Colorfulness?:In Search of a Better Metric for Gamut Limit Colors
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A Guide to Colorimetry
A Guide to Colorimetry Sherwood Scientific Ltd 1 The Paddocks Cherry Hinton Road Cambridge CB1 8DH England www.sherwood-scientific.com Tel: +44 (0)1223 243444 Fax: +44 (0)1223 243300 Registered in England and Wales Registration Number 2329039 Reg. Office as above Group I II III IV V VI VII VIII Period 1A 8A 1 2 1 H 2A 3A 4A 5A 6A 7A He 1.008 4.003 3 4 5 6 7 8 9 10 2 Li Be B C N O F Ne 6.939 9.0122 10.811 12.011 14.007 15.999 18.998 20.183 11 12 13 14 15 16 17 18 3 Na Mg 3B 4B 5B 6B 7B [---------------8B-------------] 1B 2B Al Si P S Cl Ar 22.99 24.312 26.982 28.086 30.974 32.064 35.453 39.948 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 4 K Ca Sc Ti V Cr Mn Fe Co Ni Cu Zn Ga Ge As Se Br Kr 39.102 40.08 44.956 47.9 50.942 51.996 54.938 55.847 58.933 58.71 63.546 65.37 69.72 72.59 74.922 78.96 79.904 83.8 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 5 Rb Sr Y Zr Nb Mo Tc Ru Rh Pd Ag Cd In Sn Sb Te I Xe 85.47 87.52 88.905 91.22 92.906 95.94 [97] 101.07 102.91 106.4 107.87 112.4 114.82 118.69 121.75 127.6 126.9 131.3 55 56 57* 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 6 Cs Ba La Hf Ta W Re Os Ir Pt Au Hg Ti Pb Bi Po At Rn 132.91 137.34 138.91 178.49 180.95 183.85 186.2 190.2 192.2 195.09 196.97 200.59 204.37 207.19 208.98 209 210 222 87 88 89** 104 105 106 107 108 109 110 111 112 114 116 7 Fr Ra Ac Rt Db Sg Bh Hs Mt 215 226.03 227.03 [261] [262] [266] [264] [269] [268] [271] [272] [277] [289] [289] 58 59 60 61 62 63 64 65 66 67 68 69 70 71 * Lanthanides Ce Pr Nd Pm Sm Eu Gd Tb Dy Ho Er Tm Yb Lu 140.12 140.91 144.24 145 150.35 151.96 157.25 158.92 152.5 164.93 167.26 168.93 173.04 174.97 90 91 92 93 94 95 96 97 98 99 100 101 102 103 ** Actinides Th Pa U Np Pu Am Cm Bk Cf Es Fm Md No Lr 232.04 231 238 237.05 239.05 241.06 244.06 249.08 251 252.08 257.1 258.1 259.1 262.11 The ability to analyse and quantify colour in aqueous solutions and liquids using a colorimeter is something today’s analyst takes for granted. -
Colornet--Estimating Colorfulness in Natural Images
COLORNET - ESTIMATING COLORFULNESS IN NATURAL IMAGES Emin Zerman∗, Aakanksha Rana∗, Aljosa Smolic V-SENSE, School of Computer Science, Trinity College Dublin, Dublin, Ireland ABSTRACT learning-based objective metric ‘ColorNet’ for the estimation of colorfulness in natural images. Based on a convolutional neural Measuring the colorfulness of a natural or virtual scene is critical network (CNN), our proposed ColorNet is a two-stage color rating for many applications in image processing field ranging from captur- model, where at stage I, a feature network extracts the characteristics ing to display. In this paper, we propose the first deep learning-based features from the natural images and at stage II, a rating network colorfulness estimation metric. For this purpose, we develop a color estimates the colorfulness rating. To design our feature network, rating model which simultaneously learns to extracts the pertinent we explore the designs of the popular high-level CNN based fea- characteristic color features and the mapping from feature space to ture models such as VGG [22], ResNet [23], and MobileNet [24] the ideal colorfulness scores for a variety of natural colored images. architectures which we finally alter and tune for our colorfulness Additionally, we propose to overcome the lack of adequate annotated metric problem at hand. We also propose a rating network which dataset problem by combining/aligning two publicly available color- is simultaneously learned to estimate the relationship between the fulness databases using the results of a new subjective test which characteristic features and ideal colorfulness scores. employs a common subset of both databases. Using the obtained In this paper, we additionally overcome the challenge of the subjectively annotated dataset with 180 colored images, we finally absence of a well-annotated dataset for training and validating Col- demonstrate the efficacy of our proposed model over the traditional orNet model in a supervised manner. -
1 Human Color Vision
