Basics of Video
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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. -
Off the Grid
IOP PUBLISHING NANOTECHNOLOGY Nanotechnology 24 (2013) 335703 (7pp) doi:10.1088/0957-4484/24/33/335703 Improved accuracy and speed in scanning probe microscopy by image reconstruction from non-gridded position sensor data Dominik Ziegler1, Travis R Meyer2, Rodrigo Farnham3, Christoph Brune4, Andrea L Bertozzi2 and Paul D Ashby1 1 Lawrence Berkeley National Laboratory, Molecular Foundry, 1 Cyclotron Road, 94720 Berkeley, CA, USA 2 Department of Mathematics, University of California Los Angeles, 520 Portola Plaza, Los Angeles, CA 90095-1555, USA 3 Department of Mathematics and Statistics, California State University, Long Beach, 1250 Bellflower Boulevard, Long Beach, CA, 90840-1001, USA 4 Department of Mathematics and Computer Science, University of Munster,¨ Einsteinstrasse 62, D-48149 Munster,¨ Germany E-mail: [email protected] Received 16 March 2013, in final form 3 July 2013 Published 26 July 2013 Online at stacks.iop.org/Nano/24/335703 Abstract Scanning probe microscopy (SPM) has facilitated many scientific discoveries utilizing its strengths of spatial resolution, non-destructive characterization and realistic in situ environments. However, accurate spatial data are required for quantitative applications but this is challenging for SPM especially when imaging at higher frame rates. We present a new operation mode for scanning probe microscopy that uses advanced image processing techniques to render accurate images based on position sensor data. This technique, which we call sensor inpainting, frees the scanner to no longer be at a specific location at a given time. This drastically reduces the engineering effort of position control and enables the use of scan waveforms that are better suited for the high inertia nanopositioners of SPM. -
Robust Pulse-Rate from Chrominance-Based Rppg Gerard De Haan and Vincent Jeanne
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. ?, NO. ?, MONTH 2013 1 Robust pulse-rate from chrominance-based rPPG Gerard de Haan and Vincent Jeanne Abstract—Remote photoplethysmography (rPPG) enables con- PPG signal into two independent signals built as a linear tactless monitoring of the blood volume pulse using a regular combination of two color channels [8]. One combination camera. Recent research focused on improved motion robustness, approximated the clean pulse-signal, the other the motion but the proposed blind source separation techniques (BSS) in RGB color space show limited success. We present an analysis of artifact, and the energy in the pulse-signal was minimized the motion problem, from which far superior chrominance-based to optimize the combination. Poh et al. extended this work methods emerge. For a population of 117 stationary subjects, we proposing a linear combination of all three color channels show our methods to perform in 92% good agreement (±1:96σ) defining three independent signals with Independent Compo- with contact PPG, with RMSE and standard deviation both a nent Analysis (ICA) using non-Gaussianity as the criterion factor of two better than BSS-based methods. In a fitness setting using a simple spectral peak detector, the obtained pulse-rate for independence [5]. Lewandowska et al. varied this con- for modest motion (bike) improves from 79% to 98% correct, cept defining three independent linear combinations of the and for vigorous motion (stepping) from less than 11% to more color channels with Principal Component Analysis (PCA) [6]. than 48% correct. We expect the greatly improved robustness to With both Blind Source Separation (BSS) techniques, the considerably widen the application scope of the technology. -
Colour Space Conversion
Colour 1 Shan He 2013 What is light? 'White' light is a combination of many different light wavelengths 2 Shan He 2013 Colour spectrum – visible light • Colour is how we perceive variation in the energy (oscillation wavelength) of visible light waves/particles. Ultraviolet Infrared 400 nm Wavelength 700 nm • Our eye perceives lower energy light (longer wavelength) as so-called red; and higher (shorter wavelength) as so-called blue • Our eyes are more sensitive to light (i.e. see it with more intensity) in some frequencies than in others 3 Shan He 2013 Colour perception . It seems that colour is our brain's perception of the relative levels of stimulation of the three types of cone cells within the human eye . Each type of cone cell is generally sensitive to the red, blue and green regions of the light spectrum Images of living human retina with different cones sensitive to different colours. 4 Shan He 2013 Colour perception . Spectral sensitivity of the cone cells overlap somewhat, so one wavelength of light will likely stimulate all three cells to some extent . So by mixing multiple components pure light (i.e. of a fixed spectrum) and altering intensity of the mixed components, we can vary the overall colour that is perceived by the brain. 5 Shan He 2013 Colour perception: Eye of Brain? • Stroop effect 6 Shan He 2013 Do you see same colours I see? • The number of color-sensitive cones in the human retina differs dramatically among people • Language might affect the way we perceive colour 7 Shan He 2013 Colour spaces . -
Er.Heena Gulati, Er. Parminder Singh
