新動画用拡張色空間xvycc(IEC61966-2-4)
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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. -
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. -
Iec 61966-2-4
This is a preview - click here to buy the full publication IEC 61966-2-4 Edition 1.0 2006-01 INTERNATIONAL STANDARD Multimedia systems and equipment – Colour measurement and management – Part 2-4: Colour management – Extended-gamut YCC colour space for video applications – xvYCC INTERNATIONAL ELECTROTECHNICAL COMMISSION PRICE CODE R ICS 33.160.40 ISBN 2-8318-8426-8 This is a preview - click here to buy the full publication – 2 – 61966-2-4 IEC:2006(E) CONTENTS FOREWORD...........................................................................................................................3 INTRODUCTION.....................................................................................................................5 1 Scope...............................................................................................................................6 2 Normative references .......................................................................................................6 3 Terms and definitions.........................................................................................................6 4 Colorimetric parameters and related characteristics .........................................................7 4.1 Primary colours and reference white........................................................................7 4.2 Opto-electronic transfer characteristics ...................................................................7 4.3 YCC (luma-chroma-chroma) encoding methods.......................................................8 -
246E9QSB/00 Philips LCD Monitor with Ultra Wide-Color
Philips LCD monitor with Ultra Wide-Color E Line 24 (23.8" / 60.5 cm diag.) Full HD (1920 x 1080) 246E9QSB Stunning color, stylish design The Philips E line monitor features stylish design with extraordinary picture performance. A narrow border Full HD display with Ultra Wide-Color brings you to real true-to-life visuals. Enjoy superior viewing in a stylish design. Superb Picture Quality • Ultra Wide-Color wider range of colors for a vivid picture • IPS LED wide view technology for image and color accuracy • 16:9 Full HD display for crisp detailed images Features designed for you • Narrow border display for a seamless appearance • Less eye fatigue with Flicker-free technology • LowBlue Mode for easy on-the-eyes productivity • EasyRead mode for a paper-like reading experience Greener everyday • Eco-friendly materials meet major international standards • Low power consumption saves energy bills LCD monitor with Ultra Wide-Color 246E9QSB/00 E Line 24 (23.8" / 60.5 cm diag.), Full HD (1920 x 1080) Highlights Ultra Wide-Color Technology 16:9 Full HD display a new solution to regulate brightness and reduce flicker for more comfortable viewing. LowBlue Mode Ultra Wide-Color Technology delivers a wider Picture quality matters. Regular displays deliver spectrum of colors for a more brilliant picture. quality, but you expect more. This display Ultra Wide-Color wider "color gamut" features enhanced Full HD 1920 x 1080 produces more natural-looking greens, vivid resolution. With Full HD for crisp detail paired Studies have shown that just as ultra-violet rays reds and deeper blues. -
Specification of Srgb
How to interpret the sRGB color space (specified in IEC 61966-2-1) for ICC profiles A. Key sRGB color space specifications (see IEC 61966-2-1 https://webstore.iec.ch/publication/6168 for more information). 1. Chromaticity co-ordinates of primaries: R: x = 0.64, y = 0.33, z = 0.03; G: x = 0.30, y = 0.60, z = 0.10; B: x = 0.15, y = 0.06, z = 0.79. Note: These are defined in ITU-R BT.709 (the television standard for HDTV capture). 2. Reference display‘Gamma’: Approximately 2.2 (see precise specification of color component transfer function below). 3. Reference display white point chromaticity: x = 0.3127, y = 0.3290, z = 0.3583 (equivalent to the chromaticity of CIE Illuminant D65). 4. Reference display white point luminance: 80 cd/m2 (includes veiling glare). Note: The reference display white point tristimulus values are: Xabs = 76.04, Yabs = 80, Zabs = 87.12. 5. Reference veiling glare luminance: 0.2 cd/m2 (this is the reference viewer-observed black point luminance). Note: The reference viewer-observed black point tristimulus values are assumed to be: Xabs = 0.1901, Yabs = 0.2, Zabs = 0.2178. These values are not specified in IEC 61966-2-1, and are an additional interpretation provided in this document. 6. Tristimulus value normalization: The CIE 1931 XYZ values are scaled from 0.0 to 1.0. Note: The following scaling equations can be used. These equations are not provided in IEC 61966-2-1, and are an additional interpretation provided in this document. 76.04 X abs 0.1901 XN = = 0.0125313 (Xabs – 0.1901) 80 76.04 0.1901 Yabs 0.2 YN = = 0.0125313 (Yabs – 0.2) 80 0.2 87.12 Zabs 0.2178 ZN = = 0.0125313 (Zabs – 0.2178) 80 87.12 0.2178 7. -
HD-SDI, HDMI, and Tempus Fugit
