Appendix G: the Pantone “Our Color Wheel” Compared to the Chromaticity Diagram (2016) 1
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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. -
An Improved SPSIM Index for Image Quality Assessment
S S symmetry Article An Improved SPSIM Index for Image Quality Assessment Mariusz Frackiewicz * , Grzegorz Szolc and Henryk Palus Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland; [email protected] (G.S.); [email protected] (H.P.) * Correspondence: [email protected]; Tel.: +48-32-2371066 Abstract: Objective image quality assessment (IQA) measures are playing an increasingly important role in the evaluation of digital image quality. New IQA indices are expected to be strongly correlated with subjective observer evaluations expressed by Mean Opinion Score (MOS) or Difference Mean Opinion Score (DMOS). One such recently proposed index is the SuperPixel-based SIMilarity (SPSIM) index, which uses superpixel patches instead of a rectangular pixel grid. The authors of this paper have proposed three modifications to the SPSIM index. For this purpose, the color space used by SPSIM was changed and the way SPSIM determines similarity maps was modified using methods derived from an algorithm for computing the Mean Deviation Similarity Index (MDSI). The third modification was a combination of the first two. These three new quality indices were used in the assessment process. The experimental results obtained for many color images from five image databases demonstrated the advantages of the proposed SPSIM modifications. Keywords: image quality assessment; image databases; superpixels; color image; color space; image quality measures Citation: Frackiewicz, M.; Szolc, G.; Palus, H. An Improved SPSIM Index 1. Introduction for Image Quality Assessment. Quantitative domination of acquired color images over gray level images results in Symmetry 2021, 13, 518. https:// the development not only of color image processing methods but also of Image Quality doi.org/10.3390/sym13030518 Assessment (IQA) methods. -
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. -
Urban Land Grab Or Fair Urbanization?
Urban land grab or fair urbanization? Compulsory land acquisition and sustainable livelihoods in Hue, Vietnam Stedelijke landroof of eerlijke verstedelijking? Landonteigenlng en duurzaam levensonderhoud in Hue, Vietnam (met een samenvatting in het Nederlands) Chiếm đoạt đất đai đô thị hay đô thị hoá công bằng? Thu hồi đất đai cưỡng chế và sinh kế bền vững ở Huế, Việt Nam (với một phần tóm tắt bằng tiếng Việt) Proefschrift ter verkrijging van de graad van doctor aan de Universiteit Utrecht op gezag van de rector magnificus, prof.dr. G.J. van der Zwaan, ingevolge het besluit van het college voor promoties in het openbaar te verdedigen op maandag 21 december 2015 des middags te 12.45 uur door Nguyen Quang Phuc geboren op 10 december 1980 te Thua Thien Hue, Vietnam Promotor: Prof. dr. E.B. Zoomers Copromotor: Dr. A.C.M. van Westen This thesis was accomplished with financial support from Vietnam International Education Development (VIED), Ministry of Education and Training, and LANDac programme (the IS Academy on Land Governance for Equitable and Sustainable Development). ISBN 978-94-6301-026-9 Uitgeverij Eburon Postbus 2867 2601 CW Delft Tel.: 015-2131484 [email protected]/ www.eburon.nl Cover design and pictures: Nguyen Quang Phuc Cartography and design figures: Nguyen Quang Phuc © 2015 Nguyen Quang Phuc. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior permission in writing from the proprietor. © 2015 Nguyen Quang Phuc. -
Cielab Color Space
Gernot Hoffmann CIELab Color Space Contents . Introduction 2 2. Formulas 4 3. Primaries and Matrices 0 4. Gamut Restrictions and Tests 5. Inverse Gamma Correction 2 6. CIE L*=50 3 7. NTSC L*=50 4 8. sRGB L*=/0/.../90/99 5 9. AdobeRGB L*=0/.../90 26 0. ProPhotoRGB L*=0/.../90 35 . 