Digital Color Processing
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An Evaluation of Color Differences Across Different Devices Craitishia Lewis Clemson University, [email protected]
Clemson University TigerPrints All Theses Theses 12-2013 What You See Isn't Always What You Get: An Evaluation of Color Differences Across Different Devices Craitishia Lewis Clemson University, [email protected] Follow this and additional works at: https://tigerprints.clemson.edu/all_theses Part of the Communication Commons Recommended Citation Lewis, Craitishia, "What You See Isn't Always What You Get: An Evaluation of Color Differences Across Different Devices" (2013). All Theses. 1808. https://tigerprints.clemson.edu/all_theses/1808 This Thesis is brought to you for free and open access by the Theses at TigerPrints. It has been accepted for inclusion in All Theses by an authorized administrator of TigerPrints. For more information, please contact [email protected]. What You See Isn’t Always What You Get: An Evaluation of Color Differences Across Different Devices A Thesis Presented to The Graduate School of Clemson University In Partial Fulfillment Of the Requirements for the Degree Master of Science Graphic Communications By Craitishia Lewis December 2013 Accepted by: Dr. Samuel Ingram, Committee Chair Kern Cox Dr. Eric Weisenmiller Dr. Russell Purvis ABSTRACT The objective of this thesis was to examine color differences between different digital devices such as, phones, tablets, and monitors. New technology has always been the catalyst for growth and change within the printing industry. With gadgets like the iPhone and the iPad becoming increasingly more popular in the recent years, printers have yet another technological advancement to consider. Soft proofing strategies use color management technology that allows the client to view their proof on a monitor as a duplication of how the finished product will appear on a printed piece of paper. -
Package 'Colorscience'
Package ‘colorscience’ October 29, 2019 Type Package Title Color Science Methods and Data Version 1.0.8 Encoding UTF-8 Date 2019-10-29 Maintainer Glenn Davis <[email protected]> Description Methods and data for color science - color conversions by observer, illuminant, and gamma. Color matching functions and chromaticity diagrams. Color indices, color differences, and spectral data conversion/analysis. License GPL (>= 3) Depends R (>= 2.10), Hmisc, pracma, sp Enhances png LazyData yes Author Jose Gama [aut], Glenn Davis [aut, cre] Repository CRAN NeedsCompilation no Date/Publication 2019-10-29 18:40:02 UTC R topics documented: ASTM.D1925.YellownessIndex . .5 ASTM.E313.Whiteness . .6 ASTM.E313.YellownessIndex . .7 Berger59.Whiteness . .7 BVR2XYZ . .8 cccie31 . .9 cccie64 . 10 CCT2XYZ . 11 CentralsISCCNBS . 11 CheckColorLookup . 12 1 2 R topics documented: ChromaticAdaptation . 13 chromaticity.diagram . 14 chromaticity.diagram.color . 14 CIE.Whiteness . 15 CIE1931xy2CIE1960uv . 16 CIE1931xy2CIE1976uv . 17 CIE1931XYZ2CIE1931xyz . 18 CIE1931XYZ2CIE1960uv . 19 CIE1931XYZ2CIE1976uv . 20 CIE1960UCS2CIE1964 . 21 CIE1960UCS2xy . 22 CIE1976chroma . 23 CIE1976hueangle . 23 CIE1976uv2CIE1931xy . 24 CIE1976uv2CIE1960uv . 25 CIE1976uvSaturation . 26 CIELabtoDIN99 . 27 CIEluminanceY2NCSblackness . 28 CIETint . 28 ciexyz31 . 29 ciexyz64 . 30 CMY2CMYK . 31 CMY2RGB . 32 CMYK2CMY . 32 ColorBlockFromMunsell . 33 compuphaseDifferenceRGB . 34 conversionIlluminance . 35 conversionLuminance . 36 createIsoTempLinesTable . 37 daylightcomponents . 38 deltaE1976 -
Photometric Modelling for Efficient Lighting and Display Technologies