TECHNICALL Y SPEAKING... By Steve Somers, Vice President of Engineering HD-SDI, HDMI, and Tempus Fugit D-SDI (high definition serial digital interface) and HDMI (high definition multimedia interface) Hversion 1.3 are receiving considerable attention these days. “These days” really moved ahead rapidly now that I recall writing in this column on HD-SDI just one year ago. And, exactly two years ago the topic was DVI and HDMI. To be predictably trite, it seems like just yesterday. As with all things digital, there is much change and much to talk about. HD-SDI Redux difference channels suffice with one-half 372M spreads out the image information The HD-SDI is the 1.5 Gbps backbone the sample rate at 37.125 MHz, the ‘2s’ between the two channels to distribute of uncompressed high definition video in 4:2:2. This format is sufficient for high the data payload. Odd-numbered lines conveyance within the professional HD definition television. But, its robustness map to link A and even-numbered lines production environment. It’s been around and simplicity is pressing it into the higher map to link B. Table 1 indicates the since about 1996 and is quite literally the bandwidth demands of digital cinema and organization of 4:2:2, 4:4:4, and 4:4:4:4 savior of high definition interfacing and other uses like 12-bit, 4096 level signal data with respect to the available frame delivery at modest cost over medium-haul formats, refresh rates above 30 frames per rates. distances using RG-6 style video coax. -
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. -
NFP Brand Guide At-A-Glance
NFP Brand Guide At-A-Glance Communication tools to keep your conversations growing. At-A-Glance Brand Guidelines Page 2 Clear Space Always maintain a minimum area of clear space around the NFP logo. This area is equal to the height of the nexus symbol as shown to the right. Do not let anything infringe upon this space. Minimum Size = x 1x Print Minimum Digital Minimum 1x 1x 1x 0.75˝ 90px Signature Color Use NFP Green – PANTONE® 363C (RGB 170/208/149) – whenever possible. When you can’t use NFP Green – on forms and limited color communications – use black. The logo can also be reversed out to white for use on solid and dark backgrounds. The reversed logo should not be used on complex backgrounds or a background without sufficient contrast. Logo Do Nots Changing the NFP logo in any way weakens its impact and our brand strength, and detracts from the consistent image we want to project. The examples below are not exhaustive, but help demonstrate what not to do. Don’t use an older version Don’t redraw any of our Signature. elements. Don’t modify or change Don’t add a stroke. the color. Don’t place on complex Don’t add effects or backgrounds. drop shadows. Don’t create lockups with Don’t use our Signature any other logos. How cares... as a “read through.” Acme Brothers Don’t modify the letter forms or use another Don’t distort the logo. NFP typeface. Don’t use the full- Don’t add graphics color logo on colored or drawings. -
Compsci 372 – Tutorial
CompSci 372 – Tutorial Part 10 Colour 20/10/2008 CompSci 372 - Tutorial 10 1 Electromagnetic Radiation 20/10/2008 CompSci 372 - Tutorial 10 2 Spectrum ● Spectral Density Function (SDF) Energy Energy 400nm Wavelength 700nm 400nm Wavelength 700nm Energy Energy 400nm Wavelength 700nm 400nm Wavelength 700nm 20/10/2008 CompSci 372 - Tutorial 10 3 Spectrum Energy 400nm Wavelength 700nm ● Hue: Dominant wavelength ● Luminance: Sum of energy, Brightness ● Saturation: Ratio Hue part/Luminance 20/10/2008 CompSci 372 - Tutorial 10 4 Spectrum Energy 400nm Wavelength 700nm ● Hue High S High S Med L Low L ● Luminance ● Saturation 20/10/2008 CompSci 372 - Tutorial 10 5 Spectrum Energy 400nm Wavelength 700nm ● Hue Low S Low S High L Low L ● Luminance ● Saturation 20/10/2008 CompSci 372 - Tutorial 10 6 Interaction with Materials ● Absorption ● Transmission ● Reflection ● (Combination) ● Preservation of Energy 20/10/2008 CompSci 372 - Tutorial 10 7 Spectrum ● Spectral Response Function (SRF) Energy Energy 400nm Wavelength 700nm 400nm Wavelength 700nm Energy Energy 400nm Wavelength 700nm 400nm Wavelength 700nm 20/10/2008 CompSci 372 - Tutorial 10 8 Result ● SDF · SRF = Result SDF Energy 400nm Wavelength 700nm Energy 400nm Wavelength 700nm Energy 400nm Wavelength 700nm 20/10/2008 CompSci 372 - Tutorial 10 9 Result ● Light sources defined by their SDF ● Materials defined by their SRF – Absorbed Light SRF ● Surfaces, Eye, CCD chip, ... – Reflected Light SRF ● Mirror, polished surface,... – Transmitted Light SRF ● Glass, water, ... 20/10/2008 CompSci 372 - Tutorial -
![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. -
Color Space Analysis for Iris Recognition
Graduate Theses, Dissertations, and Problem Reports 2007 Color space analysis for iris recognition Matthew K. Monaco West Virginia University Follow this and additional works at: https://researchrepository.wvu.edu/etd Recommended Citation Monaco, Matthew K., "Color space analysis for iris recognition" (2007). Graduate Theses, Dissertations, and Problem Reports. 1859. https://researchrepository.wvu.edu/etd/1859 This Thesis is protected by copyright and/or related rights. It has been brought to you by the The Research Repository @ WVU with permission from the rights-holder(s). You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you must obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in WVU Graduate Theses, Dissertations, and Problem Reports collection by an authorized administrator of The Research Repository @ WVU. For more information, please contact [email protected]. Color Space Analysis for Iris Recognition by Matthew K. Monaco Thesis submitted to the College of Engineering and Mineral Resources at West Virginia University in partial ful¯llment of the requirements for the degree of Master of Science in Electrical Engineering Arun A. Ross, Ph.D., Chair Lawrence Hornak, Ph.D. Xin Li, Ph.D. Lane Department of Computer Science and Electrical Engineering Morgantown, West Virginia 2007 Keywords: Iris, Iris Recognition, Multispectral, Iris Anatomy, Iris Feature Extraction, Iris Fusion, Score Level Fusion, Multimodal Biometrics, Color Space Analysis, Iris Enhancement, Iris Color Analysis, Color Iris Recognition Copyright 2007 Matthew K. -
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.