.org/wikipedia/commons/4/41/Regular_hexagon.svg License: Public do- main Contributors: ? Original artist: ? • File:Regular_icosagon.svg Source: https://upload.wikimedia.org/wikipedia/commons/6/60/Regular_icosagon.svg License: Public do- main Contributors: ? Original artist: ? • File:Regular_nonagon.svg Source: https://upload.wikimedia.org/wikipedia/commons/d/dd/Regular_nonagon.svg License: Public do- main Contributors: Created by Gustavb using User:Gustavb/regular_polygon.pl. Original artist: Gustavb • File:Regular_octadecagon.svg Source: https://upload.wikimedia.org/wikipedia/commons/b/b7/Regular_octadecagon.svg License: Pub- lic domain Contributors: ? Original artist: ? • File:Regular_octagon.svg Source: https://upload.wikimedia.org/wikipedia/commons/6/66/Regular_octagon.svg License: Public do- main Contributors: Created by Gustavb using User:Gustavb/regular_polygon.pl. Original artist: Gustavb • File:Regular_pentadecagon.svg Source: https://upload.wikimedia.org/wikipedia/commons/0/03/Regular_pentadecagon.svg License: Public domain Contributors: ? Original artist: ? • File:Regular_pentagon.svg Source: https://upload.wikimedia.org/wikipedia/commons/3/39/Regular_pentagon.svg License: Public do- main Contributors: Created by Gustavb using Gustavb/regular polygon.pl Original artist: Gustavb • File:Regular_quadrilateral.svg Source: https://upload.wikimedia.org/wikipedia/commons/f/f7/Regular_quadrilateral.svg License: Pub- lic domain Contributors: ? Original artist: ? • File:Regular_tetradecagon.svg Source: https://upload.wikimedia.org/wikipedia/commons/b/b8/Regular_tetradecagon.svg License: Pub- lic domain Contributors: ? Original artist: ? • File:Regular_triangle.svg Source: https://upload.wikimedia.org/wikipedia/commons/e/ec/Regular_triangle.svg License: Public domain Contributors: ? Original artist: ? • File:Regular_tridecagon.svg Source: https://upload.wikimedia.org/wikipedia/commons/0/09/Regular_tridecagon.svg License: Public domain Contributors: ? Original artist: ? 98 CHAPTER 11. TEXT AND IMAGE SOURCES, CONTRIBUTORS, AND LICENSES