Sinan G Sinan ENÇ PHOTOMETRIC MODELLING FOR PHOTOMETRIC MODELLING FOR EFFICIENT LIGHTING LIGHTING EFFICIENT FOR MODELLING PHOTOMETRIC EFFICIENT LIGHTING AND DISPLAY TECHNOLOGIES AND DISPLAY TECHNOLOGIES DISPLAY AND A MASTER’S THESIS By Sinan GENÇ December 2016 AGU 2016 ABDULLAH GÜL UNIVERSITY PHOTOMETRIC MODELLING FOR EFFICIENT LIGHTING AND DISPLAY TECHNOLOGIES A THESIS SUBMITTED TO THE DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING AND THE GRADUATE SCHOOL OF ENGINEERING AND SCIENCE OF ABDULLAH GÜL UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE By Sinan GENÇ December 2016 i SCIENTIFIC ETHICS COMPLIANCE I hereby declare that all information in this document has been obtained in accordance with academic rules and ethical conduct. I also declare that, as required by these rules and conduct, I have fully cited and referenced all materials and results that are not original to this work. Sinan GENÇ ii REGULATORY COMPLIANCE M.Sc. thesis titled “PHOTOMETRIC MODELLING FOR EFFICIENT LIGHTING AND DISPLAY TECHNOLOGIES” has been prepared in accordance with the Thesis Writing Guidelines of the Abdullah Gül University, Graduate School of Engineering & Science. Prepared By Advisor Sinan GENÇ Asst. Prof. Evren MUTLUGÜN Head of the Electrical and Computer Engineering Program Assoc. Prof. Vehbi Çağrı GÜNGÖR iii ACCEPTANCE AND APPROVAL M.Sc. thesis titled “PHOTOMETRIC MODELLING FOR EFFICIENT LIGHTING AND DISPLAY TECHNOLOGIES” and prepared by Sinan GENÇ has been accepted by the jury in the Electrical and Computer Engineering Graduate Program at Abdullah Gül University, Graduate School of Engineering & Science. 26/12/2016 (26/12/2016) JURY: Prof. Bülent YILMAZ :………………………………. Assoc. Prof. M. Serdar ÖNSES :……………………………… Asst. -
Chapter 6 COLOR and COLOR VISION
Chapter 6 – page 1 You need to learn the concepts and formulae highlighted in red. The rest of the text is for your intellectual enjoyment, but is not a requirement for homework or exams. Chapter 6 COLOR AND COLOR VISION COLOR White light is a mixture of lights of different wavelengths. If you break white light from the sun into its components, by using a prism or a diffraction grating, you see a sequence of colors that continuously vary from red to violet. The prism separates the different colors, because the index of refraction n is slightly different for each wavelength, that is, for each color. This phenomenon is called dispersion. When white light illuminates a prism, the colors of the spectrum are separated and refracted at the first as well as the second prism surface encountered. They are deflected towards the normal on the first refraction and away from the normal on the second. If the prism is made of crown glass, the index of refraction for violet rays n400nm= 1.59, while for red rays n700nm=1.58. From Snell’s law, the greater n, the more the rays are deflected, therefore violet rays are deflected more than red rays. The infinity of colors you see in the real spectrum (top panel above) are called spectral colors. The second panel is a simplified version of the spectrum, with abrupt and completely artificial separations between colors. As a figure of speech, however, we do identify quite a broad range of wavelengths as red, another as orange and so on. -
Computer Graphicsgraphics -- Weekweek 1212
ComputerComputer GraphicsGraphics -- WeekWeek 1212 Bengt-Olaf Schneider IBM T.J. Watson Research Center Questions about Last Week ? Computer Graphics – Week 12 © Bengt-Olaf Schneider, 1999 Overview of Week 12 Graphics Hardware Output devices (CRT and LCD) Graphics architectures Performance modeling Color Color theory Color gamuts Gamut matching Color spaces (next week) Computer Graphics – Week 12 © Bengt-Olaf Schneider, 1999 Graphics Hardware: Overview Display technologies Graphics architecture fundamentals Many of the general techniques discussed earlier in the semester were developed with hardware in mind Close similarity between software and hardware architectures Computer Graphics – Week 12 © Bengt-Olaf Schneider, 1999 Display Technologies CRT (Cathode Ray Tube) LCD (Liquid Crystal Display) Computer Graphics – Week 12 © Bengt-Olaf Schneider, 1999 CRT: Basic Structure (1) Top View Vertical Deflection Collector Control Grid System Electrode Phosphor Electron Beam Cathode Electron Lens Horizontal Deflection System Computer Graphics – Week 12 © Bengt-Olaf Schneider, 1999 CRT: Basic Structure (2) Deflection System Typically magnetic and not electrostatic Reduces the length of the tube and allows wider Vertical Deflection Collector deflection angles Control Grid System Electrode Phosphor Phospor Electron Beam Cathode Electron Lens Horizontal Fluorescence: Light emitted Deflection System after impact of electrons Persistence: Duration of emission after beam is turned off. Determines flicker properties of the CRT. Computer Graphics – Week -