International Journal of Computer Science Trends and Technology (IJCST) – Volume 3 Issue 2, Mar-Apr 2015 RESEARCH ARTICLE OPEN ACCESS SWT Approach For The Detection Of Cotton Contaminants Er.Heena Gulati [1], Er. Parminder Singh [2] Research Scholar [1], Assistant Professor [2] Department of Computer Science and Engineering Doaba college of Engineering and Technology Kharar PTU University Punjab - India ABSTRACT Presence of foreign fibers’ & cotton contaminants in cotton degrades the quality of cotton The digital image processing techniques based on computer vision provides a good way to eliminate such contaminants from cotton. There are various techniques used to detect the cotton contaminants and foreign fibres. The major contaminants found in cotton are plastic film, nylon straps, jute, dry cotton, bird feather, glass, paper, rust, oil grease, metal wires and various foreign fibres like silk, nylon polypropylene of different colors and some of white colour may or may not be of cotton itself. After analyzing cotton contaminants characteristics adequately, the paper presents various techniques for detection of foreign fibres and contaminants from cotton. Many techniques were implemented like HSI, YDbDR, YCbCR .RGB images are converted into these components then by calculating the threshold values these images are fused in the end which detects the contaminants .In this research the YCbCR , YDbDR color spaces and fusion technique is applied that is SWT in the end which will fuse the image which is being analysis according to its threshold value and will provide good results which are based on parameters like mean ,standard deviation and variance and time. Keywords:- Cotton Contaminants; Detection; YCBCR,YDBDR,SWT Fusion, Comparison I. -
Cisco Broadband Data Book
Broadband Data Book © 2020 Cisco and/or its affiliates. All rights reserved. THE BROADBAND DATABOOK Cable Access Business Unit Systems Engineering Revision 21 August 2019 © 2020 Cisco and/or its affiliates. All rights reserved. 1 Table of Contents Section 1: INTRODUCTION ................................................................................................. 4 Section 2: FREQUENCY CHARTS ........................................................................................ 6 Section 3: RF CHARACTERISTICS OF BROADCAST TV SIGNALS ..................................... 28 Section 4: AMPLIFIER OUTPUT TILT ................................................................................. 37 Section 5: RF TAPS and PASSIVES CHARACTERISTICS ................................................... 42 Section 6: COAXIAL CABLE CHARACTERISTICS .............................................................. 64 Section 7: STANDARD HFC GRAPHIC SYMBOLS ............................................................. 72 Section 8: DTV STANDARDS WORLDWIDE ....................................................................... 80 Section 9: DIGITAL SIGNALS ............................................................................................ 90 Section 10: STANDARD DIGITAL INTERFACES ............................................................... 100 Section 11: DOCSIS SIGNAL CHARACTERISTICS ........................................................... 108 Section 12: FIBER CABLE CHARACTERISTICS ............................................................... -
High-Frequency Radiowa Ve Probing of the High-Latitude Ionosphere
RAYMOND A. GREENWALD HIGH-FREQUENCY RADIOWAVE PROBING OF THE HIGH-LATITUDE IONOSPHERE During the past several years, a program of high-frequency radiowave studies of the high-latitude ionosphere has been developed in the APL Space Department. Studies are now being conducted on the formation and motion of high-latitude ionospheric electron density irregularities, using a sophisti cated high-frequency radar system installed at Goose Bay, .Labrador. The radar antenna is also being used to receive signals from a beacon transmitter located at Thule, Greenland. This information is providing a better understanding of the spatial and temporal variability of high-latitude propagation channels and their relationship to disturbances in the magnetosphere-ionosphere system . INTRODUCTION turbances prior to their impingement on the magneto At altitudes above 100 kilometers, the atmosphere sphere is quite limited. Therefore, we still have only of the earth gradually changes from a predominantly limited success in forecasting sudden changes in the neutral medium to an increasingly ionized gas or plas high-latitude ionosphere and consequently in high ma. The ionization is caused chiefly by a combination latitude radiowave propagation. of solar extreme ultraviolet radiation and, at high lati In order for space scientists to obtain a better un tudes, particle precipitation from the earth's magne derstanding of the various interactions occurring tosphere. Because of its ionized nature between 100 among the solar wind, the magnetosphere, and the ion and 1000 kilometers, this part of the atmosphere is osphere, active measurement programs are conduct commonly referred to as the ionosphere. In this re ed in all three regions. -
Measuring Perceived Color Difference Using YIQ Color Space