CAMC01 9/30/04 3:13 PM Page 1 1 Human Color Vision Color appearance models aim to extend basic colorimetry to the level of speci- fying the perceived color of stimuli in a wide variety of viewing conditions. To fully appreciate the formulation, implementation, and application of color appearance models, several fundamental topics in color science must first be understood. These are the topics of the first few chapters of this book. Since color appearance represents several of the dimensions of our visual experience, any system designed to predict correlates to these experiences must be based, to some degree, on the form and function of the human visual system. All of the color appearance models described in this book are derived with human visual function in mind. It becomes much simpler to understand the formulations of the various models if the basic anatomy, physiology, and performance of the visual system is understood. Thus, this book begins with a treatment of the human visual system. As necessitated by the limited scope available in a single chapter, this treatment of the visual system is an overview of the topics most important for an appreciation of color appearance modeling. The field of vision science is immense and fascinating. Readers are encouraged to explore the liter- ature and the many useful texts on human vision in order to gain further insight and details. Of particular note are the review paper on the mechan- isms of color vision by Lennie and D’Zmura (1988), the text on human color vision by Kaiser and Boynton (1996), the more general text on the founda- tions of vision by Wandell (1995), the comprehensive treatment by Palmer (1999), and edited collections on color vision by Backhaus et al. -
The Helmholtz-Kohlrausch Effect
Out of the Wood BY MIKE WOOD Lightness— The Helmholtz-Kohlrausch effect Don’t be put off by the strange name also strongly suggest looking at the online lightness most people see when looking at of this issue’s article. The Helmholtz- version of this article, as the images will be this image. Kohlrausch (HK) effect might sound displayed on your monitor and behave Note: Not everyone will see the differences esoteric, but it’s a human eye behavioral more like lights than the ink pigments in as strongly, particularly with a printed page effect with colored lighting that you are the printed copy. You can access Protocol like this. Just about everyone sees this effect undoubtedly already familiar with, if issues on-line at http://plasa.me/protocol with lights, but the strength of the effect varies perhaps not under its formal name. This or through the iPhone or iPad App at from individual to individual. In particular, effect refers to the human eye (or entoptic) http://plasa.me/protocolapp. if you are red-green color-blind, then you may phenomenon that colored light appears The simplest way to show the Helmholtz- see very little difference. brighter to us than white light of the same Kohlrausch effect is with an illustration. What I see—which will agree with what luminance. This is particularly relevant to The top half of Figure 1 shows seven most of you see—is that the red and pink the entertainment industry, as it is most differently colored patches against a grey patches look by far the brightest, while blue, obvious when using colored lights. -
RAL-Product-2019-SC-1.Pdf
INHALT / PRODUKTÜBERSICHT / RAL FARBEN PRODUCT OVERVIEW RAL COLOURS – Innovation and reliability. Worldwide. RAL FARBEN / PRODUKTÜBERSICHT / INHALT RAL CLASSIC THE WORLD‘S LEADING INDUSTRIAL COLOUR COLLECTION The RAL CLASSIC colour collection has for 90 years been indispensable in the clear communication of colours and a guarantee for obtaining exactly the same colours – worldwide. APPLICATION EXAMPLES Steel sculpture by world famous sculptor Anish Kapoor and star architect Cecil Balmond is London’s Olympic landmark. The ArcelorMittal Orbit glows in RAL 3003 Ruby Red. Allmilmö – a leading premium brand manufacturer of high-quality kitchen furnishings – produces these kitchen models in RAL 1023 traffic yellow. A design classic that is available in various colours. The picture shows a model in RAL 1004 Golden yellow. Emergency exit signs have the colour RAL 6002 Leaf green. Thonet produces the S 43 cantilever chair by Mart Stam in 11 RAL colours. RAL CLASSIC / PRODUCT OVERVIEW / RAL COLOURS RAL 840-HR Primary standards with 213 RAL CLASSIC colours Semi matt A5-sized 14.8 x 21.0 cm Colour illustration A6-sized 10.5 x 14.8 cm Binding colour samples for colour matching and quality control RAL 840-HR | 841-GL Including XYZ-values, colour distance from the original standard and reflectance curve Single cards available RAL 841-GL Primary standards with 196 RAL CLASSIC colours High gloss Allmilmö – a leading premium brand manufacturer of high-quality kitchen furnishings – A5-sized 14.8 x 21.0 cm produces these kitchen models in RAL 1023 traffic yellow. Colour illustration A6-sized 10.5 x 14.8 cm Binding colour samples for colour matching and quality control Including XYZ-values, colour distance from the original standard and reflectance curve Single cards available 07 Thonet produces the S 43 cantilever chair by Mart Stam in 11 RAL colours. -
