Color and Appearance Standardization
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Understanding Color and Gamut Poster
Understanding Colors and Gamut www.tektronix.com/video Contact Tektronix: ASEAN / Australasia (65) 6356 3900 Austria* 00800 2255 4835 Understanding High Balkans, Israel, South Africa and other ISE Countries +41 52 675 3777 Definition Video Poster Belgium* 00800 2255 4835 Brazil +55 (11) 3759 7627 This poster provides graphical Canada 1 (800) 833-9200 reference to understanding Central East Europe and the Baltics +41 52 675 3777 high definition video. Central Europe & Greece +41 52 675 3777 Denmark +45 80 88 1401 Finland +41 52 675 3777 France* 00800 2255 4835 To order your free copy of this poster, please visit: Germany* 00800 2255 4835 www.tek.com/poster/understanding-hd-and-3g-sdi-video-poster Hong Kong 400-820-5835 India 000-800-650-1835 Italy* 00800 2255 4835 Japan 81 (3) 6714-3010 Luxembourg +41 52 675 3777 MPEG-2 Transport Stream Advanced Television Systems Committee (ATSC) Mexico, Central/South America & Caribbean 52 (55) 56 04 50 90 ISO/IEC 13818-1 International Standard Program and System Information Protocol (PSIP) for Terrestrial Broadcast and cable (Doc. A//65B and A/69) System Time Table (STT) Rating Region Table (RRT) Direct Channel Change Table (DCCT) ISO/IEC 13818-2 Video Levels and Profiles MPEG Poster ISO/IEC 13818-1 Transport Packet PES PACKET SYNTAX DIAGRAM 24 bits 8 bits 16 bits Syntax Bits Format Syntax Bits Format Syntax Bits Format 4:2:0 4:2:2 4:2:0, 4:2:2 1920x1152 1920x1088 1920x1152 Packet PES Optional system_time_table_section(){ rating_region_table_section(){ directed_channel_change_table_section(){ High Syntax -
Creating 4K/UHD Content Poster
Creating 4K/UHD Content Colorimetry Image Format / SMPTE Standards Figure A2. Using a Table B1: SMPTE Standards The television color specification is based on standards defined by the CIE (Commission 100% color bar signal Square Division separates the image into quad links for distribution. to show conversion Internationale de L’Éclairage) in 1931. The CIE specified an idealized set of primary XYZ SMPTE Standards of RGB levels from UHDTV 1: 3840x2160 (4x1920x1080) tristimulus values. This set is a group of all-positive values converted from R’G’B’ where 700 mv (100%) to ST 125 SDTV Component Video Signal Coding for 4:4:4 and 4:2:2 for 13.5 MHz and 18 MHz Systems 0mv (0%) for each ST 240 Television – 1125-Line High-Definition Production Systems – Signal Parameters Y is proportional to the luminance of the additive mix. This specification is used as the color component with a color bar split ST 259 Television – SDTV Digital Signal/Data – Serial Digital Interface basis for color within 4K/UHDTV1 that supports both ITU-R BT.709 and BT2020. 2020 field BT.2020 and ST 272 Television – Formatting AES/EBU Audio and Auxiliary Data into Digital Video Ancillary Data Space BT.709 test signal. ST 274 Television – 1920 x 1080 Image Sample Structure, Digital Representation and Digital Timing Reference Sequences for The WFM8300 was Table A1: Illuminant (Ill.) Value Multiple Picture Rates 709 configured for Source X / Y BT.709 colorimetry ST 296 1280 x 720 Progressive Image 4:2:2 and 4:4:4 Sample Structure – Analog & Digital Representation & Analog Interface as shown in the video ST 299-0/1/2 24-Bit Digital Audio Format for SMPTE Bit-Serial Interfaces at 1.5 Gb/s and 3 Gb/s – Document Suite Illuminant A: Tungsten Filament Lamp, 2854°K x = 0.4476 y = 0.4075 session display. -
Spectral Primary Decomposition for Rendering with RGB Reflectance
Eurographics Symposium on Rendering (DL-only Track) (2019) T. Boubekeur and P. Sen (Editors) Spectral Primary Decomposition for Rendering with sRGB Reflectance Ian Mallett1 and Cem Yuksel1 1University of Utah Ground Truth Our Method Meng et al. 2015 D65 Environment 35 Error (Noise & Imprecision) Error (Color Distortion) E D CIE76 0:0 Lambertian Plane Figure 1: Spectral rendering of a texture containing the entire sRGB gamut as the Lambertian albedo for a plane under a D65 environment. In this configuration, ideally, rendered sRGB pixels should match the texture’s values. Prior work by Meng et al. [MSHD15] produces noticeable color distortion, whereas our method produces no error beyond numerical precision and Monte Carlo sampling noise (the magnitude of the DE induced by this noise varies with the image because sRGB is perceptually nonlinear). Contemporary work [JH19] is also nearly able to achieve this, but at a significant implementation and memory cost. Abstract Spectral renderers, as-compared to RGB renderers, are able to simulate light transport that is closer to reality, capturing light behavior that is impossible to simulate with any three-primary decomposition. However, spectral rendering requires spectral scene data (e.g. textures and material properties), which is not widely available, severely limiting the practicality of spectral rendering. Unfortunately, producing a physically valid reflectance spectrum from a given sRGB triple has been a challenging problem, and indeed until very recently constructing a spectrum without colorimetric round-trip error was thought to be impos- sible. In this paper, we introduce a new procedure for efficiently generating a reflectance spectrum from any given sRGB input data. -
