christine fernandez - maloigne - advanced color image processing and analysis

Christine Fernandez-Maloigne Editor Advanced Color Image Processing and Analysis 123 Editor Christine Fernandez-Maloigne Xlim-SIC Laboratory University of Poitiers 11 Bd Marie et Pierre Curie Futuroscope France ISBN 978-1-4419-6189-1 ISBN 978-1-4419-6190-7 (eBook) DOI 10.1007/978-1-4419-6190-7 Springer New York Heidelberg Dordrecht London Library of Congress Control Number: 2012939723 © Springer Science+Business Media New York 2013 This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer Permissions for use may be obtained through RightsLink at the Copyright Clearance Center Violations are liable to prosecution under the respective Copyright Law The use of general descriptive names, registered names, trademarks, service marks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made The publisher makes no warranty, express or implied, with respect to the material contained herein Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Preface Color is life and life is color! We live our life in colors and the nature that surrounds us offers them all, in all their nuances, including the colors of the rainbow Colors inspire us to express our feelings We can be “red in the face” or “purple with rage.” We can feel “blue with cold” in winter or “green with envy,” looking at our neighbors’ new car Or, are we perhaps the black sheep of our family? Color has accompanied us through the mists of time The history of colors is indissociable, on the cultural as well as the economic level, from the discovery of new pigments and new dyes From four or five at the dawn of humanity, the number of dyes has increased to a few thousands today Aristotle ascribed color and light to Antiquity At the time, there was another notion of the constitution of colors: perhaps influenced by the importance of luminosity in the Mediterranean countries, clearness and darkness were dominating concepts compared to hues Elsewhere, colors were only classified by their luminosity as white and black Hues were largely secondary and their role little exploited It should be said that it was rather difficult at that time to obtain dyes offering saturated colors During the Middle Ages, the prevalence of the perception of luminosity continued to influence the comprehension of color, and this generally became more complicated with the theological connotations and with the dual nature of light declining in Lumen, the source of light of divine origin (for example, solar light) and Lux, which acquires a more sensory and perceptual aspect like the light of a very close wood fire, which one can handle This duality is included in the modern photometric units where lumen is the unit that describes the flow of the source of light and Lux is the unit of illumination received by a material surface This design based on clearness, the notion taken up by the painters of the Renaissance as well under the term of value, continues to play a major role, in particular for graphic designers who are very attached to the concept of the contrast of luminosity for the harmony of colors In this philosophy, there are only two primary colors, white and black, and the other colors can only be quite precise mixtures of white and black We can now measure the distance that separates our perception from that of the olden times v vi Preface Each color carries its own signature, its own vibration its own universal language built over millennia! The Egyptians of Antiquity gave to the principal colors a symbolic value system resulting from the perception they had of natural phenomena in correlation with these colors: the yellow of the sun, the green of the vegetation, the black of the fertile ground, the blue of the sky, and the red of the desert For religious paintings, the priests generally authorized only a limited number of colors: white, black, the three basic colors (red, yellow and blue), or their combinations (green, brown, pink and gray) Ever since, the language of color has made its way through time, and today therapeutic techniques use colors to convey this universal language to the unconscious, to open doors to facilitate the cure In the scientific world, although the fundamental laws of physics were discovered in the 1930s, colorimetrics had to await the rise of data processing to be able to use the many matrix algebra applications that it implies In the numerical world, color is of vital importance, as it is necessary to code and to model, while respecting the basic phenomena of the perception of its appearance, as we recall in Chaps and Then color is measured numerically (Chap 3), moves from one peripheral to another (Chap 4), is handled (Chaps 5–7), to extract automatically discriminating information from the images and the videos (Chaps 8–11) to allow an automatic analysis It is also necessary to specifically protect this information, as we show in Chap 12, to evaluate its quality, with the metrics and standardized protocols described in Chap 13 It is with the two applications in which color is central, the field of art and the field of medicine, that we conclude this work (Chaps 14 and 15), which has brought together authors from all the continents Whether looked at as a symbol of joy or of sorrow, single or combined, color is indeed a symbol of union! Thanks to it, I met many impassioned researchers from around the world who became my friends, who are like the members of a big family, rich in colors of skin, hair, eyes, landscapes, and emotions Each chapter of this will deliver to you a part of the enigma of digital color imaging and, within filigree, the stories of all these rainbow meetings Good reading! Contents Fundamentals of Color M James Shyu and Jussi Parkkinen CIECAM02 and Its Recent Developments Ming Ronnier Luo and Changjun Li 19 Colour Difference Evaluation Manuel Melgosa, Alain Tr´ meau, and Guihua Cui e 59 Cross-Media Color Reproduction and Display Characterization Jean-Baptiste Thomas, Jon Y Hardeberg, and Alain Tr´ meau e 81 Dihedral Color Filtering 119 Reiner Lenz, Vasileios Zografos, and Martin Solli Color Representation and Processes with Clifford Algebra 147 Philippe Carr´ and Michel Berthier e Image Super-Resolution, a State-of-the-Art Review and Evaluation 181 Aldo Maalouf and Mohamed-Chaker Larabi Color Image Segmentation 219 Mihai Ivanovici, Noă l Richard, and Dietrich Paulus e Parametric Stochastic Modeling for Color Image Segmentation and Texture Characterization 279 Imtnan-Ul-Haque Qazi, Olivier Alata, and Zoltan Kato 10 Color Invariants for Object