Color Appearance Models Today's Topic
Color Appearance Models Arjun Satish Mitsunobu Sugimoto 1 Today's topic Color Appearance Models CIELAB The Nayatani et al. Model The Hunt Model The RLAB Model 2 1 Terminology recap Color Hue Brightness/Lightness Colorfulness/Chroma Saturation 3 Color Attribute of visual perception consisting of any combination of chromatic and achromatic content. Chromatic name Achromatic name others 4 2 Hue Attribute of a visual sensation according to which an area appears to be similar to one of the perceived colors Often refers red, green, blue, and yellow 5 Brightness Attribute of a visual sensation according to which an area appears to emit more or less light. Absolute level of the perception 6 3 Lightness The brightness of an area judged as a ratio to the brightness of a similarly illuminated area that appears to be white Relative amount of light reflected, or relative brightness normalized for changes in the illumination and view conditions 7 Colorfulness Attribute of a visual sensation according to which the perceived color of an area appears to be more or less chromatic 8 4 Chroma Colorfulness of an area judged as a ratio of the brightness of a similarly illuminated area that appears white Relationship between colorfulness and chroma is similar to relationship between brightness and lightness 9 Saturation Colorfulness of an area judged as a ratio to its brightness Chroma – ratio to white Saturation – ratio to its brightness 10 5 Definition of Color Appearance Model so much description of color such as: wavelength, cone response, tristimulus values, chromaticity coordinates, color spaces, … it is difficult to distinguish them correctly We need a model which makes them straightforward 11 Definition of Color Appearance Model CIE Technical Committee 1-34 (TC1-34) (Comission Internationale de l'Eclairage) They agreed on the following definition: A color appearance model is any model that includes predictors of at least the relative color-appearance attributes of lightness, chroma, and hue. -
Calculating Correlated Color Temperatures Across the Entire Gamut of Daylight and Skylight Chromaticities
Calculating correlated color temperatures across the entire gamut of daylight and skylight chromaticities Javier Herna´ ndez-Andre´ s, Raymond L. Lee, Jr., and Javier Romero Natural outdoor illumination daily undergoes large changes in its correlated color temperature ͑CCT͒, yet existing equations for calculating CCT from chromaticity coordinates span only part of this range. To improve both the gamut and accuracy of these CCT calculations, we use chromaticities calculated from our measurements of nearly 7000 daylight and skylight spectra to test an equation that accurately maps CIE 1931 chromaticities x and y into CCT. We extend the work of McCamy ͓Color Res. Appl. 12, 285–287 ͑1992͔͒ by using a chromaticity epicenter for CCT and the inverse slope of the line that connects it to x and y. With two epicenters for different CCT ranges, our simple equation is accurate across wide chromaticity and CCT ranges ͑3000–106 K͒ spanned by daylight and skylight. © 1999 Optical Society of America OCIS codes: 010.1290, 330.1710, 330.1730. 1. Introduction term correlated color temperature ͑CCT͒ instead of A colorimetric landmark often included in the Com- color temperature to describe its appearance. Sup- mission Internationale de l’Eclairage ͑CIE͒ 1931 pose that x1, y1 is the chromaticity of such an off-locus chromaticity diagram is the locus of chromaticity co- light source. By definition, the CCT of x1, y1 is the ordinates defined by blackbody radiators ͑see Fig. 1, temperature of the Planckian radiator whose chro- inset͒. One can calculate this Planckian ͑or black- maticity is nearest to x1, y1. The colorimetric body͒ locus by colorimetrically integrating the Planck minimum-distance calculations that determine CCT function at many different temperatures, with each must be done within the color space of the CIE 1960 temperature specifying a unique pair of 1931 x, y uniformity chromaticity scale ͑UCS͒ diagram. -
Colornet - Estimating Colorfulness in Natural Images