Chromatic Adaptation Transform by Spectral Reconstruction Scott A
Chromatic Adaptation Transform by Spectral Reconstruction Scott A. Burns, University of Illinois at Urbana-Champaign, [email protected] February 28, 2019 Note to readers: This version of the paper is a preprint of a paper to appear in Color Research and Application in October 2019 (Citation: Burns SA. Chromatic adaptation transform by spectral reconstruction. Color Res Appl. 2019;44(5):682-693). The full text of the final version is available courtesy of Wiley Content Sharing initiative at: https://rdcu.be/bEZbD. The final published version differs substantially from the preprint shown here, as follows. The claims of negative tristimulus values being “failures” of a CAT are removed, since in some circumstances such as with “supersaturated” colors, it may be reasonable for a CAT to produce such results. The revised version simply states that in certain applications, tristimulus values outside the spectral locus or having negative values are undesirable. In these cases, the proposed method will guarantee that the destination colors will always be within the spectral locus. Abstract: A color appearance model (CAM) is an advanced colorimetric tool used to predict color appearance under a wide variety of viewing conditions. A chromatic adaptation transform (CAT) is an integral part of a CAM. Its role is to predict “corresponding colors,” that is, a pair of colors that have the same color appearance when viewed under different illuminants, after partial or full adaptation to each illuminant. Modern CATs perform well when applied to a limited range of illuminant pairs and a limited range of source (test) colors. However, they can fail if operated outside these ranges. -
ARC Laboratory Handbook. Vol. 5 Colour: Specification and Measurement
Andrea Urland CONSERVATION OF ARCHITECTURAL HERITAGE, OFARCHITECTURALHERITAGE, CONSERVATION Colour Specification andmeasurement HISTORIC STRUCTURESANDMATERIALS UNESCO ICCROM WHC VOLUME ARC 5 /99 LABORATCOROY HLANODBOUOKR The ICCROM ARC Laboratory Handbook is intended to assist professionals working in the field of conserva- tion of architectural heritage and historic structures. It has been prepared mainly for architects and engineers, but may also be relevant for conservator-restorers or archaeologists. It aims to: - offer an overview of each problem area combined with laboratory practicals and case studies; - describe some of the most widely used practices and illustrate the various approaches to the analysis of materials and their deterioration; - facilitate interdisciplinary teamwork among scientists and other professionals involved in the conservation process. The Handbook has evolved from lecture and laboratory handouts that have been developed for the ICCROM training programmes. It has been devised within the framework of the current courses, principally the International Refresher Course on Conservation of Architectural Heritage and Historic Structures (ARC). The general layout of each volume is as follows: introductory information, explanations of scientific termi- nology, the most common problems met, types of analysis, laboratory tests, case studies and bibliography. The concept behind the Handbook is modular and it has been purposely structured as a series of independent volumes to allow: - authors to periodically update the -
Estimation of Illuminants from Projections on the Planckian Locus Baptiste Mazin, Julie Delon, Yann Gousseau
Estimation of Illuminants From Projections on the Planckian Locus Baptiste Mazin, Julie Delon, Yann Gousseau To cite this version: Baptiste Mazin, Julie Delon, Yann Gousseau. Estimation of Illuminants From Projections on the Planckian Locus. IEEE Transactions on Image Processing, Institute of Electrical and Electronics Engineers, 2015, 24 (6), pp. 1944 - 1955. 10.1109/TIP.2015.2405414. hal-00915853v3 HAL Id: hal-00915853 https://hal.archives-ouvertes.fr/hal-00915853v3 Submitted on 9 Oct 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. 1 Estimation of Illuminants From Projections on the Planckian Locus Baptiste Mazin, Julie Delon and Yann Gousseau Abstract—This paper introduces a new approach for the have been proposed, restraining the hypothesis to well chosen automatic estimation of illuminants in a digital color image. surfaces of the scene, that are assumed to be grey [41]. Let The method relies on two assumptions. First, the image is us mention the work [54], which makes use of an invariant supposed to contain at least a small set of achromatic pixels. The second assumption is physical and concerns the set of color coordinate [20] that depends only on surface reflectance possible illuminants, assumed to be well approximated by black and not on the scene illuminant. -
Color Appearance Models Second Edition
