COLOR APPEARANCE MODELS, 2Nd Ed. Table of Contents
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Color Models
Color Models Jian Huang CS456 Main Color Spaces • CIE XYZ, xyY • RGB, CMYK • HSV (Munsell, HSL, IHS) • Lab, UVW, YUV, YCrCb, Luv, Differences in Color Spaces • What is the use? For display, editing, computation, compression, …? • Several key (very often conflicting) features may be sought after: – Additive (RGB) or subtractive (CMYK) – Separation of luminance and chromaticity – Equal distance between colors are equally perceivable CIE Standard • CIE: International Commission on Illumination (Comission Internationale de l’Eclairage). • Human perception based standard (1931), established with color matching experiment • Standard observer: a composite of a group of 15 to 20 people CIE Experiment CIE Experiment Result • Three pure light source: R = 700 nm, G = 546 nm, B = 436 nm. CIE Color Space • 3 hypothetical light sources, X, Y, and Z, which yield positive matching curves • Y: roughly corresponds to luminous efficiency characteristic of human eye CIE Color Space CIE xyY Space • Irregular 3D volume shape is difficult to understand • Chromaticity diagram (the same color of the varying intensity, Y, should all end up at the same point) Color Gamut • The range of color representation of a display device RGB (monitors) • The de facto standard The RGB Cube • RGB color space is perceptually non-linear • RGB space is a subset of the colors human can perceive • Con: what is ‘bloody red’ in RGB? CMY(K): printing • Cyan, Magenta, Yellow (Black) – CMY(K) • A subtractive color model dye color absorbs reflects cyan red blue and green magenta green blue and red yellow blue red and green black all none RGB and CMY • Converting between RGB and CMY RGB and CMY HSV • This color model is based on polar coordinates, not Cartesian coordinates. -
When Red Lights Look Yellow
Publications 11-2005 When Red Lights Look Yellow Joanne M. Wood Queensland University of Technology David A. Atchison Queensland University of Technology Alex Chaparro Wichita State University, [email protected] Follow this and additional works at: https://commons.erau.edu/publication Part of the Cognition and Perception Commons, Musculoskeletal, Neural, and Ocular Physiology Commons, and the Ophthalmology Commons Scholarly Commons Citation Wood, J. M., Atchison, D. A., & Chaparro, A. (2005). When Red Lights Look Yellow. Investigative Ophthalmology & Visual Science, 46(11). https://doi.org/10.1167/iovs.04-1513 This Article is brought to you for free and open access by Scholarly Commons. It has been accepted for inclusion in Publications by an authorized administrator of Scholarly Commons. For more information, please contact [email protected]. When Red Lights Look Yellow Joanne M. Wood,1 David A. Atchison,1 and Alex Chaparro2 PURPOSE. Red signals are typically used to signify danger. This observer’s distance spectacle prescription. It did not occur study was conducted to investigate a situation identified by with lens powers in excess of ϩ1.00 D, as the signals became train drivers in which red signals appear yellow when viewed too blurred for the viewer to distinguish the color. A compre- at long distances (ϳ900 m) through progressive-addition hensive eye and vision examination of the train driver who had lenses. originally reported the color misperception revealed that his METHODS. A laboratory study was conducted to investigate the corrected vision was normal. The train driver was also shown effects of defocus, target size, ambient illumination, and sur- to have normal color vision, as assessed by the Ishihara, Farns- round characteristics on the extent of the color misperception worth Lantern, and Farnsworth D15 tests. -
COLOR SPACE MODELS for VIDEO and CHROMA SUBSAMPLING
COLOR SPACE MODELS for VIDEO and CHROMA SUBSAMPLING Color space A color model is an abstract mathematical model describing the way colors can be represented as tuples of numbers, typically as three or four values or color components (e.g. RGB and CMYK are color models). However, a color model with no associated mapping function to an absolute color space is a more or less arbitrary color system with little connection to the requirements of any given application. Adding a certain mapping function between the color model and a certain reference color space results in a definite "footprint" within the reference color space. This "footprint" is known as a gamut, and, in combination with the color model, defines a new color space. For example, Adobe RGB and sRGB are two different absolute color spaces, both based on the RGB model. In the most generic sense of the definition above, color spaces can be defined without the use of a color model. These spaces, such as Pantone, are in effect a given set of names or numbers which are defined by the existence of a corresponding set of physical color swatches. This article focuses on the mathematical model concept. Understanding the concept Most people have heard that a wide range of colors can be created by the primary colors red, blue, and yellow, if working with paints. Those colors then define a color space. We can specify the amount of red color as the X axis, the amount of blue as the Y axis, and the amount of yellow as the Z axis, giving us a three-dimensional space, wherein every possible color has a unique position. -