Recognition 327 Damien Muselet and Brian Funt 11 Motion Estimation in Colour Image Sequences 377 Jenny Benois-Pineau, Brian C Lovell, and Robert J Andrews vii viii Contents 12 Protection of Colour Images by Selective Encryption 397 W Puech, A.G Bors, and J.M Rodrigues 13 Quality Assessment of Still Images 423 Mohamed-Chaker Larabi, Christophe Charrier, and Abdelhakim Saadane 14 Image Spectrometers, Color High Fidelity, and Fine-Art Paintings 449 Alejandro Rib´ s e 15 Application of Spectral Imaging to Electronic Endoscopes 485 Yoichi Miyake Index 499 Chapter Fundamentals of Color M James Shyu and Jussi Parkkinen The color is the glory of the light Jean Guitton Abstract Color is an important feature in visual information reaching the human eye or an artificial visual system The color information is based on the electromagnetic (EM) radiation reflected, transmitted, or irradiated by an object to be observed Distribution of this radiation intensity is represented as a wavelength spectrum In the standard approach, color is seen as human sensation to this spectrum on the wavelength range 380–780 nm A more general approach is to manage color as color information carried by the EM radiation This modern approach is not restricted to the limitations of human vision The color can be managed, not only in a traditional three-dimensional space like RGB or L∗ a∗ b∗ but also in an n-dimensional spectral space In this chapter, we describe the basis for both approaches and discuss some fundamental questions in color science Keywords Color fundamentals • Color theory • History of color theory • Colorimetry • Advanced colorimetry • Electromagnetic radiation • Reflectance spectrum • Metamerism • Standard observer • Color representation • Color space • Spectral color space • n-dimensional spectral space • Color signal • Human vision • Color detection system M.J Shyu ( ) Department of Information Communications, Chinese Culture University, Taipei, Taiwan e-mail: mjshyu@faculty.pccu.edu.tw J Parkkinen School of Computing, University of Eastern Finland, Joensuu, Finland School of Engineering, Monash University Sunway Campus, Selangor, Malaysia e-mail: jussi@monash.edu C Fernandez-Maloigne (ed.), Advanced Color Image Processing and Analysis, DOI 10.1007/978-1-4419-6190-7 1, © Springer Science+Business Media New York 2013 M.J Shyu and J Parkkinen 1.1 Everything Starts with Light The ability of human beings to perceive color is fantastic Not only does it make it possible for us to see the world in a more vibrant way, but it also creates the wonder that we can express our emotions by using various colors In Fig 1.1, the colors on the wooden window are painted with the meaning of bringing prosperity In a way, we see the wonderful world through the colors as a window There are endless ways to use, to interpret, and even to process color with the versatility that is in the nature of color However, to better handle the vocabulary of color, we need to understand its attributes first How to process as well as analyze color images for specific purposes under various conditions is another important subject which further extends the wonder of color In the communication between humans, color is a fundamental property of objects We learn different colors in our early childhood and this seems to be obvious for us However, when we start to analyze color more accurately and, for example, want to measure color accurately, it is not so obvious anymore For accurate color measurement, understanding, and management, we need to answer the question: What is color? Fig 1.1 A colorful window with the theme of bringing prosperity (Photographed by M James Shyu in Pingtong, Taiwan) Fundamentals of Color In a common use of the term and as an attribute of an object, color is treated in many ways in human communication Color has importance in many different disciplines and there are a number of views to the color: in biology, color vision and colorization of plants and animals; in psychology, color vision; in medicine, eye diseases and human vision; in art, color as an emotional experience; in physics, the signal carrying the color information and light matter interaction; in chemistry, the molecular structure and causes of color; in technology, different color measuring and display systems; in cultural, studies color naming; and in philosophy, color as an abstract entity related to objects through language [2, 9, 28] It is said that there is no color in the light—to quote Sir Isaac Newton, “For the Rays to speak properly are not coloured In them there is nothing else than a certain Power and Disposition to stir up a Sensation of this or that Colour” [21, 26] It is the perception of human vision that generates the feeling of color It is the perceived color feeling of the human vision defining how we receive the physical property of light Nevertheless, if color is only defined by human vision, it leaves all other animals “color blind.” However, it is known that many animals see colors and have an even richer color world than human being [13, 19] The new technological development in illumination and in camera and display technology requires new way of managing colors RGB or other three-dimensional color representations are not enough anymore The light-emitting diodes (LED) are coming into illumination and displays rapidly There, the color radiation spectrum is so peaky that managing it requires a more accurate color representation than RGB There exist also digital cameras and displays, where colors are represented by four or six colors Also this technology requires new ways to express and compute color values Therefore, if we want to understand color thoroughly and be able to manage color in all purposes, where it is used today, we cannot restrict ourselves to the human vision We have to look color through the signal, which causes color sensation by humans This signal we call color signal or color spectrum 1.2 Development of Color Theory In color vocabulary, black and white are the first words to be used as color names [2] After them when the language develops, come red and yellow The vocabulary is naturally related to the understanding of nature Therefore in ancient times, the color names were related to the four basic elements of the world, water, air, fire, and earth [9] In ancient times, the color theory was developed by philosophers like Plato and Aristotle For the later development of color theory, it is notable that white was seen as a basic color Also the color mixtures were taken into theories, but each basic color was considered to be a