Color Appearance Models Second Edition Mark D. Fairchild Munsell Color Science Laboratory Rochester Institute of Technology, USA Color Appearance Models Wiley–IS&T Series in Imaging Science and Technology Series Editor: Michael A. Kriss Formerly of the Eastman Kodak Research Laboratories and the University of Rochester The Reproduction of Colour (6th Edition) R. W. G. Hunt Color Appearance Models (2nd Edition) Mark D. Fairchild Published in Association with the Society for Imaging Science and Technology Color Appearance Models Second Edition Mark D. Fairchild Munsell Color Science Laboratory Rochester Institute of Technology, USA Copyright © 2005 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England Telephone (+44) 1243 779777 This book was previously publisher by Pearson Education, Inc Email (for orders and customer service enquiries): [email protected] Visit our Home Page on www.wileyeurope.com or www.wiley.com 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, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP, UK, without the permission in writing of the Publisher. Requests to the Publisher should be addressed to the Permissions Department, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England, or emailed to [email protected], or faxed to (+44) 1243 770571. This publication is designed to offer Authors the opportunity to publish accurate and authoritative information in regard to the subject matter covered. -
Major Color Order Systems and Their Psychophysical Structure
Chapter 7 Major Color Order Systems and Their Psychophysical Structure In this chapter only the Munsell, OSA-UCS, and Swedish NCS systems will be discussed. The former two are the most important attempts to create psy- chologically uniform systems, the latter uses the presumed natural approach of Hering (see Chapter 2) to create a color order system, having a regular structure, but not one uniform in terms of size of perceived differences. There are several other newer color order systems extant, but they neither make claims for uniformity nor for regularity according to new, significant psycho- logical attributes. The issue of viewing conditions for these systems has been attended to in different ways.As discussed previously,the Munsell system is illustrated as if the chips at each value level would be viewed against an achromatic surround of the same value.Chips of two adjacent value levels are illustrated as if viewed against an achromatic surround of intermediate value. The actual atlas displays the chips on white paper (historically of various degrees of whiteness), thus result- ing in distortions of the value scale, particularly at lower values.The OSA-UCS system is defined for an achromatic surround of luminous reflectance Y = 30 (L = 0).The atlas samples are in transparent jackets.NCS,finally,has been estab- lished against an immediate achromatic surround of Y = 78 in a light booth painted with an achromatic gray of Y = 54.The atlas displays samples on white paper. Both latter systems only result in the intended color experiences when viewed against the appropriate surrounds. Munsell and NCS are defined for Color Space and Its Divisions: Color Order from Antiquity to the Present, by Rolf G. -
PRECISE COLOR COMMUNICATION COLOR CONTROL from PERCEPTION to INSTRUMENTATION Knowing Color
PRECISE COLOR COMMUNICATION COLOR CONTROL FROM PERCEPTION TO INSTRUMENTATION Knowing color. Knowing by color. In any environment, color attracts attention. An infinite number of colors surround us in our everyday lives. We all take color pretty much for granted, but it has a wide range of roles in our daily lives: not only does it influence our tastes in food and other purchases, the color of a person’s face can also tell us about that person’s health. Even though colors affect us so much and their importance continues to grow, our knowledge of color and its control is often insufficient, leading to a variety of problems in deciding product color or in business transactions involving color. Since judgement is often performed according to a person’s impression or experience, it is impossible for everyone to visually control color accurately using common, uniform standards. Is there a way in which we can express a given color* accurately, describe that color to another person, and have that person correctly reproduce the color we perceive? How can color communication between all fields of industry and study be performed smoothly? Clearly, we need more information and knowledge about color. *In this booklet, color will be used as referring to the color of an object. Contents PART I Why does an apple look red? ········································································································4 Human beings can perceive specific wavelengths as colors. ························································6 What color is this apple ? ··············································································································8 Two red balls. How would you describe the differences between their colors to someone? ·······0 Hue. Lightness. Saturation. The world of color is a mixture of these three attributes. -
