Multimedia Systems Color Space
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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 -
US Army Photography Course Laboratory Procedures SS0509
SUBCOURSE EDITION SS0509 8 LABORATORY PROCEDURES US ARMY STILL PHOTOGRAPHIC SPECIALIST MOS 84B SKILL LEVEL 1 AUTHORSHIP RESPONSIBILITY: SSG Dennis L. Foster 560th Signal Battalion Visual Information/Calibration Training Development Division Lowry AFB, Colorado LABORATORY PROCEDURES SUBCOURSE NO. SS0509-8 (Developmental Date: 30 June 1988) US Army Signal Center and Fort Gordon Fort Gordon, Georgia Five Credit Hours GENERAL The laboratory procedures subcourse is designed to teach tasks related to work in a photographic laboratory. Information is provided on the types and uses of chemistry, procedures for processing negatives and prints, and for mixing and storing chemicals, procedures for producing contact and projection prints, and photographic quality control. This subcourse is divided into three lessons with each lesson corresponding to a terminal learning objective as indicated below. Lesson 1: PREPARATION OF PHOTOGRAPHIC CHEMISTRY TASK: Determine the types and uses of chemistry, for both black and white and color, the procedures for processing negatives and prints, the procedures for mixing and storing chemicals. CONDITIONS: Given information and diagrams on the types of chemistry and procedures for mixing and storage. STANDARDS: Demonstrate competency of the task skills and knowledge by correctly responding to at least 75% of the multiple-choice test covering preparation of photographic chemistry. (This objective supports SM tasks 113-578-3022, Mix Photographic Chemistry; 113-578-3023, Process Black and White Film Manually; 113-578-3024, Dry Negatives in Photographic Film Drier; 113-578-3026, Process Black and White Photographic Paper). i Lesson 2: PRODUCE A PHOTOGRAPHIC PRINT TASK: Perform the procedures for producing an acceptable contact and projection print. -
Yasser Syed & Chris Seeger Comcast/NBCU
Usage of Video Signaling Code Points for Automating UHD and HD Production-to-Distribution Workflows Yasser Syed & Chris Seeger Comcast/NBCU Comcast TPX 1 VIPER Architecture Simpler Times - Delivering to TVs 720 1920 601 HD 486 1080 1080i 709 • SD - HD Conversions • Resolution, Standard Dynamic Range and 601/709 Color Spaces • 4:3 - 16:9 Conversions • 4:2:0 - 8-bit YUV video Comcast TPX 2 VIPER Architecture What is UHD / 4K, HFR, HDR, WCG? HIGH WIDE HIGHER HIGHER DYNAMIC RESOLUTION COLOR FRAME RATE RANGE 4K 60p GAMUT Brighter and More Colorful Darker Pixels Pixels MORE FASTER BETTER PIXELS PIXELS PIXELS ENABLED BY DOLBYVISION Comcast TPX 3 VIPER Architecture Volume of Scripted Workflows is Growing Not considering: • Live Events (news/sports) • Localized events but with wider distributions • User-generated content Comcast TPX 4 VIPER Architecture More Formats to Distribute to More Devices Standard Definition Broadcast/Cable IPTV WiFi DVDs/Files • More display devices: TVs, Tablets, Mobile Phones, Laptops • More display formats: SD, HD, HDR, 4K, 8K, 10-bit, 8-bit, 4:2:2, 4:2:0 • More distribution paths: Broadcast/Cable, IPTV, WiFi, Laptops • Integration/Compositing at receiving device Comcast TPX 5 VIPER Architecture Signal Normalization AUTOMATED LOGIC FOR CONVERSION IN • Compositing, grading, editing SDR HLG PQ depends on proper signal BT.709 BT.2100 BT.2100 normalization of all source files (i.e. - Still Graphics & Titling, Bugs, Tickers, Requires Conversion Native Lower-Thirds, weather graphics, etc.) • ALL content must be moved into a single color volume space. Normalized Compositing • Transformation from different Timeline/Switcher/Transcoder - PQ-BT.2100 colourspaces (BT.601, BT.709, BT.2020) and transfer functions (Gamma 2.4, PQ, HLG) Convert Native • Correct signaling allows automation of conversion settings. -
Fresnel Zone Plate Imaging in Nuclear Medicine
FRESNEL ZONE PLATE IMAGING IN NUCLEAR MEDICINE Harrison H. Barrett Raytheon Research Division, Waltham, Massachusetts Considerable progress has been made in recent so that there is essentially no collimation. The zone years in detecting the scintillation pattern produced plate has a series of equi-area annular zones, alter by a gamma-ray image. Systems such as the Anger nately transparent and opaque to the gamma rays, camera (7) and Autoflouroscope (2) give efficient with the edges of the zones located at radii given by counting while an image intensifier camera (3,4) rn = n = 1,2, N. gives better spatial resolution at some sacrifice in (D efficiency. However, the common means of image To understand the operation of this aperture, con formation, the pinhole aperture and parallel-hole sider first a point source of gamma rays. Then collimator, are very inefficient. Only a tiny fraction the scintillation pattern on the crystal is a projected (~0.1-0.01%) of the gamma-ray photons emitted shadow of the zone plate, with the position of the by the source are transmitted to the detector plane shadow depending linearly on the position of the (scintillator crystal), and this fraction can be in source. The shadow thus contains the desired infor creased only by unacceptably degrading the spatial mation about the source location. It may be regarded resolution. It would be desirable, of course, to have as a coded image similar to a hologram. Indeed, a a large-aperture, gamma-ray lens so that good col Fresnel zone plate is simply the hologram of a point lection efficiency and good resolution could be ob source (9). -
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
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. -
Quantitative Schlieren Measurement of Explosively‐Driven Shock Wave