single and separate entity [14] Also from the point of view of the revolution of color theory by Newton [20], it is interesting to note that Aristotle had a seven basic color scale, where colors crimson, M.J Shyu and J Parkkinen a 0.9 0.8 B 0.7 G 0.6 R 0.5 Y 0.4 M 0.3 C 0.2 0.1 380 430 480 530 580 630 680 730 b Fig 1.2 (a) A set of color spectra (x-axis: wavelength from 380 to 730 nm, y-axis: reflectance factor) and (b) the corresponding colors violet, leek-green, deep blue, and gray or yellow formed the color scale from black to white [9] Aristotle also explains the color sensation so, that color sets the air in movement and that movement extends from object to the eye [24] From these theories, one can see that already in ancient times, there exists the idea of some colors to be mixtures of primary colors and seven primary colors Also, it is easy to understand the upcoming problems of Newton’s description of colors, when the view was that each primary color is a single entity and the color sensation was seen as a kind of mechanical contact between light and the eye The ancient way of thinking was strong until the Seventeenth century In the middle of the Seventeenth century, the collected information was enough to break the theory of ancient Greek about light and color There were a number of experiments by prism and color in the early Seventeenth century The credit for the discovery of the nature of light as a spectrum of wavelengths is given to Isaac Newton [20] The idea that colors are formed as a combination of different component rays, which are immaterial by nature, was revolutionary at Newton’s time It broke the strong influence of ancient Greek thinking This revolutionary idea was not easily accepted A notable person was Johann Wolfgang von Goethe, who was still in the Nineteenth century opposing Newton’s theory strongly [10] Newton also presented colors in a color circle In his idea, there were seven basic colors: violet, indigo, blue, green, yellow, orange, and red [14] In the spectral approach to color as shown in Fig 1.2, the wavelength scale is linear and continuing Index Clifford algebras colour Fourier transform bivectors, 175–178 Clifford Fourier transforms, 165–166 definition, 162, 171–172 generalization, 161 group actions, 161 mathematical viewpoint, 166–167 nD images, 160 numerical analysis, 162–165 properties, 172–175 rotation, 167–168 R4 rotations, 169–170 spin characters, 168–169 usual transform, 170–171 description, 148 spatial approach colour transform, 154–156 definition, 152–154 quaternion concept, 148–152 spatial filtering, 156–160 Closed-loop segmentation, 262–263 CMM See Color management module (CMM) CMS See Color management systems (CMS) Color appearance model (CAM) See CIECAM02 Color-based object recognition advantages, 330 description, 331 discriminative power and invariance, 330–331 goal, 328–329 invariance (see Color invariants) keypoint detection (see Keypoint detection) local region descriptors (see Scale-invariant feature transform (SIFT) descriptor) Color detection system humans/physical signal, 14 object, 14 reaching, 15 wavelength sensitive sensors, 15, 16 Color filter array (CFA) Bayer CFA, 453, 454 CCD/CMOS, 454 demosaicing algorithms, 454 high-fidelity capabilities, 454 single matrix sensor, 453 Color fundamentals light accurate measurement, compute color values, 3, human communication and vision, RGB and LED, theme, bringing prosperity, 501 linear and non-linear scales, 13–14 metamerism, 10–11 physical attributes artificial, vision systems, coordinate system and PCA, definition, detector response Di and system, low dimensional color representation, measurement and illumination, n-dimensional vector space, objects and human visual system, 6, reflectance spectrum r(λ ) and vector space approach, sensitive cone-cells, spectral approach, linear models, physical property measurement densitometry vs colorimetry, 12 density, spectral conditions defines, 11 fundamental colorimetry, 12 narrow-band density, 11–12 printing and publishing, 11 reflectance density (DR) and densitometers, 11 simultaneous contrast effect, 12 spectral matching method and colorimetric matching, 12 representation, 8–9 theory, 3–6 Color high fidelity and acquisition systems CFA (see Color filter array (CFA)) dispersing devices, 458–460 Foveon X3, 456–457 multi-sensor, 455–456 sequential, 457–458 bibliography, art paintings, 451 color reproduction, 450 digital image, 450 image-acquisition system model basic model, 461–462 3D geometry, 460 imaged object, 463–465 radiant energy, 460 imaging fine art paintings, 469–479 spectral acquisition and reconstruction classification (see Spectral reflectance) integral equation, 465–466 Color image segmentation analysis, goal, 292 application, point of view, 222 approaches active contours, 252–254 graph-based, 254–259 JSEG, 246–251 502 Color image segmentation (cont.) pyramidal, 241–244 refinement, criteria, 241 watershed, 245–246 wide spectrum, 241 Bayesian model, 289 Berkeley data set, 302–303 classes, 221 clique potentials VC and MAP estimation, 291 color gradient and distances, adjacent pixels, 230–231 computer vision, 220 definition, 220 distances and similarity measures, 226–230 evolution, 221–222 features, 231–240 formalisms, 223–224 frameworks, 269 Gaussian mixture parameters and RJMCMC, 290 Gestalt-groups, 270 Gibbs distribution, 291, 292 gray-scale images, 221 Hammersley–Clifford theorem, 291 homogeneity, 225–226 human vision, 270 informative priors and doubletons, 293 joint distribution and probability, 292 JSEG, 301 label process., 290–291 neighborhoods, 224–225 observation and probability measure, 290 optimization, MAP criterion, 300 paths, 220 performance evaluation closed-loop segmentation, 262–263 image-based quality metrics, 267–269 open-loop process, 260 semantical quality metrics, 264–266 supervised segmentation, 263–264 validation and strategies, 260–262 pixel class and singleton potential, 292 posterior distribution acceptance probability, 299–300 class splitting, 297–298 hybrid sampler, 294 merging, classes, 298–299 Metropolis–Hastings method, 294 move types and sweep, 294 reversible jump mechanism, 295–296 precision–recall curve, JSEG and RJMCMC, 301, 302 pseudo-likelihood, 293 Index quantities, 300 RJMCMC, 301–302 rose41, 301, 302 techniques-region growing and edge detection, 221–222 TurboPixel/SuperPixels, 269 vectorial approaches, 222 Color imaging See Spectral reflectance Colorimetric characterization cross-media color reproduction, 82–90 device (see Colorimetric device characterization) display color (see Display color