Standard Colour Spaces
Artists House t: +44 (0)20 7292 0400 14-15 Manette St f: +44 (0)20 7292 0401 London, W1D 4AP www.filmlight.ltd.uk UNITED KINGDOM Technical Note Standard Colour Spaces Richard Kirk Document ref. FL-TL-TN-0417-StdColourSpaces Creation date January 9, 2004 Last modified 30 November 2010 Version no. 4.0 Summary In 1931, the Commission Internationale d'Éclairiage (CIE) recommended a system for colour measurement. This system allowed the specification of colour matches using the CIX XYZ tristimulus values. In 1976, the CIE recommended the CIE LAB and CIE LUV colour spaces for the measurement of colour differences, and colour tolerances. These colour spaces, and their more modern variants are the basic tools for modern colorimetry. You can use Truelight without knowing all about CIE colour spaces. However, if you wonder why the XYZ and the L*a*b* calibration for the same monitor look different, you may find your explanation here. Whites are dealt with in a separate section. Most of us know what white paper and white paint is. We might think we know what white light is too. A full discussion of what is and what is not 'white' is much too big a task for this small note, but we introduce a few basic issues. Scanners and printers usually communicate in their own device-dependent RGB. Video has its own colour standard. Densitometers use Status A and Status M colour spaces. We describe all the spaces that Truelight uses to build up a colour transform. Finally we describe a number of effects that are not covered by the standard colour models. -
Standard Illuminant
Standard illuminant From Wikipedia, the free encyclopedia A standard illuminant is a profile or spectrum of visible light which is published in order to allow images or colors recorded under different lighting to be compared. Contents 1 CIE illuminants 1.1 Illuminant A 1.2 Illuminants B and C 1.3 Illuminant series D 1.4 Illuminant E Relative spectral power distributions (SPDs) of CIE 1.5 Illuminant series F illuminants A, B, and C from 380nm to 780nm. 2 White point 2.1 White points of standard illuminants 3 References 4 External links CIE illuminants The International Commission on Illumination (usually abbreviated CIE for its French name) is the body responsible for publishing all of the well-known standard illuminants. Each of these is known by a letter or by a letter-number combination. Illuminants A, B, and C were introduced in 1931, with the intention of respectively representing average incandescent light, direct sunlight, and average daylight. Illuminants D represent phases of daylight, Illuminant E is the equal-energy illuminant, while Illuminants F represent fluorescent lamps of various composition. There are instructions on how to experimentally produce light sources ("standard sources") corresponding to the older illuminants. For the relatively newer ones (such as series D), experimenters are left to measure to profiles of their sources and compare them to the published spectra:[1] At present no artificial source is recommended to realize CIE standard illuminant D65 or any other illuminant D of different CCT. It is hoped that new developments in light sources and filters will eventually offer sufficient basis for a CIE recommendation. -
Computing Chromatic Adaptation
Computing Chromatic Adaptation Sabine S¨usstrunk A thesis submitted for the Degree of Doctor of Philosophy in the School of Computing Sciences, University of East Anglia, Norwich. July 2005 c This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with the author and that no quotation from the thesis, nor any information derived therefrom, may be published without the author’s prior written consent. ii Abstract Most of today’s chromatic adaptation transforms (CATs) are based on a modified form of the von Kries chromatic adaptation model, which states that chromatic adaptation is an independent gain regulation of the three photoreceptors in the human visual system. However, modern CATs apply the scaling not in cone space, but use “sharper” sensors, i.e. sensors that have a narrower shape than cones. The recommended transforms currently in use are derived by minimizing perceptual error over experimentally obtained corresponding color data sets. We show that these sensors are still not optimally sharp. Using different com- putational approaches, we obtain sensors that are even more narrowband. In a first experiment, we derive a CAT by using spectral sharpening on Lam’s corresponding color data set. The resulting Sharp CAT, which minimizes XYZ errors, performs as well as the current most popular CATs when tested on several corresponding color data sets and evaluating perceptual error. Designing a spherical sampling technique, we can indeed show that these CAT sensors are not unique, and that there exist a large number of sensors that perform just as well as CAT02, the chromatic adap- tation transform used in CIECAM02 and the ICC color management framework.