COLORNET - ESTIMATING COLORFULNESS IN NATURAL IMAGES Emin Zerman∗, Aakanksha Rana∗, Aljosa Smolic V-SENSE, School of Computer Science, Trinity College Dublin, Dublin, Ireland ABSTRACT learning-based objective metric ‘ColorNet’ for the estimation of colorfulness in natural images. Based on a convolutional neural Measuring the colorfulness of a natural or virtual scene is critical network (CNN), our proposed ColorNet is a two-stage color rating for many applications in image processing field ranging from captur- model, where at stage I, a feature network extracts the characteristics ing to display. In this paper, we propose the first deep learning-based features from the natural images and at stage II, a rating network colorfulness estimation metric. For this purpose, we develop a color estimates the colorfulness rating. To design our feature network, rating model which simultaneously learns to extracts the pertinent we explore the designs of the popular high-level CNN based fea- characteristic color features and the mapping from feature space to ture models such as VGG [22], ResNet [23], and MobileNet [24] the ideal colorfulness scores for a variety of natural colored images. architectures which we finally alter and tune for our colorfulness Additionally, we propose to overcome the lack of adequate annotated metric problem at hand. We also propose a rating network which dataset problem by combining/aligning two publicly available color- is simultaneously learned to estimate the relationship between the fulness databases using the results of a new subjective test which characteristic features and ideal colorfulness scores. employs a common subset of both databases. Using the obtained In this paper, we additionally overcome the challenge of the subjectively annotated dataset with 180 colored images, we finally absence of a well-annotated dataset for training and validating Col- demonstrate the efficacy of our proposed model over the traditional orNet model in a supervised manner. -
Graphic Standard Guidelinesview
Maricopa County Graphic Standard Guidelines basic standards The updated Maricopa County seal is the basic building block of our new visual image. It is a symbol of many things our County represents. The goal is to establish an image that is credible, “ownable” and that with proper use will promote the County as a well-integrated organization. This graphic standards manual was prepared to ensure that we speak to all with a common “voice,” projecting a distinctive and relevant image of Maricopa County, while allowing the necessary flexibility for individual departmental messages. These guidelines provide an objective set of boundaries to ensure consistent quality in the application of the seal and safeguard against potential problems that could dilute efforts to build the Maricopa County identity. addendum: typefaces Garamond (replaces Minion Regular) abcdefghijklmnopqrstuvwxyz 0123456789 ABCDEFGHIJKLMNOPQRSTUVWXYZ Garamond Italic (replaces Minion Italic) abcdefghijklmnopqrstuvwxyz 0123456789 ABCDEFGHIJKLMNOPQRSTUVWXYZ Garamond Bold (replaces Minion Semibold and Bold) abcdefghijklmnopqrstuvwxyz 0123456789 ABCDEFGHIJKLMNOPQRSTUVWXYZ Note: Do not artificially italicize Garamond Bold. Microsoft Garamond has only three faces included in its set (roman, italic and bold). This face is licensed from the AGFA/Monotype corporation. An additional two weights (Monotype Alternate Italic and Bold Italic) are available from the AGFA/Monotype web site (www.fonts.com). Please check with your department before purchasing. Tahoma (replaces Avenir Book) abcdefghijklmnopqrstuvwxyz 0123456789 ABCDEFGHIJKLMNOPQRSTUVWXYZ Tahoma Bold (replaces Avenir Medium and Heavy) abcdefghijklmnopqrstuvwxyz 0123456789 ABCDEFGHIJKLMNOPQRSTUVWXYZ Note: Tahoma does not have italic faces in its family. Do not artificially italicize this face. a.1 typeface update:It has come to the attention of the Public Information Office that the typefaces specified for use on Maricopa County materials are not widely available throughout the County computer network, and are cost prohibitive to purchase. -
Nondestructive Colorimetric Method to Determine the Oxygen Diffusion Rate Through Closures Used in Winemaking