characterization) intelligent displays, 114 media value and point-wise, 113 model inversion, 104–108 quality evaluation color correction, 112–113 combination needs vs constraints seem, 109–110 forward model, 110 image-processing technique, 109–110 time and measurement, 109 quantitative evaluation accurate professional color characterization, 111–112 average and maximum error, 112 Δ E∗ ab thresholds, color imaging devices, 111 JND, 111–112 Colorimetric device characterization calibration process and color conversion algorithm, 84 CIEXYZ and CIELAB color space, 85 description, 84 first rough/draft model, 85 input devices digital camera and RGB channels, 87 3D look-up tables, 87 forward transform and spectral transmission, 86 linear relationship and Fourier coefficients, 86 matrix and LUT, 86 physical model, 86 scanner and negative film, 87 spectral sensitivity/color target based, 87 transform color information, 86 numerical model and physical approach, 84–85 output devices, 87–88 Index Colorimetry color matching functions, 8, densitometry and matching, 12 Color-invariant features (CIF) definition, labels, 354, 355 invariance properties and assumptions, 353, 355, 356 spectral derivatives, 347–348 Color invariants description, 331 distributions, normalizations moments, 346 rank measure, 345 transformation, 345–346 features (see Color-invariant features (CIF)) Gaussian color model CIF, 347–348 Lambertian surface reflectance, 348–350 matte surface, 348 transformation, RGB components, 347 intra-and inter-color channel ratios (see Color ratios) spatial derivatives description, 350–351 highlight and shadow/shading invariance, 352–353 shadow/shading and quasi-invariance, 351–352 specular direction, 351 vectors, 351 surface reflection models and color-image formation description, 332 dichromatic, 333 illumination (see Illumination invariance) Kubelka-Munk, 333 Lambertian, 332 properties, 333–334 sensitivities, 334–335 Color management module (CMM), 83 Color management systems (CMS), 83 Color ratios narrowband sensors and illumination Lambertian model, 339–340 matte surface, 340–341 single pixel Lambertian model, narrowband sensors and blackbody illuminant, 343–344 matte surface and ideal white illumination, 342–343 503 neutral interface reflection, balanced sensors, and ideal white illumination, 341–342 Color representation implicit model, 8–9 light source, object and observer, matching functions, CIE, tristimulus functions, Color reproduction theory BRDF, 487 electronic endoscope, 488 gastric mucosa, 490 image recording, 487 spectral endoscope, 488, 489 spectral reflectance, 489 spectral transmittance and sensitivity, 488 vector equation, 488 Color sets, 234 Color signal definition, 17 detector response, linear models, spectral approach, 14 spectrum, wavelength sensitive sensors, 15–16 Color space, linear and non-linear scales CIELAB, 13–14 CIELUV, 13 colorful banners, 14, 15 gray scales, physical and perceptual linear space, 13 mathematical manipulation, 13 measurement, physical property, 13 Color structure code (CSC) hexagonal hierarchical island structure, 243 segmentation, test images, 244 Color texture classification average percentage, 314, 315 bin cubes, 3D histograms, 312 data sets (DS), vistex and outex databases, 309 distance measures, 310–311 IHLS and L*a*b*, 313 KL divergence, 312 LBP, 314 luminance and chrominance spectra, 313–314 probabilistic cue fusion, 311–312 RGB results, 313 spatial distribution, 313 test data sets DS2 and DS3 , 315 Color texture segmentation class label field, ten images simulations, 317 504 Color texture segmentation (cont.) label field estimation, 316–317 LPE sequence, 315–316 mathematical models, 321 mean percentages, pixel classification errors, 319, 321 Potts model, 318 results with and without spatial regularization, 318–319 three parametric models, LPE distribution results, 319, 320 vistex and photoshop databases, 317 Color theory circle form, 4–5 coordinates/differences, 5–6 human retina, sensitive cells, mechanical contact, light and the eye, physical signal, EM radiation, revolution, 3–4 sensation and spectrum, wavelengths, trichromatic theory, human color vision, vocabulary, Young—Helmholz theory, Colour appearance attributes brightness (Q) and colourfulness (M), 26 chroma (C) and saturation (s), 27 hue (h and H), 28 lightness (J), 26 Colour difference evaluation advanced colorimetry, 63 anchor pair and grey scale method, 61 appearance and matching, 63 CIE, 17 colour centers proposed, 60 CIEDE2000, 76 complex images, 75–76 formulas (see Colour-difference formulas) intra and inter-observer variability, 61 parametric effects and reference conditions, 60 relationship, visual vs computed PF/3, 72–73 STRESS, 73–75 subjective (Δ V) and objective (Δ E) colour pairs, 71 visual vs instrumental, 62 Colour-difference formulas advanced appearance model, CIECAM02, 68 Berns’ models and Euclidean colour spaces, 71 chroma dependency, 71 CIECAM02’s chromatic adaptation transformation, 70 CIE 1964 chromaticity coordinates, 69 Index CIELAB L∗, a∗, b∗ coordinates, 67–68 compressed cone responses and linear transformation, 70–71 DIN99 and DIN99d, 67 exponential function, nonlinear stage, 70 multi-stage colour vision theory and line integration., 70 OSA-UCS and Euclidean, 69 CIE CIEDE20002, 64–65 CIELUV and CIELAB colour spaces, 63–64 description, 63 reference conditions and CMC, 64 U*,V*,W* colour space, 63 definition, 61 Colour image protection, SE AES, 399 CFB, 399 cryptanalysis and computation time, 417–419 DRM, 398 encryption system, 399–403 and JPEG compression and block bits, ratio, 410, 412 Lena image, 410 PSNR, crypto-compressed Lena image, 411, 413 QF, 410, 411 mobile devices, 419 motion estimation and tracking, 420 multimedia data, 398 proposed method Huffman coding stage, 403 image decryption, 408–409 image sequences, 404 proposed methodology, 404 quantified blocks, JPEG, 405–408 ROI, chrominance components, 404–405 ROI, colour images, 413–417 visual cryptography, 398 VLC, 399 Contrast sensitivity function (CSF), 250–251 Contrast sensitivity functions, 437 COSO See Center-on-surround-off (COSO) Cross-media color reproduction complex and distributed, 82–83 defining, dictionaries, 83 description, 82 device colorimetric characterization, 84–88 gamut considerations, 88–90 Index languages and dictionaries, 83 management systems, 83–84 CSC See Color structure code (CSC) CSF See Contrast sensitivity function (CSF) D DCT See Discrete cosine transform (DCT) DFT See Discrete Fourier transform (DFT) Digital rights management (DRM), 398 Dihedral color filtering computational