3D Views 44 2. Linear and Standard Nonlinear CIELab 47 3. Human Gamut in CIELab 48 4. Low Chromaticity 49 5. sRGB L*=50 with RGB Numbers 50 6. PostScript Kernels 5 7. Mapping CIELab to xyY 56 8. Number of Different Colors 59 9. HLS-Hue for sRGB in CIELab 60 20. References 62 1.1 Introduction CIE XYZ is an absolute color space (not device dependent). Each visible color has non-negative coordinates X,Y,Z. CIE xyY, the horseshoe diagram as shown below, is a perspective projection of XYZ coordinates onto a plane xy. The luminance is missing. CIELab is a nonlinear transformation of XYZ into coordinates L*,a*,b*. The gamut for any RGB color system is a triangle in the CIE xyY chromaticity diagram, here shown for the CIE primaries, the NTSC primaries, the Rec.709 primaries (which are also valid for sRGB and therefore for many PC monitors) and the non-physical working space ProPhotoRGB. The white points are individually defined for the color spaces. The CIELab color space was intended for equal perceptual differences for equal chan- ges in the coordinates L*,a* and b*. Color differences deltaE are defined as Euclidian distances in CIELab. This document shows color charts in CIELab for several RGB color spaces. -
Expanded Gamut Shoot-Out: Real Systems, Real Results
Expanded Gamut Shoot-Out: Real Systems, Real Results Abhay Sharma Click toRyerson edit Master University, subtitle Toronto style Advisors Roger Breton, Marc Levine, John Seymour, Bill Pope Comprehensive Report – 450+ downloads tinyurl.com/ExpandedGamut Agenda – Expanded Gamut § Why do we need Expanded Gamut? § What is Expanded Gamut? (CMYK-OGV) § Use cases – Spot Colors vs Images PANTONE 109 C § Printing Spot Colors with Kodak Spotless (KSS) § Increased Accuracy § Using only 3 inks § Print all spot colors, without spot color inks § How do I implement EG? § Issues with Adobe and Pantone § Flexo testing in 2020 Vendors and Participants Software Solutions 1. Alwan – Toolbox, ColorHub 2. CGS ORIS – X GAMUT 3. ColorLogic – ColorAnt, CoPrA, ZePrA 4. GMG Color – OpenColor, ColorServer 5. Heidelberg – Prinect ColorToolbox 6. Kodak – Kodak Spotless Software, Prinergy PDF Editor § Hybrid Software - PACKZ (pronounced “packs”) RIP/DFE § efi Fiery XF (Command WorkStation) – Epson P9000 § SmartStream Production Pro – HP Indigo 7900 Color Management Solutions § X-Rite i1Profiler Expanded Gamut Tools § PANTONE Color Manager, Adobe Acrobat Pro, Adobe Photoshop Why do we need Expanded Gamut? - because imaging systems are imperfect Printing inks and dyes CMYK color gamut is small Color negative film What are the Use Cases for Expanded Gamut? ✓ 1. Spot Colors 2. Images PANTONE 301 C PANTONE 109 C Expanded gamut is most urgently needed in spot color reproduction for labels and package printing. Orange, Green, Violet - expands the colorspace Y G O C+Y M+Y -
Basic Color I
Basic Color I Up to this point we have explored and /or reviewed 4 several basic visual elements that will help develop a solid foundation for a Language of Painting. With a strong focus on the dynamics of paint application, we have connected simple dots with confident lines, con- figured lines into a wide array of shapes, and applied contrasting pigments in concert with varied pressures to yield seamless gradations of value. However, the previous chapter incorporated a new element that many find to be one of the most powerful tools for the creative endeavor-- color. The Painted Pressure Scale chapter, had us grow our palette from the sparse black and white that we have started with to include three colors: Red, Yellow and Blue. It is our hope that your experience generated a good number of questions from this inaugural color use. With this chapter we will take some first steps towards answering those questions. We will also revisit some of the color concepts you may already hold and introduce you to some of the more advanced methods for identifying, using and understanding this brilliant aspect of the artist’s salvo. On any initial investigation into color we are faced with a robust vocabulary of terms and concepts that may leave us somewhat confused. Like the many other aspects of our curriculum, we make a strong effort to simplify all of this. We will endeavor to make use of what you may have learned in the past and offer options for you to integrate color in the manner you wish into your creative process efficiently and effectively. -
Applying CIECAM02 for Mobile Display Viewing Conditions