Color Difference Delta E - a Survey
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/236023905 Color difference Delta E - A survey Article in Machine Graphics and Vision · April 2011 CITATIONS READS 12 8,785 2 authors: Wojciech Mokrzycki Maciej Tatol Cardinal Stefan Wyszynski University in Warsaw University of Warmia and Mazury in Olsztyn 157 PUBLICATIONS 177 CITATIONS 5 PUBLICATIONS 27 CITATIONS SEE PROFILE SEE PROFILE All content following this page was uploaded by Wojciech Mokrzycki on 08 August 2017. The user has requested enhancement of the downloaded file. Colour difference ∆E - A survey Mokrzycki W.S., Tatol M. {mokrzycki,mtatol}@matman.uwm.edu.pl Faculty of Mathematics and Informatics University of Warmia and Mazury, Sloneczna 54, Olsztyn, Poland Preprint submitted to Machine Graphic & Vision, 08:10:2012 1 Contents 1. Introduction 4 2. The concept of color difference and its tolerance 4 2.1. Determinants of color perception . 4 2.2. Difference in color and tolerance for color of product . 5 3. An early period in ∆E formalization 6 3.1. JND units and the ∆EDN formula . 6 3.2. Judd NBS units, Judd ∆EJ and Judd-Hunter ∆ENBS formulas . 6 3.3. Adams chromatic valence color space and the ∆EA formula . 6 3.4. MacAdam ellipses and the ∆EFMCII formula . 8 4. The ANLab model and ∆E formulas 10 4.1. The ANLab model . 10 4.2. The ∆EAN formula . 10 4.3. McLaren ∆EMcL and McDonald ∆EJPC79 formulas . 10 4.4. The Hunter color system and the ∆EH formula . 11 5. ∆E formulas in uniform color spaces 11 5.1. -
Several Color Appearance Phenomena in Color Reproduction
2nd International Conference on Electronic & Mechanical Engineering and Information Technology (EMEIT-2012) Several Color Appearance Phenomena in Color Reproduction Qin-ling Dai1,a, Xiao-zhou Li2*,b, Ai Xu2 1 School of Materials Engineering (Southwest Forestry University), Kunming, China 650224 2 Key Laboratory of Pulp & Paper Science and Technology (Shandong Polytechnic University), Ministry of Education, Ji’nan, China, 250353 [email protected], bcorresponding author: [email protected] Keywords: color reproduction, color appearance, color appearance phenomena Abstract. Color perceived performance was influenced by various color appearance phenomena caused by varying viewing conditions in color reproduction process. It is necessary to do some research on the color appearance phenomena to represent the color appearance models qualitatively and quantitatively and accurate color reproduction easily. Only the phenomena were studied thoroughly, could the color transmission and reproduction be well performed. The color appearance and common color appearance phenomena of color reproduction were analyzed in this paper. And the basic theory of color appearance in color reproduction was also studied. Introduction High fidelity digital printing plays an important role in high fidelity color transmission and reproduction and it is one of the most important techniques to perform high fidelity color reproduction. High fidelity digital printing helps to perform accurate color reproduction of the original which can’t be performed because of paper and ink in traditional printing process [1]. In color printing, the color difference caused by paper, ink and viewing conditions is various. The difference is not only colorimetric difference but also different color appearance phenomena. While the different color appearance phenomenon is the leading factor to influence the color vision perceived. -
Prediction of Munsell Appearance Scales Using Various Color-Appearance Models
Prediction of Munsell Appearance Scales Using Various Color- Appearance Models David R. Wyble,* Mark D. Fairchild Munsell Color Science Laboratory, Rochester Institute of Technology, 54 Lomb Memorial Dr., Rochester, New York 14623-5605 Received 1 April 1999; accepted 10 July 1999 Abstract: The chromaticities of the Munsell Renotation predict the color of the objects accurately in these examples, Dataset were applied to eight color-appearance models. a color-appearance model is required. Modern color-appear- Models used were: CIELAB, Hunt, Nayatani, RLAB, LLAB, ance models should, therefore, be able to account for CIECAM97s, ZLAB, and IPT. Models were used to predict changes in illumination, surround, observer state of adapta- three appearance correlates of lightness, chroma, and hue. tion, and, in some cases, media changes. This definition is Model output of these appearance correlates were evalu- slightly relaxed for the purposes of this article, so simpler ated for their uniformity, in light of the constant perceptual models such as CIELAB can be included in the analysis. nature of the Munsell Renotation data. Some background is This study compares several modern color-appearance provided on the experimental derivation of the Renotation models with respect to their ability to predict uniformly the Data, including the specific tasks performed by observers to dimensions (appearance scales) of the Munsell Renotation evaluate a sample hue leaf for chroma uniformity. No par- Data,1 hereafter referred to as the Munsell data. Input to all ticular model excelled at all metrics. In general, as might be models is the chromaticities of the Munsell data, and is expected, models derived from the Munsell System per- more fully described below. -