efficiency, 144 DFT, 120 EVT (see Extreme value theory (EVT)) group theory, 121–125 illustration image size 192 × 128, 125 line and edge, 125, 127 original and 24 magnitude filter images, 125, 126 original image and 48 filter results, 125, 126 image classification accuracy, various filter packages, 139 andy warhol–claude monet and garden–beach, 139 collections, 138–139 entire descriptor, 139, 140 EVT and histogram, andy warhol– claude monet set, 140, 143 packages, 139–143 SVM-ranked images resulting, 141, 144 linear, 127–128 MMSE and re-ranking and classification, 120 principal component analysis correlation and orthonormal matrix, 129 intertwining operator, 129 log diagonal, second-order moment matrices, 129–130, 131 second-order moment matrix, 129, 130 structure, full second-order moment matrices, 130, 132 three-parameter extreme-value distribution model, 144 transforms, orientation, and scale blob-detector and space, 137 denoting and rotating, 136 diagonal elements and operation, 137–138 edge magnitude, 136, 137 four-and eight-point orbit, 135 group theoretical tools, 136 operating, RGB vectors, 135 505 orthonormal and norm, vectors, 136 vector components and polar coordinates, 136 Dihedral groups definition and description, 121 Dn , n-sided regular polygon, 121 D4 , symmetry transformations, 121 DIN99 DIN99d, 67 logarithmic transformation, 67 Discrete cosine transform (DCT), 400 Discrete Fourier transform (DFT) FFT, 135 integer-valued transform, 135 Discrete Quaternionic Fourier Transform (DQFT), 162–163 Dispersing devices diffraction and reflection grating, 459 optical prisms, 458 pixel interval, 459, 460 spatial and spectral dimensions, 460 Display color characterization classification, 91 description, 91 3D LUT models, 91–92 numerical models, 92 physical models colorimetric transform, 98–102 curve retrieval, 95–98 PLVC, 102–104 subtractive case, 94 Distances Bhattacharyya distance, 229 Chebyshev distance, 228 color-specific, 227–228 EMD, 230 Euclidean distance, 228 Hamming distance, 228 Hellinger distance, 230 KL, 230 Kolmogorov–Smirnov distance, 230 Mahalanobis distance, 229 Minkowski distance, 229 3-D LUT models, 91–92 DQFT See Discrete Quaternionic Fourier Transform (DQFT) DRM See Digital rights management (DRM) 3-D scalar model, 286 E Earth mover’s distance (EMD), 230 ECB See Electronic code book (ECB) 506 Index Electromagnetic (EM) radiation physical properties, physical signal, Electronic code book (ECB) CFB modes, 402 IV, 401 Electronic endoscopy color reproduction theory (see Color reproduction theory) dispersion, visible light, 486–487 electromagnetic waves, light, 486 spectral image, 492–495 spectral reflectance, 490–492 trichromatic theory, 486 EM algorithm See Expectationmaximization (EM) algorithm EMD See Earth mover’s distance (EMD) EM radiation See Electromagnetic (EM) radiation End of block (EOB), 401 Endoscope spectroscopy system color reproduction (see Color reproduction theory) configuration and photograph, 488, 489 FICE spectral image processing, 495, 496 EOB See End of block (EOB) Evaluation methods, 425 EVT See Extreme value theory (EVT) Expectationmaximization (EM) algorithm, 283 Extreme value theory (EVT) accumulator and stochastic processes, 131 and 3-parameter Weibull clusters, 133–134 black box, 130–131 distribution families, 132 image type and model distribution, 133 mode, median, and synthesis, 134 original image, edge filter result, and tails (maxima), 134 Flexible spectral imaging color enhancement (FICE) esophageal mucosa, 494 image of gullet, 495 observation wavelengths and rapid switching, 495 pre-calculated coefficients, 493 spectral images, gastric mucosa, 493 wavelengths, 493, 494 FLIR See Forward-looking infrared (FLIR) Forward-looking infrared (FLIR), 433 Fourier transform Clifford colour, spin characters colour spectrum, 175–178 definition, 171–172 properties, 172–175 usual transform, 170–171 mathematical background characters, abelian group, 167 classical one-dimensional formula, 166–167 rotation, 167–168 R4 rotations, 169–170 Spin characters, 168–169 quaternion/Clifford algebra Clifford Fourier transforms, 165–166 constructions, 161 generalizations, 161 numerical analysis, 162–165 quaternionic Fourier transforms, 161 Fractal features box, 239 CIELab color space, 240 correlation dimension, 240 Euclidian distance, 239 gray-level images, 239 Hausdorff and Renyi dimension, 238–239 measure, dimension, 238 pseudo-images, 240 RGB color space, 239 F Fast Fourier transform (FFT), 135, 309, 310 Features color distribution, 232–234 fractal features, 238–240 spaces, 232 texture features, 234–238 texture level, regions/zones, 231 FFT See Fast Fourier transform (FFT) FICE See Flexible spectral imaging color enhancement (FICE) G Gain-offset-gamma (GOG) model, 97 Gain-offset-gamma-offset (GOGO) model, 101 Gamut mapping CIELAB, 89 optimal, definition, 90 quality assessment, 89 spatial and categorization, 89 Gaussian Markov Random field (GMRF) 3-D, 286 multichannel color texture characterization, 284 Index Gaussian law and estimation, 285 linear relation, random vectors, 285 parameters, matrices, 285 variance matrix, 285 Gauss–Siedel iterative equations, 380 GMRF See Gaussian Markov Random field (GMRF) GOG model See Gain-offset-gamma (GOG) model GOGO model See Gain-offset-gamma-offset (GOGO) model Gradient vector flow (GVF) approaches, 252 Graph-based approaches directed edge, 254 disjoint subsets S and T, 254 edges weighting functions, types, 255, 256 Gaussian form, 256 graphcut formulation, 256, 257 graph problem, 255 initial formalism, 255 initial graph cut formulation, 258 λ parameter, 258–259 Mincut, 255 segmentation process, 254 sink node, 258, 260 σ value, 256–257 terminal nodes, 254 GVF See Gradient vector flow (GVF) H Hammersley–Clifford theorem, 284, 291 HDTV displays, 182, 185–186 Helmholtz-Kohlrausch effect, 35 Helson-Judd effect, 36 History, color theory, 3–6 Homogeneity, 225–226 Huffman coding AES, CFB mode, 408 CFB stream cipher scheme, 402, 405 construction, plaintext cryptographic hashing, 407 frequency ordering, 406–407 visual characteristics, 406 DCT coefficients, ROI, 409 proposed SE method, 406 ROI detection, 403 substitution, bitstream, 408 Human vision definition, medicine eye diseases, powerful tool, manage color, 17 traditional color, Human visual system (HVS) 507 HVS-based detection, 361–362 perceptual quality metrics, 437 psychophysical experiments, 425 Hunt effect, 34–35 HVS See Human visual system (HVS) Hyperspectral imaging fiber optics reflectance spectroscopy, 475 motorized structure, 476 transmission grating, 475, 476 I ICM