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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. -
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. -
Camera Raw Workflows
RAW WORKFLOWS: FROM CAMERA TO POST Copyright 2007, Jason Rodriguez, Silicon Imaging, Inc. Introduction What is a RAW file format, and what cameras shoot to these formats? How does working with RAW file-format cameras change the way I shoot? What changes are happening inside the camera I need to be aware of, and what happens when I go into post? What are the available post paths? Is there just one, or are there many ways to reach my end goals? What post tools support RAW file format workflows? How do RAW codecs like CineForm RAW enable me to work faster and with more efficiency? What is a RAW file? In simplest terms is the native digital data off the sensor's A/D converter with no further destructive DSP processing applied Derived from a photometrically linear data source, or can be reconstructed to produce data that directly correspond to the light that was captured by the sensor at the time of exposure (i.e., LOG->Lin reverse LUT) Photometrically Linear 1:1 Photons Digital Values Doubling of light means doubling of digitally encoded value What is a RAW file? In film-analogy would be termed a “digital negative” because it is a latent representation of the light that was captured by the sensor (up to the limit of the full-well capacity of the sensor) “RAW” cameras include Thomson Viper, Arri D-20, Dalsa Evolution 4K, Silicon Imaging SI-2K, Red One, Vision Research Phantom, noXHD, Reel-Stream “Quasi-RAW” cameras include the Panavision Genesis In-Camera Processing Most non-RAW cameras on the market record to 8-bit YUV formats -
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. -
Khronos Data Format Specification
Khronos Data Format Specification Andrew Garrard Version 1.2, Revision 1 2019-03-31 1 / 207 Khronos Data Format Specification License Information Copyright (C) 2014-2019 The Khronos Group Inc. All Rights Reserved. This specification is protected by copyright laws and contains material proprietary to the Khronos Group, Inc. It or any components may not be reproduced, republished, distributed, transmitted, displayed, broadcast, or otherwise exploited in any manner without the express prior written permission of Khronos Group. You may use this specification for implementing the functionality therein, without altering or removing any trademark, copyright or other notice from the specification, but the receipt or possession of this specification does not convey any rights to reproduce, disclose, or distribute its contents, or to manufacture, use, or sell anything that it may describe, in whole or in part. This version of the Data Format Specification is published and copyrighted by Khronos, but is not a Khronos ratified specification. Accordingly, it does not fall within the scope of the Khronos IP policy, except to the extent that sections of it are normatively referenced in ratified Khronos specifications. Such references incorporate the referenced sections into the ratified specifications, and bring those sections into the scope of the policy for those specifications. Khronos Group grants express permission to any current Promoter, Contributor or Adopter member of Khronos to copy and redistribute UNMODIFIED versions of this specification in any fashion, provided that NO CHARGE is made for the specification and the latest available update of the specification for any version of the API is used whenever possible. -
How Close Is Close Enough? Specifying Colour Tolerances for Hdr and Wcg Displays
HOW CLOSE IS CLOSE ENOUGH? SPECIFYING COLOUR TOLERANCES FOR HDR AND WCG DISPLAYS Jaclyn A. Pytlarz, Elizabeth G. Pieri Dolby Laboratories Inc., USA ABSTRACT With a new high-dynamic-range (HDR) and wide-colour-gamut (WCG) standard defined in ITU-R BT.2100 (1), display and projector manufacturers are racing to extend their visible colour gamut by brightening and widening colour primaries. The question is: how close is close enough? Having this answer is increasingly important for both consumer and professional display manufacturers who strive to balance design trade-offs. In this paper, we present “ground truth” visible colour differences from a psychophysical experiment using HDR laser cinema projectors with near BT.2100 colour primaries up to 1000 cd/m2. We present our findings, compare colour difference metrics, and propose specifying colour tolerances for HDR/WCG displays using the ΔICTCP (2) metric. INTRODUCTION AND BACKGROUND From initial display design to consumer applications, measuring colour differences is a vital component of the imaging pipeline. Now that the industry has moved towards displays with higher dynamic range as well as wider, more saturated colours, no standardized method of measuring colour differences exists. In display calibration, aside from metamerism effects, it is crucial that the specified tolerances align with human perception. Otherwise, one of two undesirable situations might result: first, tolerances are too large and calibrated displays will not appear to visually match; second, tolerances are unnecessarily tight and the calibration process becomes uneconomic. The goal of this paper is to find a colour difference measurement metric for HDR/WCG displays that balances the two and closely aligns with human vision. -
Khronos Data Format Specification