Applying CIECAM02 for Mobile Display Viewing Conditions YungKyung Park*, ChangJun Li*, M. R. Luo*, Youngshin Kwak**, Du-Sik Park **, and Changyeong Kim**; * University of Leeds, Colour Imaging Lab, UK*, ** Samsung Advanced Institute of Technology, Yongin, South Korea** Abstract Small displays are widely used for mobile phones, PDA and 0.7 Portable DVD players. They are small to be carried around and 0.6 viewed under various surround conditions. An experiment was carried out to accumulate colour appearance data on a 2 inch 0.5 mobile phone display, a 4 inch PDA display and a 7 inch LCD 0.4 display using the magnitude estimation method. It was divided into v' 12 experimental phases according to four surround conditions 0.3 (dark, dim, average, and bright). The visual results in terms of 0.2 lightness, colourfulness, brightness and hue from different phases were used to test and refine the CIE colour appearance model, 0.1 CIECAM02 [1]. The refined model is based on continuous 0 functions to calculate different surround parameters for mobile 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 displays. There was a large improvement of the model u' performance, especially for bright surround condition. Figure 1. The colour gamut of the three displays studied. Introduction Many previous colour appearance studies were carried out using household TV or PC displays viewed under rather restricted viewing conditions. In practice, the colour appearance of mobile displays is affected by a variety of viewing conditions. First of all, the display size is much smaller than the other displays as it is built to be carried around easily. -
Medtronic Brand Color Chart
Medtronic Brand Color Chart Medtronic Visual Identity System: Color Ratios Navy Blue Medtronic Blue Cobalt Blue Charcoal Blue Gray Dark Gray Yellow Light Orange Gray Orange Medium Blue Sky Blue Light Blue Light Gray Pale Gray White Purple Green Turquoise Primary Blue Color Palette 70% Primary Neutral Color Palette 20% Accent Color Palette 10% The Medtronic Brand Color Palette was created for use in all material. Please use the CMYK, RGB, HEX, and LAB values as often as possible. If you are not able to use the LAB values, you can use the Pantone equivalents, but be aware the color output will vary from the other four color breakdowns. If you need a spot color, the preference is for you to use the LAB values. Primary Blue Color Palette Navy Blue Medtronic Blue C: 100 C: 99 R: 0 L: 15 R: 0 L: 31 M: 94 Web/HEX Pantone M: 74 Web/HEX Pantone G: 30 A: 2 G: 75 A: -2 Y: 47 #001E46 533 C Y: 17 #004B87 2154 C B: 70 B: -20 B: 135 B: -40 K: 43 K: 4 Cobalt Blue Medium Blue C: 81 C: 73 R: 0 L: 52 R: 0 L: 64 M: 35 Web/HEX Pantone M: 12 Web/HEX Pantone G: 133 A: -12 G: 169 A: -23 Y: 0 #0085CA 2382 C Y: 0 #00A9E0 2191 C B: 202 B: -45 B: 224 B: -39 K: 0 K : 0 Sky Blue Light Blue C: 55 C: 29 R: 113 L: 75 R: 185 L: 85 M: 4 Web/HEX Pantone M: 5 Web/HEX Pantone G: 197 A: -20 G: 217 A: -9 Y: 4 #71C5E8 297 C Y: 5 #B9D9EB 290 C B: 232 B: -26 B: 235 B: -13 K: 0 K: 0 Primary Neutral Color Palette Charcoal Gray Blue Gray C: 0 Pantone C: 68 R: 83 L: 36 R: 91 L: 51 M: 0 Web/HEX Cool M: 40 Web/HEX Pantone G: 86 A: 0 G: 127 A: -9 Y: 0 #53565a Gray Y: 28 #5B7F95 5415 -
Computational Color Harmony Based on Coloroid System
Computational Aesthetics in Graphics, Visualization and Imaging (2005) L. Neumann, M. Sbert, B. Gooch, W. Purgathofer (Editors) Computational Color Harmony based on Coloroid System László Neumanny, Antal Nemcsicsz, and Attila Neumannx yGrup de Gràfics de Girona, Universitat de Girona, and Institució Catalana de Recerca i Estudis Avançats, ICREA, Barcelona, Spain zBudapest University of Technology and Economics, Hungary xInstitute of Computer Graphics and Algorithms, Vienna University of Technology, Austria [email protected], [email protected], [email protected] (a) (b) Figure 1: (a) visualization of the overall appearance of a dichromatic color set with `caleidoscope' option of the Color Plan Designer software and (b) interactive color selection of a dichromatic color set in multi-layer mode, applying rotated regular grid. Abstract This paper presents experimentally based rules and methods for the creation of harmonic color sets. First, dichro- matic rules are presented which concern the harmony relationships of two hues. For an arbitrarily given hue pair, we define the just harmonic saturation values, resulting in minimally harmonic color pairs. These values express the fuzzy border between harmony and disharmony regions using a single scalar. Second, the value of harmony is defined corresponding to the contrast of lightness, i.e. the difference of perceptual lightness values. Third, we formulate the harmony value of the saturation contrast, depending on hue and lightness. The results of these investigations form a basis for a unified, coherent dichromatic harmony formula as well as for analysis of polychromatic color harmony. Introduced color harmony rules are based on Coloroid, which is one of the 5 6 main color-order systems and − furthermore it is an aesthetically uniform continuous color space. -