See Iterated conditional mode (ICM) IFC See Image fidelity criterion (IFC) Illumination invariance chromaticity, 336 constant relative SPD, 335 diagonal model, 336–337 ideal white, 336 linear and affine transformation, 337–338 monotonically increasing functions, 338 neighboring locations, 335–336 Planckian blackbody, 335 Image fidelity criterion (IFC), 433 Image quality assessment fidelity measurement, 425 HVS, 445 objective measures error visibility, 434–437 low-complexity measures, 431–434 perceptual quality metrics, 437–440 structural similarity index, 440 subjective quality, 440 performance evaluation correlation analysis, 441 metrics, Kappa test, 443–444 outliers ratio, 443 Pearson’s correlation coefficient, 442 RMSE, 440–441 scatter plots, linear correlation, 441 Spearman rank order correlation, 442–443 statistical approaches, 444 prediction monotonicity, 445 sensory and perceptive processes, 425 subjective measurements absolute measure tests, 429 categorical ordering task, 428 comparative tests, 427–429 experimental duration, 427 forced-choice experiment, 427, 428 instructions, observer, 427 MOS calculation and statistical analysis, 429–430 508 Image quality assessment (cont.) observer’s characteristics, 426 stimulus properties, 426 viewing conditions, 427 types, measurement, 424 Image re-ranking and classification, 120 Image spectrometers See Color high fidelity Image super-resolution HDTV displays, 182 interpolation-based methods (see Interpolation-based methods) learning-based methods (see Learningbased methods) MOS values (see Mean opinion scores (MOS)) objective evaluation, 211 reconstructed-based methods, 202–204 subjective evaluation environment setup, 208–210 MOS, 208 procedure, 210–211 scores processing, 211 test material, 208 Initialization vector (IV), 401, 408 International organization for standardization (ISO) digital compression subjective testing, 426 subjective measurements, 445 International telecommunication union (ITU) digital compression subjective testing, 426 image quality assessment, 445 Interpolation-based methods adjacent and nonadjacent pixels, 188 color super-resolution, problem, 197 conserves textures and edges, 201 corner pixel, 188 COSO filter, 187 covariance, 192–193 duality, 192 edge-directed interpolation method architecture, 187 framework, 186–187 edge model, 183–184 Euler equation, 200 factor, 185–186 geometric flow, 198, 199 high-resolution pixel m, 188 imaging process, 195 initial estimate image, 197 LOG, 187 low and high resolution, 182–183 NEDI algorithm, 192, 194 operators, 184, 185 Index Original Lena and Lighthouse image, 188, 189 pixels, high-resolution image, 182–183 problems, 183 rendering, 187 structure tensor, 199 super-resolution process, 196 use, 183 variational interpolation approach, 200 wavelet transform, 189–191 Intertwining operator, 129 Inverse model description, 104 indirect CMY color space, 106 cubic voxel, tetrahedra, 107 definition, grid, 107–108 3-D LUT and printer devices, 106 forward and analytical, 105–106 PLVC and tetrahedral structure, 107–108 transform RGB and CIELAB, 106, 107 uniform color space, 105 uniform mapping, CMY and nonuniform mapping, CIELAB space, 106, 107 practical, 105 IPT Euclidean colour space IPT-EUC, 71 transformation, tristimulus values, 70 ISO See International organization for standardization (ISO) Iterated conditional mode (ICM), 318 ITU See International telecommunication union (ITU) IV See Initialization vector (IV) J JND See Just noticeable difference (JND) JPEG compression AES encryption algorithm CBC, 401 CFB stream cipher scheme, 401, 402 ECB, 401 OFB, 401 PE, 402 zigzag permutation, 403 algorithm DCT, 400 EOB, 400–401 Huffman coding block, 400 ZRL, 401 classical ciphers, 399 confidentiality, 399–400 Index Lena image, 410 PSNR, Lena image, 411, 413 QF, 410, 411 ratio, SE and block bits, 410, 412 JSEG accuracy, 321 based segmentation, 246, 247 CSF, 250–251 Dombre proposes, 249–250 images, various resolutions and possible segmentation, 222 J-criterion, 246–247 post and pre-processing, 248 predefined threshold, 246 quantization parameter and region merging threshold, 248 vs RJMCMC, 301, 302 valleys, 246 watershed process, 249 Just noticeable difference (JND), 111–112 509 K Keypoint detection description, 354 Harris detector complexity, 358 discriminative power, 358 Moravec detector, 357 repeatability, 358 Harris-Laplace and Hessian-Laplace, 359–360 Hessian-based detector, 358–359 HVS, 361–362 key-region detection hierarchical segmentation, 361 MSER, 360–361 learning, detection attention, 362 information theory, 363 object-background classification approach, 363 quality criteria, detectors, 356–357 KL divergence See Kullback–Leibler (KL) divergence Kullback–Leibler (KL) divergence, 281, 312 estimation process, 206 MAP high-resolution image, 206 Markov Random Field model, 207 super-resolution, 204–205 Linear filtering L-tupel, 127 pattern space division, 128 properties, 128 Riesz representation theorem, 127 steerable, condition, 128 Linear prediction error (LPE), 315–316 Linear prediction models MSAR, 289 multichannel/vectorial AR, 288 complex vectors, 286 2-D and neighborhood support regions, 287 different estimations, PSD, 289 HLS color space, 286–287 MGMRF, 288–289 PSD, 287–288 spectral analysis color texture classification, 309–315 IHLS and L*a*b*, 304–309 segmentation, color textures, 315–321 Liquid crystal tunable filters (LCTF), 458 Local binary patterns (LBP), 314 Local region descriptors See Scale-invariant feature transform (SIFT) descriptor LOG See Laplacian-of-Gaussian (LOG) Look-up table (LUT) 1D, 104, 105 3D, 91–92 matrix, 86 Low complexity metrics FLIR, 433 IFC, 433 image quality, 432 NCC, 434 PSNR, 431, 445 SPD, 433 vision system, 433 Low-level image processing, 120 LPE See Linear prediction error (LPE) LUT See Look-up table (LUT) L Laplacian-of-Gaussian (LOG), 187 LBP See Local binary patterns (LBP) Learning-based methods Bayes rules, 207 database, facial expressions, 205 M MAP See Maximum a posteriori (MAP) Markov random fields (MRF) model Gibbs distribution and Hammersley– Clifford theorem, 284 and GMRF, 284–286 510 Markov random fields (MRF) model (cont.) learning-based methods, 207 reflexive and symmetric graph, 284 Masking effects activity function, 434 HVS, 436, 445 limb, 435 Maximally stable extremal region (MSER), 360–361 Maximum a posteriori (MAP) approach, super-resolution image, 202 criterion, 300 estimates, 290, 291, 316 Maximum likelihood (ML) approach, 203 Maximum likelihood estimation (MLE), 283 Mean opinion scores (MOS) Lighthouse, Caster, Iris, Lena and Haifa