Programación Matemática y Software (2010) Vol. 2. No 2. ISSN: 2007-3283 Recibido: 17 de Agosto de 2010 Aceptado: 25 de Noviembre de 2010 Publicado en línea: 30 de Diciembre de 2010 Measuring perceived color difference using YIQ NTSC transmission color space in mobile applications Yuriy Kotsarenko, Fernando Ramos TECNOLOGICO DE DE MONTERREY, CAMPUS CUERNAVACA. Resumen: En este trabajo varias fórmulas están introducidas que permiten calcular la medir la diferencia entre colores de forma perceptible, utilizando el espacio de colores YIQ. Las formulas clásicas y sus derivados que utilizan los espacios CIELAB y CIELUV requieren muchas transformaciones aritméticas de valores entrantes definidos comúnmente con los componentes de rojo, verde y azul, y por lo tanto son muy pesadas para su implementación en dispositivos móviles. Las fórmulas alternativas propuestas en este trabajo basadas en espacio de colores YIQ son sencillas y se calculan rápidamente, incluso en tiempo real. La comparación está incluida en este trabajo entre las formulas clásicas y las propuestas utilizando dos diferentes grupos de experimentos. El primer grupo de experimentos se enfoca en evaluar la diferencia perceptible utilizando diferentes fórmulas, mientras el segundo grupo de experimentos permite determinar el desempeño de cada una de las fórmulas para determinar su velocidad cuando se procesan imágenes. Los resultados experimentales indican que las formulas propuestas en este trabajo son muy cercanas en términos perceptibles a las de CIELAB y CIELUV, pero son significativamente más rápidas, lo que los hace buenos candidatos para la medición de las diferencias de colores en dispositivos móviles y aplicaciones en tiempo real. Abstract: An alternative color difference formulas are presented for measuring the perceived difference between two color samples defined in YIQ color space. -
Graphics Systems
Graphics Systems Dr. S.M. Malaek Assistant: M. Younesi Overview Display Hardware How are images displayed? Overview (Display Devices) Raster Scan Displays Random Scan Displays Color CRT Monirors Direct View Storage Tube Flat panel Displays Three Dimensional Viewing Devices Stereoscopic and Virtual Reality System Overview (Display Devices) The display systems are often referred to as Video Monitor or Video Display Unit (VDU). Display Hardware Video Display Devices The primary output device in a graphics system is a monitor. Video Monitor Cathode Ray Tube (CRT) 1. Electron Guns 2. Electron Beams 3. Focusing Coils 4. Deflection Coils 5. Anode Connection 6. Shadow Mask 7. Phosphor layer 8. Close-up of the phosphor coated inner side of the screen Cathode Ray Tube (CRT) Refresh CRT Light emitted by the Phosphor fades very rapidly. Refresh CRT: One way to keep the phosphor glowing is to redraw the picture repeatedly by quickly directing the electron beam back over the same points. Electron Gun Electron Gun Heat is supplied to the cathode by the filament. Electron Gun The free electrons are then accelerated toward the phosphor coating by a high positive voltage. High Positive Voltage A positively charged metal coating on the inside of the CRT envelope near the phosphor screen. A positively charged metal High Positive Voltage An accelerating anode . Electron Gun Intensity of the electron beam is controlled by setting voltage level on the control grid. Electron Gun A smaller negative voltage on the control grid simply decrease the number of electrons passing through. Focusing System Focusing System The focusing system is needed to force the electron beam to converge into a small spot as it strikes the phosphor. -
Measuring Perceived Color Difference Using YIQ NTSC Transmission Color Space in Mobile Applications
Programación Matemática y Software (2010) Vol.2. Num. 2. Dirección de Reservas de Derecho: 04-2009-011611475800-102 Measuring perceived color difference using YIQ NTSC transmission color space in mobile applications Yuriy Kotsarenko, Fernando Ramos TECNOLÓGICO DE DE MONTERREY, CAMPUS CUERNAVACA. Resumen. En este trabajo varias formulas están introducidas que permiten calcular la medir la diferencia entre colores de forma perceptible, utilizando el espacio de colores YIQ. Las formulas clásicas y sus derivados que utilizan los espacios CIELAB y CIELUV requieren muchas transformaciones aritméticas de valores entrantes definidos comúnmente con los componentes de rojo, verde y azul, y por lo tanto son muy pesadas para su implementación en dispositivos móviles. Las formulas alternativas propuestas en este trabajo basadas en espacio de colores YIQ son sencillas y se calculan rápidamente, incluso en tiempo real. La comparación está incluida en este trabajo entre las formulas clásicas y las propuestas utilizando dos diferentes grupos de experimentos. El primer grupo de experimentos se enfoca en evaluar la diferencia perceptible utilizando diferentes formulas, mientras el segundo grupo de experimentos permite determinar el desempeño de cada una de las formulas para determinar su velocidad cuando se procesan imágenes. Los resultados experimentales indican que las formulas propuestas en este trabajo son muy cercanas en términos perceptibles a las de CIELAB y CIELUV, pero son significativamente más rápidas, lo que los hace buenos candidatos para la medición de las diferencias de colores en dispositivos móviles y aplicaciones en tiempo real. Abstract. An alternative color difference formulas are presented for measuring the perceived difference between two color samples defined in YIQ color space.