• File:Resolution_illustration.png Source: https://upload.wikimedia.org/wikipedia/commons/f/f2/Resolution_illustration.png License: Public domain Contributors: ? Original artist: ? • File:SEACComputer_031.jpg Source: https://upload.wikimedia.org/wikipedia/commons/5/5c/SEACComputer_031.jpg License: Pub- lic domain Contributors: National Institute of Standards and Technology Original artist: National Institute of Standards and Technology • File:Sensorauflösungen.svg Source: https://upload.wikimedia.org/wikipedia/commons/f/f7/Sensoraufl%C3%B6sungen.svg License: CC BY-SA 3.0 Contributors: Own work Original artist: Martin Kraft • File:Shadow_mask_closeup_cursor.jpg Source: https://upload.wikimedia.org/wikipedia/commons/8/8d/Shadow_mask_closeup_cursor. jpg License: CC-BY-SA-3.0 Contributors: Own work Original artist: Selçuk Oral (drumex) • File:Square_200x200.svg Source: https://upload.wikimedia.org/wikipedia/commons/2/27/Square_200x200.svg License: Public domain Contributors: Own work Original artist: Yarnalgo • File:Star_polygon_10-3.svg Source: https://upload.wikimedia.org/wikipedia/commons/2/22/Star_polygon_10-3.svg License: Public domain Contributors: Own work Original artist: Inductiveload • File:Star_polygon_5-2.svg Source: https://upload.wikimedia.org/wikipedia/commons/b/bc/Star_polygon_5-2.svg License: Public do- main Contributors: Own work Original artist: Inductiveload • File:Star_polygon_7-2.svg Source: https://upload.wikimedia.org/wikipedia/commons/4/42/Star_polygon_7-2.svg License: Public do- main Contributors: Own work Original artist: Inductiveload • File:Star_polygon_7-3.svg Source: https://upload.wikimedia.org/wikipedia/commons/c/c9/Star_polygon_7-3.svg License: Public do- main Contributors: Own work Original artist: Inductiveload • File:Star_polygon_8-3.svg Source: https://upload.wikimedia.org/wikipedia/commons/5/5d/Star_polygon_8-3.svg License: Public do- main Contributors: Own work Original artist: Inductiveload • File:Star_polygon_9-2.svg Source: https://upload.wikimedia.org/wikipedia/commons/9/97/Star_polygon_9-2.svg License: Public do- main Contributors: Own work Original artist: Inductiveload • File:Star_polygon_9-4.svg Source: https://upload.wikimedia.org/wikipedia/commons/4/46/Star_polygon_9-4.svg License: Public do- main Contributors: Own work Original artist: Inductiveload • File:SunHistacp.jpg Source: https://upload.wikimedia.org/wikipedia/commons/2/2f/SunHistacp.jpg License: Public domain Contribu- tors: • Own work by the original uploader • Made using Paint Shop Pro Original artist: Alison Phillips • File:SunLou2.jpg Source: https://upload.wikimedia.org/wikipedia/commons/9/95/SunLou2.jpg License: Public domain Contributors: • Own work by the original uploader • Taken in uploader’s back yard in Vero Beach, FL Original artist: Alison Phillips • File:Symbol_neutral_vote.svg Source: https://upload.wikimedia.org/wikipedia/en/8/89/Symbol_neutral_vote.svg License: Public do- main Contributors: ? Original artist: ? • File:Symcamera1.jpg Source: https://upload.wikimedia.org/wikipedia/en/f/f0/Symcamera1.jpg License: CC-BY-SA-3.0 Contributors: by matlab Original artist: Ymshang • File:Symcamera2.jpg Source: https://upload.wikimedia.org/wikipedia/en/b/b1/Symcamera2.jpg License: CC-BY-SA-3.0 Contributors: matlab Original artist: Ymshang • File:Symcamera3.jpg Source: https://upload.wikimedia.org/wikipedia/en/1/12/Symcamera3.jpg License: CC-BY-SA-3.0 Contributors: matlab Original artist: Ymshang • File:Synaptic.png Source: https://upload.wikimedia.org/wikipedia/commons/0/05/Synaptic.png License: GPL Contributors: [1] Origi- nal artist: en:User:Burgundavia • File:TEIDE.JPG Source: https://upload.wikimedia.org/wikipedia/commons/a/a8/TEIDE.JPG License: Public domain Contributors: ? Original artist: ? • File:Tesseract.gif Source: https://upload.wikimedia.org/wikipedia/commons/5/55/Tesseract.gif License: Public domain Contributors: Own work Original artist: Jason Hise at English Wikipedia • File:The_Nightwatch_by_Rembrandt.jpg Source: https://upload.wikimedia.org/wikipedia/commons/2/28/The_Nightwatch_by_Rembrandt. jpg License: Public domain Contributors: www.rijksmuseum.nl : Home : Info Original artist: Rembrandt • File:The_Scream.jpg Source: https://upload.wikimedia.org/wikipedia/commons/f/f4/The_Scream.jpg License: Public domain Contrib- utors: • Page: http://www.ibiblio.org/wm/paint/auth/munch/ Original artist: Edvard Munch 11.3. CONTENT LICENSE 99

• File:View_from_the_Window_at_Le_Gras,_Joseph_Nicéphore_Niépce.jpg Source: https://upload.wikimedia.org/wikipedia/commons/ 5/5c/View_from_the_Window_at_Le_Gras%2C_Joseph_Nic%C3%A9phore_Ni%C3%A9pce.jpg License: Public domain Contribu- tors: Rebecca A. Moss, Coordinator of Visual Resources and Digital Content Library, via email. College of Liberal Arts Office of Information Technology, University of Minnesota. http://www.dcl.umn.edu Original artist: Joseph Nicéphore Niépce • File:Vincent_van_Gogh_-_The_Church_in_Auvers-sur-Oise,_View_from_the_Chevet_-_Google_Art_Project.jpg Source: https: //upload.wikimedia.org/wikipedia/commons/a/ad/Vincent_van_Gogh_-_The_Church_in_Auvers-sur-Oise%2C_View_from_the_Chevet_ -_Google_Art_Project.jpg License: Public domain Contributors: 6wEjLceQPXkTtA at Google Cultural Institute maximum zoom level Original artist: Vincent van Gogh • File:WPVA-khamsa.svg Source: https://upload.wikimedia.org/wikipedia/commons/c/cd/WPVA-khamsa.svg License: CC BY 3.0 Con- tributors: Vectorized version of Image:WPVA-khamsa.png by User:Sparkit Original artist: • first version Fluff • File:Wikidata-logo.svg Source: https://upload.wikimedia.org/wikipedia/commons/f/ff/Wikidata-logo.svg License: Public domain Con- tributors: Own work Original artist: User:Planemad • File:Wikipedia_ClearType.png Source: https://upload.wikimedia.org/wikipedia/commons/8/86/Wikipedia_ClearType.png License: Pub- lic domain Contributors: Transferred from en.wikipedia Original artist: Qz at en.wikipedia • File:Wikivoyage-Logo-v3-icon.svg Source: https://upload.wikimedia.org/wikipedia/commons/d/dd/Wikivoyage-Logo-v3-icon.svg Li- cense: CC BY-SA 3.0 Contributors: Own work Original artist: AleXXw

11.3 Content license

• Creative Commons Attribution-Share Alike 3.0