J. Agric. Food Chem. 2005, 53, 6967−6973 6967 Nondestructive Colorimetric Method To Determine the Oxygen Diffusion Rate through Closures Used in Winemaking PAULO LOPES,CEÄ DRIC SAUCIER,* AND YVES GLORIES Faculte´ d’Oenologie de Bordeaux, Universite´ Victor Segalen Bordeaux 2 UMR 1219 INRA, 351 Cours de la libe´ration, 33405 Talence Cedex, France Oxygen is one of the most important factors determining the aging potential of bottled wine, and oxygen diffusion into bottled wine is extremely dependent on the sealing effectiveness of the closure. A nondestructive colorimetric method was developed to measure oxygen diffusion from 1 to 9.8 mg/L during the postbottling period. This method was used to study oxygen diffusion through different closures available on the market. After 365 days of horizontal storage, only the control bottle was impermeable to atmospheric oxygen; all other closures exhibited variable rates of oxygen diffusion, which were much greater in the first month than in the following months. It was shown that the rate of diffusion was clearly influenced by the type of closure material used. KEYWORDS: Indigo carmine; sodium dithionite; oxygen diffusion; closures; bottles INTRODUCTION of total oxygen in bottled beer using an oximeter, based on The contact between wine and oxygen is critical for wine- Henry’s law (16), and measurement of gas composition in the making and bottle-aging processes. Some degree of oxygenation headspace of a bottle by gas chromatography (9, 17) or even may be beneficial for red wine development as the oxidative for white wines by simple absorbance measurement at 420 nm phenolic reactions will lead to color enhancement and the (10). -
Chroma and Hue Variation in Color Images of Natural Scenes
Naturalness and Image Quality: Chroma and Hue Variation in Color Images of Natural Scenes Huib de Ridder and Frans J.J. Blommaert Institute for Perception Research, Eindhoven, The Netherlands; Elena A. Fedorovskaya, Department of Psychophysiology, Moscow State University, Mokhovaya street 8, 103009 Moscow, Russia Abstract these parameters (e.g. blur, periodic structure, noise) it proved possible to derive explicit expressions for their The relation between perceptual image quality and natural- relation with the corresponding image attributes.3,4 ness was investigated by varying the colorfulness and hue Scaling experiments using multiply impaired images of color images of natural scenes. These variations were have shown that image attributes can be represented by a set created by digitizing the images, subsequently determining of orthogonal vectors in a Euclidean space.3,5-8 Accord- their color point distributions in the CIELUV color space ingly, the attributes can be said to be the orthogonal dimen- and finally multiplying either the chroma value or the hue- sions of a multidimensional psychological space underlying angle of each pixel by a constant. During the chroma/hue- image quality. The sensorial image is represented in this angle transformation the lightness and hue-angle/chroma space by a point with the perceived strengths of the at- value of each pixel were kept constant. Ten subjects rated tributes as coordinates. Image quality has sometimes been quality and naturalness on numerical scales. The results identified as a direction in this space, with the angle to a show that both quality and naturalness deteriorate as soon as dimension indicating how relevant that attribute is for the hues start to deviate from the ones in the original image. -
Appendix G: the Pantone “Our Color Wheel” Compared to the Chromaticity Diagram (2016) 1
Appendix G - 1 Appendix G: The Pantone “Our color Wheel” compared to the Chromaticity Diagram (2016) 1 There is considerable interest in the conversion of Pantone identified color numbers to other numbers within the CIE and ISO Standards. Unfortunately, most of these Standards are not based on any theoretical foundation and have evolved since the late 1920's based on empirical relationships agreed to by committees. As a general rule, these Standards have all assumed that Grassman’s Law of linearity in the visual realm. Unfortunately, this fundamental assumption is not appropriate and has never been confirmed. The visual system of all biological neural systems rely upon logarithmic summing and differencing. A particular goal has been to define precisely the border between colors occurring in the local language and vernacular. An example is the border between yellow and orange. Because of the logarithmic summations used in the neural circuits of the eye and the positions of perceived yellow and orange relative to the photoreceptors of the eye, defining the transition wavelength between these two colors is particularly acute.The perceived response is particularly sensitive to stimulus intensity in the spectral region from 560 to about 580 nanometers. This Appendix relies upon the Chromaticity Diagram (2016) developed within this work. It has previously been described as The New Chromaticity Diagram, or the New Chromaticity Diagram of Research. It is in fact a foundation document that is theoretically supportable and in turn supports a wide variety of less well founded Hering, Munsell, and various RGB and CMYK representations of the human visual spectrum.