Análise Sensorial (Sensory Analysis) 29-02-2012 by Goreti Botelho 1
Análise Sensorial (Sensory analysis) 29-02-2012 INSTITUTO POLITÉCNICO DE COIMBRA INSTITUTO POLITÉCNICO DE COIMBRA ESCOLA SUPERIOR AGRÁRIA ESCOLA SUPERIOR AGRÁRIA LEAL LEAL Análise Sensorial Sensory analysis AULA T/P Nº 3 Lesson T/P Nº 3 SUMÁRIO: Summary Parte expositiva: Sistemas de medição de cor: diagrama de cromaticidade CIE, sistema de Theoretical part: Hunter e sistema de Munsell. Color Measurement Systems: CIE chromaticity diagram, Hunter system Parte prática: and Munsell system. Determinação de cores problema utilizando o diagrama de cromaticidade Practical part: CIE. Determination of a color problem by using the CIE chromaticity diagram. Utilização do colorímetro de refletância para determinação da cor de frutos. Use of the reflectance colorimeter to determine the color of fruits. Prova sensorial de dois sumos para compreensão da cor de um produto na Sensory taste of two juices to understand the color effect of a product in percepção sensorial. sensory perception. Goreti Botelho 1 Goreti Botelho 2 Why do we need devices to replace the human vision in the food industry? Limitações do olho humano • a) não é reprodutível – o mesmo alimento apresentado a vários provadores ou ao mesmo provador em momentos diferentes pode merecer qualificações diferentes. Este último fenómeno deve-se ao facto de que, em oposição à grande capacidade humana de apreciar diferenças, o homem não tem uma boa “memória da cor”, ou seja, é difícil recordar uma cor quando não a está a ver. • b) a nomenclatura é pouco concreta e até confusa. As expressões “verde muito claro” ou “amarelo intenso” não são suficientes para definir uma cor e muito menos para a reproduzir ou compará-la com outras quando não se dispõe do objecto que tem essa cor. -
AMSA Meat Color Measurement Guidelines
AMSA Meat Color Measurement Guidelines Revised December 2012 American Meat Science Association http://www.m eatscience.org AMSA Meat Color Measurement Guidelines Revised December 2012 American Meat Science Association 201 West Springfi eld Avenue, Suite 1202 Champaign, Illinois USA 61820 800-517-2672 [email protected] http://www.m eatscience.org iii CONTENTS Technical Writing Committee .................................................................................................................... v Preface ..............................................................................................................................................................vi Section I: Introduction ................................................................................................................................. 1 Section II: Myoglobin Chemistry ............................................................................................................... 3 A. Fundamental Myoglobin Chemistry ................................................................................................................ 3 B. Dynamics of Myoglobin Redox Form Interconversions ........................................................................... 3 C. Visual, Practical Meat Color Versus Actual Pigment Chemistry ........................................................... 5 D. Factors Affecting Meat Color ............................................................................................................................... 6 E. Muscle -
Radiometry of Light Emitting Diodes Table of Contents