Khronos Data Format Specification Andrew Garrard Version 1.3.1 2020-04-03 1 / 281 Khronos Data Format Specification License Information Copyright (C) 2014-2019 The Khronos Group Inc. All Rights Reserved. This specification is protected by copyright laws and contains material proprietary to the Khronos Group, Inc. It or any components may not be reproduced, republished, distributed, transmitted, displayed, broadcast, or otherwise exploited in any manner without the express prior written permission of Khronos Group. You may use this specification for implementing the functionality therein, without altering or removing any trademark, copyright or other notice from the specification, but the receipt or possession of this specification does not convey any rights to reproduce, disclose, or distribute its contents, or to manufacture, use, or sell anything that it may describe, in whole or in part. This version of the Data Format Specification is published and copyrighted by Khronos, but is not a Khronos ratified specification. Accordingly, it does not fall within the scope of the Khronos IP policy, except to the extent that sections of it are normatively referenced in ratified Khronos specifications. Such references incorporate the referenced sections into the ratified specifications, and bring those sections into the scope of the policy for those specifications. Khronos Group grants express permission to any current Promoter, Contributor or Adopter member of Khronos to copy and redistribute UNMODIFIED versions of this specification in any fashion, provided that NO CHARGE is made for the specification and the latest available update of the specification for any version of the API is used whenever possible. -
KONA Series Capture, Display, Convert
KONA Series Capture, Display, Convert Installation and Operation Manual Version 16.1 Published July 14, 2021 Notices Trademarks AJA® and Because it matters.® are registered trademarks of AJA Video Systems, Inc. for use with most AJA products. AJA™ is a trademark of AJA Video Systems, Inc. for use with recorder, router, software and camera products. Because it matters.™ is a trademark of AJA Video Systems, Inc. for use with camera products. Corvid Ultra®, lo®, Ki Pro®, KONA®, KUMO®, ROI® and T-Tap® are registered trademarks of AJA Video Systems, Inc. AJA Control Room™, KiStor™, Science of the Beautiful™, TruScale™, V2Analog™ and V2Digital™ are trademarks of AJA Video Systems, Inc. All other trademarks are the property of their respective owners. Copyright Copyright © 2021 AJA Video Systems, Inc. All rights reserved. All information in this manual is subject to change without notice. No part of the document may be reproduced or transmitted in any form, or by any means, electronic or mechanical, including photocopying or recording, without the express written permission of AJA Video Systems, Inc. Contacting AJA Support When calling for support, have all information at hand prior to calling. To contact AJA for sales or support, use any of the following methods: Telephone +1.530.271.3190 FAX +1.530.271.3140 Web https://www.aja.com Support Email [email protected] Sales Email [email protected] KONA Capture, Display, Convert v16.1 2 www.aja.com Contents Notices . .2 Trademarks . 2 Copyright . 2 Contacting AJA Support . 2 Chapter 1 – Introduction . .5 Overview. .5 KONA Models Covered in this Manual . -
User Requirements for Video Monitors in Television Production
TECH 3320 USER REQUIREMENTS FOR VIDEO MONITORS IN TELEVISION PRODUCTION VERSION 4.1 Geneva September 2019 This page and several other pages in the document are intentionally left blank. This document is paginated for two sided printing Tech 3320 v4.1 User requirements for Video Monitors in Television Production Conformance Notation This document contains both normative text and informative text. All text is normative except for that in the Introduction, any § explicitly labeled as ‘Informative’ or individual paragraphs which start with ‘Note:’. Normative text describes indispensable or mandatory elements. It contains the conformance keywords ‘shall’, ‘should’ or ‘may’, defined as follows: ‘Shall’ and ‘shall not’: Indicate requirements to be followed strictly and from which no deviation is permitted in order to conform to the document. ‘Should’ and ‘should not’: Indicate that, among several possibilities, one is recommended as particularly suitable, without mentioning or excluding others. OR indicate that a certain course of action is preferred but not necessarily required. OR indicate that (in the negative form) a certain possibility or course of action is deprecated but not prohibited. ‘May’ and ‘need not’: Indicate a course of action permissible within the limits of the document. Informative text is potentially helpful to the user, but it is not indispensable, and it does not affect the normative text. Informative text does not contain any conformance keywords. Unless otherwise stated, a conformant implementation is one which includes all mandatory provisions (‘shall’) and, if implemented, all recommended provisions (‘should’) as described. A conformant implementation need not implement optional provisions (‘may’) and need not implement them as described. -
Color Images, Color Spaces and Color Image Processing