Development of a Methodology for Analyzing the Color Content of a Selected Group of Printed Color Analysis Systems
AN ABSTRACT OF THE THESIS OF Edith E. Collin for the degree of Master of Sciencein Clothing, Textiles and Related Arts presented on April 7, 1986. Title: Development of a Methodology for Analyzing theColor Content of a Selected Group of Printed Color Analysis Systems Redacted for Privacy Abstract approved: Ardis Koester The purpose of this study was to develop amethodology to compare the color choice recommendationsfor each personal color analysis category identified by the authorsof selected publications. The procedure used included: (1) identification of publications with color analysis systemsdirected toward female clientele; (2) comparison of number and names of categoriesused; (3) identification, by use of Munsell colornotations, the visual and written color recommendations ascribed toeach category; and (4) comparison of the publications on the basisof: (a) number and names of categories; (b) numberof color recommendations in each category; (c) range of hue value and chroma presented;(d) comparison of visual and written color recommendations by categoryand author. With the exception of comparison of publications onthe basis of written color recommendations, all components of themethodology were successful. Comparison of the publications used in development ofthe methodology revealed that: 1. The majority of authors use the seasonal category system. 2. The number of color recommendations per category was quite consistent within a publication but varied widely among authors. 3. There were few similarities in color recommendations even among authors using the same name categories. 4. There was poor agreement between written and visual color recommendations within all color categories. 5. There was no discernable theoretical basis for the color recommendations presented by any author included in this study. -
Color Space Conversion
L Technical Color Space Information Conversion The role of color space conversion in the process of scanning and reproduc- ing a color image begins the moment an image is captured by a scanner and continues through the point at which it is output on film. For the purpose of this article we will discuss conversions between three types of color systems: RGB, CIE, and CMYK. The RGB (Red, Green, & Blue) and CMYK (Cyan, Magenta, Yellow, & Black) color models have been described in greater detail in the Linotype-Hell Technical information piece entitled Color in Printing. For some background information on the CIE (Commission Internationale de l’Eclairage), please refer to Color Spaces and PostScript Level 2. CIE as a reference color space Most scanners acquire color in the form of RGB data. The majority of non- photographic printing methods employ CMYK inks or toners. In a closed sys- tem, where the characteristics of both the scanner and the printing method are well-defined, conversions may be made from RGB to CMYK through tables that maintain a reasonable level of color consistency. The process becomes somewhat more difficult as you add monitors, other scanners, proofing devices or printing processes that have different characteristics. It becomes critical to have a consistent yardstick, if you will, a means of con- verting between different color systems (and back again) without a loss in color fidelity. This is what CIE provides. Let’s start with some background on CIE, and then we will look at how it can be applied in an open system. Measuring color Light reflected off of, or transmitted through a colored object can be mea- sured by the wavelengths of light that are reflected or transmitted.