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. -
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. -
Accurately Reproducing Pantone Colors on Digital Presses
Accurately Reproducing Pantone Colors on Digital Presses By Anne Howard Graphic Communication Department College of Liberal Arts California Polytechnic State University June 2012 Abstract Anne Howard Graphic Communication Department, June 2012 Advisor: Dr. Xiaoying Rong The purpose of this study was to find out how accurately digital presses reproduce Pantone spot colors. The Pantone Matching System is a printing industry standard for spot colors. Because digital printing is becoming more popular, this study was intended to help designers decide on whether they should print Pantone colors on digital presses and expect to see similar colors on paper as they do on a computer monitor. This study investigated how a Xerox DocuColor 2060, Ricoh Pro C900s, and a Konica Minolta bizhub Press C8000 with default settings could print 45 Pantone colors from the Uncoated Solid color book with only the use of cyan, magenta, yellow and black toner. After creating a profile with a GRACoL target sheet, the 45 colors were printed again, measured and compared to the original Pantone Swatch book. Results from this study showed that the profile helped correct the DocuColor color output, however, the Konica Minolta and Ricoh color outputs generally produced the same as they did without the profile. The Konica Minolta and Ricoh have much newer versions of the EFI Fiery RIPs than the DocuColor so they are more likely to interpret Pantone colors the same way as when a profile is used. If printers are using newer presses, they should expect to see consistent color output of Pantone colors with or without profiles when using default settings. -
Illumination and Distance
PHYS 1400: Physical Science Laboratory Manual ILLUMINATION AND DISTANCE INTRODUCTION How bright is that light? You know, from experience, that a 100W light bulb is brighter than a 60W bulb. The wattage measures the energy used by the bulb, which depends on the bulb, not on where the person observing it is located. But you also know that how bright the light looks does depend on how far away it is. That 100W bulb is still emitting the same amount of energy every second, but if you are farther away from it, the energy is spread out over a greater area. You receive less energy, and perceive the light as less bright. But because the light energy is spread out over an area, it’s not a linear relationship. When you double the distance, the energy is spread out over four times as much area. If you triple the distance, the area is nine Twice the distance, ¼ as bright. Triple the distance? 11% as bright. times as great, meaning that you receive only 1/9 (or 11%) as much energy from the light source. To quantify the amount of light, we will use units called lux. The idea is simple: energy emitted per second (Watts), spread out over an area (square meters). However, a lux is not a W/m2! A lux is a lumen per m2. So, what is a lumen? Technically, it’s one candela emitted uniformly across a solid angle of 1 steradian. That’s not helping, is it? Examine the figure above. The source emits light (energy) in all directions simultaneously. -
Tabla De Conversión Pantone a NCS (Natural Color System)
Tabla de conversión Pantone a NCS (Natural Color System) PANTONE NCS (más parecido) PANTONE NCS (más parecido) Pantone Yellow C NCS 0580-Y Pantone 3985C NCS 3060-G80Y Pantone Yellow U NCS 0580-Y Pantone 3985U NCS 4040-G80Y Pantone Warm Red C NCS 0580-Y70R Pantone 3995C NCS 5040-G80Y Pantone Warm Red U NCS 0580-Y70R Pantone 3995U NCS 6020-G70Y Pantone Rubine Red C NCS 1575-R10B Pantone 400C NCS 2005-Y50R Pantone Rubine Red U NCS 1070-R20B Pantone 400U NCS 2502-R Pantone Rhodamine Red C Pantone 401C NCS 2005-Y50R Pantone Rhodamine Red U NCS 1070-R20B Pantone 401U NCS 2502-R Pantone Purple C Pantone 402C NCS 4005-Y50R Pantone Purple U NCS 2060-R40B Pantone 402U NCS 3502-R Pantone Violet C Pantone 403C NCS 4055-Y50R Pantone Violet U NCS 3050-R60B Pantone 403U NCS 4502-R Pantone Reflex Blue C NCS 3560-R80B Pantone 404C NCS 6005-Y20R Pantone Reflex Blue U NCS 3060-R70B Pantone 404U NCS 5502-R Pantone Process Blue C NCS 2065-B Pantone 405C NCS 7005-Y20R Pantone Process Blue U NCS 1565-B Pantone 405U NCS 6502-R Pantone Green C NCS 2060-B90G Pantone 406C NCS 2005-Y50R Pantone Green U NCS 2060-B90G Pantone 406U NCS 2005-Y50R Pantone Black C NCS 8005-Y20R Pantone 407C NCS 3005-Y50R Pantone Black U NCS 7502-Y Pantone 407U NCS 3005-Y80R Pantone Yellow 012C NCS 0580-Y Pantone 408C NCS 3005-Y50R Pantone Yellow 012U NCS 0580-Y Pantone 408U NCS 4005-Y80R Pantone Orange 021C NCS 0585-Y60R Pantone 409C NCS 5005-Y50R Pantone Orange 021U NCS 0580-Y60R Pantone 409U NCS 5005-Y80R Pantone Red 032C NCS 0580-Y90R Pantone 410C NCS 5005-Y50R Pantone Red 032U NCS -
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.