color images, color spaces and color image processing Ole-Johan Skrede 08.03.2017 INF2310 - Digital Image Processing Department of Informatics The Faculty of Mathematics and Natural Sciences University of Oslo After original slides by Fritz Albregtsen today’s lecture ∙ Color, color vision and color detection ∙ Color spaces and color models ∙ Transitions between color spaces ∙ Color image display ∙ Look up tables for colors ∙ Color image printing ∙ Pseudocolors and fake colors ∙ Color image processing ∙ Sections in Gonzales & Woods: ∙ 6.1 Color Funcdamentals ∙ 6.2 Color Models ∙ 6.3 Pseudocolor Image Processing ∙ 6.4 Basics of Full-Color Image Processing ∙ 6.5.5 Histogram Processing ∙ 6.6 Smoothing and Sharpening ∙ 6.7 Image Segmentation Based on Color 1 motivation ∙ We can differentiate between thousands of colors ∙ Colors make it easy to distinguish objects ∙ Visually ∙ And digitally ∙ We need to: ∙ Know what color space to use for different tasks ∙ Transit between color spaces ∙ Store color images rationally and compactly ∙ Know techniques for color image printing 2 the color of the light from the sun spectral exitance The light from the sun can be modeled with the spectral exitance of a black surface (the radiant exitance of a surface per unit wavelength) 2πhc2 1 M(λ) = { } : λ5 hc − exp λkT 1 where ∙ h ≈ 6:626 070 04 × 10−34 m2 kg s−1 is the Planck constant. ∙ c = 299 792 458 m s−1 is the speed of light. ∙ λ [m] is the radiation wavelength. ∙ k ≈ 1:380 648 52 × 10−23 m2 kg s−2 K−1 is the Boltzmann constant. T ∙ [K] is the surface temperature of the radiating Figure 1: Spectral exitance of a black body surface for different body. -
![Arxiv:1902.00267V1 [Cs.CV] 1 Feb 2019 Fcmue Iin Oto H Aaesue O Mg Classificat Image Th for in Used Applications Datasets Fundamental the Most of the Most Vision](https://docslib.b-cdn.net/cover/0817/arxiv-1902-00267v1-cs-cv-1-feb-2019-fcmue-iin-oto-h-aaesue-o-mg-classi-cat-image-th-for-in-used-applications-datasets-fundamental-the-most-of-the-most-vision-1150817.webp)
Arxiv:1902.00267V1 [Cs.CV] 1 Feb 2019 Fcmue Iin Oto H Aaesue O Mg Classificat Image Th for in Used Applications Datasets Fundamental the Most of the Most Vision
ColorNet: Investigating the importance of color spaces for image classification⋆ Shreyank N Gowda1 and Chun Yuan2 1 Computer Science Department, Tsinghua University, Beijing 10084, China [email protected] 2 Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China [email protected] Abstract. Image classification is a fundamental application in computer vision. Recently, deeper networks and highly connected networks have shown state of the art performance for image classification tasks. Most datasets these days consist of a finite number of color images. These color images are taken as input in the form of RGB images and clas- sification is done without modifying them. We explore the importance of color spaces and show that color spaces (essentially transformations of original RGB images) can significantly affect classification accuracy. Further, we show that certain classes of images are better represented in particular color spaces and for a dataset with a highly varying number of classes such as CIFAR and Imagenet, using a model that considers multi- ple color spaces within the same model gives excellent levels of accuracy. Also, we show that such a model, where the input is preprocessed into multiple color spaces simultaneously, needs far fewer parameters to ob- tain high accuracy for classification. For example, our model with 1.75M parameters significantly outperforms DenseNet 100-12 that has 12M pa- rameters and gives results comparable to Densenet-BC-190-40 that has 25.6M parameters for classification of four competitive image classifica- tion datasets namely: CIFAR-10, CIFAR-100, SVHN and Imagenet. Our model essentially takes an RGB image as input, simultaneously converts the image into 7 different color spaces and uses these as inputs to individ- ual densenets. -
Application of Contrast Sensitivity Functions in Standard and High Dynamic Range Color Spaces
https://doi.org/10.2352/ISSN.2470-1173.2021.11.HVEI-153 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Color Threshold Functions: Application of Contrast Sensitivity Functions in Standard and High Dynamic Range Color Spaces Minjung Kim, Maryam Azimi, and Rafał K. Mantiuk Department of Computer Science and Technology, University of Cambridge Abstract vision science. However, it is not obvious as to how contrast Contrast sensitivity functions (CSFs) describe the smallest thresholds in DKL would translate to thresholds in other color visible contrast across a range of stimulus and viewing param- spaces across their color components due to non-linearities such eters. CSFs are useful for imaging and video applications, as as PQ encoding. contrast thresholds describe the maximum of color reproduction In this work, we adapt our spatio-chromatic CSF1 [12] to error that is invisible to the human observer. However, existing predict color threshold functions (CTFs). CTFs describe detec- CSFs are limited. First, they are typically only defined for achro- tion thresholds in color spaces that are more commonly used in matic contrast. Second, even when they are defined for chromatic imaging, video, and color science applications, such as sRGB contrast, the thresholds are described along the cardinal dimen- and YCbCr. The spatio-chromatic CSF model from [12] can sions of linear opponent color spaces, and therefore are difficult predict detection thresholds for any point in the color space and to relate to the dimensions of more commonly used color spaces, for any chromatic and achromatic modulation.