Full Paper DOI: 10.1002/prep.201600097 Quantitative Schlieren Measurement of Explosively-Driven Shock Wave Density, Temperature, and Pressure Profiles Jesse D. Tobin[a] and Michael J. Hargather*[a] Abstract: Shock waves produced from the detonation of the shock wave temperature decay profile. Alternatively, laboratory-scale explosive charges are characterized using the shock wave pressure decay profile can be estimated by high-speed, quantitative schlieren imaging. This imaging assuming the shape of the temperature decay. Results are allows the refractive index gradient field to be measured presented for two explosive sources. The results demon- and converted to a density field using an Abel deconvolu- strate the ability to measure both temperature and pres- tion. The density field is used in conjunction with simulta- sure decay profiles optically for spherical shock waves that neous piezoelectric pressure measurements to determine have detached from the driving explosion product gases. Keywords: Schlieren · Explosive temperature · Quantitative optical techniques 1 Introduction Characterization of explosive effects has traditionally been charge. This work used the quantitative density measure- performed using pressure gages and various measures of ment to estimate the pressure field and impulse produced damage, such as plate dents, to determine a “TNT Equiva- by the explosion as a function of distance. The pressure lence” of a blast [1,2]. Most of the traditional methods, measurement, however, relied on an assumed temperature. however, result in numerous equivalencies for the same The measurement of temperature in explosive events material due to large uncertainties and dependencies of can be considered a “holy grail”, which many researchers the equivalence on the distance from the explosion [3,4]. -
The Integrity of the Image
world press photo Report THE INTEGRITY OF THE IMAGE Current practices and accepted standards relating to the manipulation of still images in photojournalism and documentary photography A World Press Photo Research Project By Dr David Campbell November 2014 Published by the World Press Photo Academy Contents Executive Summary 2 8 Detecting Manipulation 14 1 Introduction 3 9 Verification 16 2 Methodology 4 10 Conclusion 18 3 Meaning of Manipulation 5 Appendix I: Research Questions 19 4 History of Manipulation 6 Appendix II: Resources: Formal Statements on Photographic Manipulation 19 5 Impact of Digital Revolution 7 About the Author 20 6 Accepted Standards and Current Practices 10 About World Press Photo 20 7 Grey Area of Processing 12 world press photo 1 | The Integrity of the Image – David Campbell/World Press Photo Executive Summary 1 The World Press Photo research project on “The Integrity of the 6 What constitutes a “minor” versus an “excessive” change is necessarily Image” was commissioned in June 2014 in order to assess what current interpretative. Respondents say that judgment is on a case-by-case basis, practice and accepted standards relating to the manipulation of still and suggest that there will never be a clear line demarcating these concepts. images in photojournalism and documentary photography are world- wide. 7 We are now in an era of computational photography, where most cameras capture data rather than images. This means that there is no 2 The research is based on a survey of 45 industry professionals from original image, and that all images require processing to exist. 15 countries, conducted using both semi-structured personal interviews and email correspondence, and supplemented with secondary research of online and library resources. -
Color Spaces
RGB Color Space 15 Chapter 3: Color Spaces Chapter 3 Color Spaces A color space is a mathematical representation RGB Color Space of a set of colors. The three most popular color models are RGB (used in computer graphics); The red, green, and blue (RGB) color space is YIQ, YUV, or YCbCr (used in video systems); widely used throughout computer graphics. and CMYK (used in color printing). However, Red, green, and blue are three primary addi- none of these color spaces are directly related tive colors (individual components are added to the intuitive notions of hue, saturation, and together to form a desired color) and are rep- brightness. This resulted in the temporary pur- resented by a three-dimensional, Cartesian suit of other models, such as HSI and HSV, to coordinate system (Figure 3.1). The indicated simplify programming, processing, and end- diagonal of the cube, with equal amounts of user manipulation. each primary component, represents various All of the color spaces can be derived from gray levels. Table 3.1 contains the RGB values the RGB information supplied by devices such for 100% amplitude, 100% saturated color bars, as cameras and scanners. a common video test signal. BLUE CYAN MAGENTA WHITE BLACK GREEN RED YELLOW Figure 3.1. The RGB Color Cube. 15 16 Chapter 3: Color Spaces Red Blue Cyan Black White Green Range Yellow Nominal Magenta R 0 to 255 255 255 0 0 255 255 0 0 G 0 to 255 255 255 255 255 0 0 0 0 B 0 to 255 255 0 255 0 255 0 255 0 Table 3.1. -
Setting Video Quality & Performance Targets for HDR and WCG Video
SETTING VIDEO QUALITY & PERFORMANCE TARGETS FOR HDR AND WCG VIDEO SERVICES SEAN T. MCCARTHY TABLE OF CONTENTS INTRODUCTION ............................................................................................. 3 Quantifying HDR WCG Video Quality & Distortions ....................................................... 3 The Performance of Existing HDR Video Quality Metrics ............................................... 4 Balancing Performance and Complexity ......................................................................... 5 CHARACTERISTICS OF HDR WCG VIDEO ........................................................ 6 Test Sequences & Preparation ....................................................................................... 6 Representing Images in Terms of Spatial Frequency ...................................................... 6 Expectable Statistics of Complex Images ....................................................................... 7 PROPOSED HDR WCG VIDEO DISTORTION ALGORITHM ............................... 8 Spatial Detail ................................................................................................................... 8 Effect of HEVC Compression on Spatial Detail Correlation .......................................... 11 Using Spatial Detail to Probe Bright & Dark Features and Textures ............................. 14 Spatial Detail Correlation for HDR WCG Features and Textures .................................. 16 Weighted Mean-Squared Error ...................................................................................