TECHNICAL GUIDE THE RADIOMETRY OF LIGHT EMITTING DIODES TABLE OF CONTENTS 1.0 Introduction . .1 2.0 What is an LED? . .1 2.1 Device Physics and Package Design . .1 2.2 Electrical Properties . .3 2.2.1 Operation at Constant Current . .3 2.2.2 Modulated or Multiplexed Operation . .3 2.2.3 Single-Shot Operation . .3 3.0 Optical Characteristics of LEDs . .3 3.1 Spectral Properties of Light Emitting Diodes . .3 3.2 Comparison of Photometers and Spectroradiometers . .5 3.3 Color and Dominant Wavelength . .6 3.4 Influence of Temperature on Radiation . .6 4.0 Radiometric and Photopic Measurements . .7 4.1 Luminous and Radiant Intensity . .7 4.2 CIE 127 . .9 4.3 Spatial Distribution Characteristics . .10 4.4 Luminous Flux and Radiant Flux . .11 5.0 Terminology . .12 5.1 Radiometric Quantities . .12 5.2 Photometric Quantities . .12 6.0 References . .13 1.0 INTRODUCTION Almost everyone is familiar with light-emitting diodes (LEDs) from their use as indicator lights and numeric displays on consumer electronic devices. The low output and lack of color options of LEDs limited the technology to these uses for some time. New LED materials and improved production processes have produced bright LEDs in colors throughout the visible spectrum, including white light. With efficacies greater than incandescent (and approaching that of fluorescent lamps) along with their durability, small size, and light weight, LEDs are finding their way into many new applications within the lighting community. These new applications have placed increasingly stringent demands on the optical characterization of LEDs, which serves as the fundamental baseline for product quality and product design. -
Got Good Color? Controlling Color Temperature in Imaging Software
Got Good Color? Controlling Color Temperature in Imaging Software When you capture an image of a specimen, you want it to look like what you saw through the eyepieces. In this article we will cover the basic concepts of color management and what you can do to make sure what you capture is as close to what you see as possible. Get a Reference Let’s define what you want to achieve: you want the image you produce on your computer screen or on the printed page to look like what you see through the eyepieces of your microscope. In order to maximize the match between these images, your system must be setup to use the same reference. White light is the primary reference that you have most control of in your imaging system. It is your responsibility to set it properly at different points in your system. Many Colors of White Although “White” sounds like a defined color, there are different shades of white. This happens because white light is made up of all wavelengths in the visible spectrum. True white light has an equal intensity at every wavelength. If any wavelength has a higher intensity than the rest, the light takes on a hue related to the dominant wavelength. A simple definition of the hue cast of white light is called “Color Temperature”. This comes from its basis in the glow of heated materials. In general the lower the temperature (in °Kelvin = C° + 273) the redder the light, the higher the temperature the bluer the light. The one standard white lighting in photography and photomicrography is 5000°K (D50) and is called daylight neutral white. -
Using Deltae* to Determine Which Colors Are Compatible
Dissertations and Theses 5-29-2011 The Distance between Colors; Using DeltaE* to Determine Which Colors Are Compatible Rosandra N. Abeyta Embry-Riddle Aeronautical University - Daytona Beach Follow this and additional works at: https://commons.erau.edu/edt Part of the Other Psychology Commons Scholarly Commons Citation Abeyta, Rosandra N., "The Distance between Colors; Using DeltaE* to Determine Which Colors Are Compatible" (2011). Dissertations and Theses. 9. https://commons.erau.edu/edt/9 This Thesis - Open Access is brought to you for free and open access by Scholarly Commons. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of Scholarly Commons. For more information, please contact [email protected]. Running Head: THE DISTANCE BETWEEN COLORS AND COMPATABILITY The distance between colors; using ∆E* to determine which colors are compatible. By Rosandra N. Abeyta A Thesis Submitted to the Department of Human Factors & Systems in Partial Fulfillment of the Requirements for the Degree of Master of Science in Human Factors & Systems Embry-Riddle Aeronautical University Daytona Beach, Florida May 29, 2011 Running Head: THE DISTANCE BETWEEN COLORS AND COMPATABILITY 2 Running Head: THE DISTANCE BETWEEN COLORS AND COMPATABILITY 3 Abstract The focus of this study was to identify colors that can be easily distinguished from one another by normal color vision and slightly deficient color vision observers, and then test those colors to determine the significance of color separation as an indicator of color discriminability for both types of participants. There were 14 color normal and 9 color deficient individuals whose level of color deficiency were determined using standard diagnostic tests. -
Prepared by Dr.P.Sumathi COLOR MODELS
UNIT V: Colour models and colour applications – properties of light – standard primaries and the chromaticity diagram – xyz colour model – CIE chromaticity diagram – RGB colour model – YIQ, CMY, HSV colour models, conversion between HSV and RGB models, HLS colour model, colour selection and applications. TEXT BOOK 1. Donald Hearn and Pauline Baker, “Computer Graphics”, Prentice Hall of India, 2001. Prepared by Dr.P.Sumathi COLOR MODELS Color Model is a method for explaining the properties or behavior of color within some particular context. No single color model can explain all aspects of color, so we make use of different models to help describe the different perceived characteristics of color. 15-1. PROPERTIES OF LIGHT Light is a narrow frequency band within the electromagnetic system. Other frequency bands within this spectrum are called radio waves, micro waves, infrared waves and x-rays. The below figure shows the frequency ranges for some of the electromagnetic bands. Each frequency value within the visible band corresponds to a distinct color. The electromagnetic spectrum is the range of frequencies (the spectrum) of electromagnetic radiation and their respective wavelengths and photon energies. Spectral colors range from the reds through orange and yellow at the low-frequency end to greens, blues and violet at the high end. Since light is an electromagnetic wave, the various colors are described in terms of either the frequency for the wave length λ of the wave. The wavelength and frequency of the monochromatic wave is inversely proportional to each other, with the proportionality constants as the speed of light C where C = λ f. -
CIE 1931 Color Space
CIE 1931 color space From Wikipedia, the free encyclopedia In the study of the perception of color, one of the first mathematically defined color spaces was the CIE 1931 XYZ color space, created by the International Commission on Illumination (CIE) in 1931. The CIE XYZ color space was derived from a series of experiments done in the late 1920s by W. David Wright and John Guild. Their experimental results were combined into the specification of the CIE RGB color space, from which the CIE XYZ color space was derived. Tristimulus values The human eye has photoreceptors (called cone cells) for medium- and high-brightness color vision, with sensitivity peaks in short (S, 420–440 nm), middle (M, 530–540 nm), and long (L, 560–580 nm) wavelengths (there is also the low-brightness monochromatic "night-vision" receptor, called rod cell, with peak sensitivity at 490-495 nm). Thus, in principle, three parameters describe a color sensation. The tristimulus values of a color are the amounts of three primary colors in a three-component additive color model needed to match that test color. The tristimulus values are most often given in the CIE 1931 color space, in which they are denoted X, Y, and Z. Any specific method for associating tristimulus values with each color is called a color space. CIE XYZ, one of many such spaces, is a commonly used standard, and serves as the basis from which many other color spaces are defined. The CIE standard observer In the CIE XYZ color space, the tristimulus values are not the S, M, and L responses of the human eye, but rather a set of tristimulus values called X, Y, and Z, which are roughly red, green and blue, respectively.