acquisition condition, 212, 214 color and grayscale, 212, 214 Pearson correlation coefficient, 216 PSNR results, 212, 215 scatter plots, 216 SSIM results, 212, 215 subjective scores, 212, 213 raw subjective scores, 211 Mean squared error (MSE) low-complexity metrics, 445 PSNR use, metrics and calculation, 211 quality assessment models, 434 reference image, 432 signal processing., 431 Metamerism color constancy, 11 color property and human visual system, 10 computer/TV screen, 10 description, 10, 453 reflectance curves, specific illumination, 10 reproduced and original colour, 453 Retinex theory, 11 textile and paper industry, 10 trichomatric imaging, 453 Metropolis–Hastings method, 294 MGD See Multivariate Gaussian distribution (MGD) MGMM See Multivariate Gaussian mixture models (MGMM) Minimum mean squared error (MMSE), 120 ML See Maximum likelihood (ML) MLE See Maximum likelihood estimation (MLE) mLUT See Multidimensional look-up table (mLUT) MMSE See Minimum mean squared error (MMSE) Index MOS See Mean opinion scores (MOS) MOS calculation and statistical analysis calculation, confidence interval, 429–430 image and video processing engineers, 430 outliers rejection, 430 PSNR, 430 psychophysical tests, 429 RMSE, 430 Motion estimation data-fusion algorithm, 394 dense optical flow methods computation, 382 disregarding, 382 error analysis, 382 least squares and pseudo-inverse calculation, 382 results, 383–386 direct extension, 378 Golland methods, 378 neighborhood least squares approach, 393 optical flow “brightness conservation equation”, 379 Horn and Shunck, 379–380 Lucas and Kanade, 380 OFE, 379 problem, 379 Taylor expansion, 379 traditional methods, 380 psychological and biological evidence, 378 sparse optical flow methods block-based, colour wavelet pyramids, 389–393 large displacement estimation, 386–389 using colour images colour models, 381 Golland proposed, 381 standard least squares techniques, 380–381 MRF See Markov Random fields (MRF) MSAR model See Multispectral simultaneous autoregressive (MSAR) model MSE See Mean squared error (MSE) MSER See Maximally stable extremal region (MSER) Multiband camera arbitrary illumination, 474 CFA color, 475 image spectrometers, 457 Multichannel complex linear prediction models, 287, 309, 321 Multidimensional look-up table (mLUT), 88 Multispectral imaging See Spectral reflectance Multispectral simultaneous autoregressive (MSAR) model, 289 Index Multivariate Gaussian distribution (MGD) definition, 282 empirical mean and estimators, 283 LPE distribution, 316 Multivariate Gaussian mixture models (MGMM) approximation, color distribution, 303 color image segmentation, 289 components, 283 definition, 282–283 label field estimation, 316–317 probability density function, 282 RGB, 318, 319 Murray-Davies model, 88 N NCC See Normalized cross correlation (NCC) N-dimensional spectral space, Neighborhoods, pixel, 224–225 Neugebauer primaries (NP), 88 Normalized cross correlation (NCC), 434 NP See Neugebauer primaries (NP) Numerical models, 92 O OFB See Output feedback (OFB) OFE See Optical flow equation (OFE) Optical flow “brightness conservation equation”, 379 Horn and Shunck, 379–380 Lucas and Kanade, 380 OFE, 379 Taylor expansion, 379 traditional methods, 380 Optical flow equation (OFE), 379 Output feedback (OFB), 401, 402 P Parametric effects, 60 Parametric spectrum estimation, 307 Parametric stochastic models description, 280 distribution approximations color image, 282 EM algorithm and MLE, 283 Kullback–Leibler divergence, 281 measures, n-d probability, 281 MGD, 282 MGMM, 282–283 RJMCMC algorithm, 283 Wishart, 283–284 511 gray-level images, 281 HLS and E ⊂ Z2 pixel, 280 linear prediction MSAR, 289 multichannel/vectorial, 286–289 spectral analysis, 304–321 mixture and color image segmentation, 289–304 MRF and GMRF, 284–286 Partial encryption (PE), 402 PCA See Principal component analysis (PCA) PCC See Pearson correlation coefficient (PCC) PCS See Profile connection space (PCS) PDF See Probability density function (PDF) PE See Partial encryption (PE) Peak signal to noise ratio (PSNR) block-based colour motion compensation “Lancer Trousse” sequence, 392, 393 on wavelet pyramid, 392 error measures, 430 processing, image, 432 signal processing, 431 upper limits, interpretation, 432, 433 Pearson correlation coefficient (PCC), 216, 442 Perceptual quality metrics contrast masking broadband noise, 439 intra-channel masking, 438 Teo and Heeger model, 439 display model cube root function, 437 Weber-Fechner law, 438 error pooling, 437–438 HVS, 437 perceptual decomposition, 438 structural similarity index, 440 Permutation groups grid, 122 S(3), three elements, 121 PF/3 combined index, 72 decimal logarithm, γ , 72 definition, 72 eclectic index, 73 natural logarithms and worse agreement, 72 Physical models colorimetric characteristic, 95 colorimetric transform black absorption box and black level estimation, 101 chromaticity tracking, primaries, 98–100 CRT and LC technology, 98 filters and measurement devices, 102 512 Physical models (cont.) GOGO and internal flare, 101 linearized luminance and ambient flare, 98 PLCC* and S-curve, 101 curve retrieval CRT, channel function, 97 digital values input, 96–97 function-based, 96 GOG and Weber’s law, 97 PLCC, 98 S-curve I and S-curve II, 97 X,Y and Z, LCD display function, 96 displays, 93 gamma law, CRT/S-shaped curve, LCD, 93 LC technology and gamma, 95 luminance curve, 94 masking and modified masking model, 93 × matrix and PLCC, 93 PLVC, 93–94, 102–104 two-steps parametric, 94 white segment, 93 Piecewise linear-assuming chromaticity constancy (PLCC) models, 93–94, 98 Piecewise linear model assuming variation in chromaticity (PLVC) models dark and midluminance colors, 103 definition, 102 1-D interpolation method, 103 inaccuracy, 104 N and RGB primaries device, 103 PLCC, 94 tristimulus values, X,Y, and Z, 103 PLCC models See Piecewise linear-assuming chromaticity constancy (PLCC) models PLVC models See Piecewise linear model assuming variation in chromaticity (PLVC) models POCS See Projection onto convex sets (POCS) Potts model, 318 Power spectral density function (PSD) estimation methods chromatic sinusoids, IHLS color space, 304–305 chrominance channels, 306, 307 HM, IHLS and L*a*b* color spaces., 305 luminance channel, 305–306 noisy sinusoidal images, 304 Principal component analysis (PCA), Probability density function (PDF), 282 Profile connection space (PCS), 83 Projection onto convex sets (POCS), 204 Index PSNR See Peak signal to noise ratio (PSNR) Pyramidal segmentation algorithms, 264 structure, 241, 242 Q Quality factor (QF), 410, 412 Quality metric image-based alterations, 268 empirical function, 267 metric propose, 268 original F metric, 267–268 PAS metric, 268 quality metric, properties, 268 SCC, 269 semantical classical approach, 266 model-based recognition and graph matching, 265, 266 Quaternion definition, 148–149 quaternionic filtering, 150–152 R3 transformations, 149–150 R Radial basis function (RBF), 12 RAM See Rank agreement measure (RAM) Rank agreement measure (RAM), 269 RBF See Radial basis function (RBF) Reconstruction-based methods analytical model, 204 Bayes law, 202 constraints, error, 202 high-resolution images, 202 MAP approach, 202 ML approach, 203 POCS super-resolution reconstruction approach, 204 Red Green Blue (RGB), 306 Reflectance spectrum, Region and boundary-based segmentation, 221 definition, 220 Haralick and Shapiro state, guidelines, 224 histogram, 232 label image, 224 low and upper scale, 250 merging threshold, 248 Ri regions, 223, 225 Region adjacency graphs (RAGs) Regions of interest (ROI) Index colour images cryptography characteristics, 415, 416 face detection, 417 sequence, 414, 415 detection, chrominance components Huffman vector, 405 human skin, 404 Reversible jump Markov chain Monte Carlo (RJMCMC) algorithm, 283, 290 F-measure, 302–303 JSEG, 301, 302 segmentation, 300 Reversible jump mechanism acceptance probability., 295 detailed balance advantages, 296 condition, 295 equation, 296 diffeomorphism Ψ ανδ dimension matching, 296 Metropolis–Hastings method, 295 RGB See Red Green Blue (RGB) RJMCMC See Reversible jump Markov chain Monte Carlo (RJMCMC) RMSE See Root mean squared error (RMSE) Root mean squared error (RMSE) accurate prediction capability, 440 error measures, 430 S Scale-invariant feature transform (SIFT) descriptor concatenation scene classification, 364 types, histograms, 365 description, 364 parallel comparison AdaBoost classifier, 367 kernels, 368 MPEG-7 compression, 367 object tracking and auto-correlograms, 367 sequential combination, 366–367 spatio-chromatic concatenation, 368 spatial derivatives, 369 transformation, 369, 370 versions, 368 YCrCb color space, 369 S-CIELAB, 75–76 513 Segmentation quality metric (SQM), 221 Selective encryption (SE) See Colour image protection, SE Semantic gap, 261 Sequential acquisition AOTF (see Acousto-optic tunable filters (AOTF)) grayscale camera, 457 hyperspectral, 457 LCTF (see Liquid crystal tunable filters (LCTF)) Shadow invariance highlight, 352–353 quasi-invariance, 351–352 SIFT descriptor See Scale-invariant feature transform (SIFT) descriptor Similarity measure distance-based normalized, 311 distances and (see Distances) object-recognition systems, 330 Spatial filtering, Clifford algebra AG filtering, 159 classical digital colour processing images, 160 geometric algebra formalism, colour edges, 156–157 Quaternion formalism, 157 Sangwine’s method, 157–158 scalar and bivectorial parts, 158 Spearman rank order correlation, 442–443 Spectral analysis, IHLS and L*a*b* luminance-chrominance interference color texture, FFT, 309, 310 frequency peak, 307 plots, 308 ratio IRCL vs IRLC , 308–309 RGB, 306 two channel complex sinusoidal images, 307 zero mean value and SNR, 308 PSD estimation methods, 304–306 Spectral color space, Spectral endoscope See Endoscope spectroscopy system Spectral image enhancement See also Electronic endoscopy FICE (see Flexible spectral imaging color enhancement (FICE)) image reconstruction, 492, 493 Spectral imaging See also Spectral reflectance BSSDF, 463 Kubelka-Munk model, 464–465 514 Spectral reflectance See also Color high fidelity CIE definition, color-matching functions, 451–452 description, use digital archiving, 477 monitoring of degradation, 477 underdrawings, 477–478 virtual restoration, 478–479 eigenvectors, 490, 491 integral equations, 490 matrix, tristimulus, 451 mean color difference, 490, 491 psychophysical, 451 reconstruction direct, 466–467 indirect, 467–468 interpolation, 468 Wiener estimation (see Wiener estimation) Spectrometer colorimeter, 91–92 goniospectrometers, 462 SQM See Segmentation quality metric (SQM) Standard observer, Steerable filters, 128 Stevens effect, 35 Stochastic models, parametric See Parametric stochastic models Streaming video websites application, 181–182 STRESS combined dataset employed, CIEDE2000 development, 74 inter and intra observer variability, 74 multidimensional scaling and PF/3, 73 Supervised segmentation, 263–264 T Tele-spectroradiometer (TSR), 23 Teo and Heeger model, 439 Texture features Haralick texture features, 236–238 J-criterion, 234–235 J-images, 235–236 overlaid RGB cooccurrence matrices, 236, 237 run-length matrix, 238 Theory, group representations D4 , 123, 124 description, notation, 121–122 digital color images, 120 dihedral groups, definition, 121 linear mapping, 123 Index matrix–vector notation, 122 one and two dimensional subspace, 123 × pattern and filter functions, 124 permutation matrix and vector space, 122 RGB vectors, 121, 122 spatial transformations and orbit D4 x, 122 tensor, 124–125 Thin plate splines (TPS), 92 Total difference models, 77 TPS See Thin plate splines (TPS) TSR See Tele-spectroradiometer (TSR) U UCS See Uniform Chromaticity Scale (UCS) Uniform Chromaticity Scale (UCS), 381 Uniform colour spaces chromatic content and SCD data, 36 CIECAM02 J vs CAM02-UCS J and CIECAM02 M vs CAM02-UCS M’, 37, 38 CIE TC1–57, 76 coefficients, CAM02-LCD, CAM02-SCD, and CAM02-UCS, 37 difference formulas, 63 DIN99, 67 ellipses plotted, CIELAB and CAM02UCS, 37, 39 embedded, 68–69 gamut mapping, 36 large and small magnitude colour differences, 36 linear, 71 V VEF See Virtual electrical field (VEF) Video quality experts group (VQEG) correlation analysis, 441 image quality assessment, 444 Viewpoint invariance, 353, 356 Virtual electrical field (VEF), 252 Visual phenomena Helmholtz–Kohlrausch effect, 35 Helson–Judd effect, 36 Hunt effect, 34–35 lightness contrast and surround effect, 35 Stevens effect, 35 von Kries chromatic adaptation coefficient law, 30 cone types (RGB), 30 VQEG See Video quality experts group (VQEG) Index W Watershed classical approach, 245 color images, 246 critical point, algorithm, 245 determination, 244–245 topographical relief, 245 unsupervised approaches, 245 WCS See Window color system (WCS) Weber’s law, 97 Wiener estimation pseudo-inverse matrix, 491 spectral radiance, 491, 492 Window color system (WCS), 48 Wishart distribution 515 average percentage error, 319 LPE, 316 mean percentages, pixel classification errors, 321 multiple dimensions, chi-square, 283 numerical stability, 298 Y Young–Helmholz theory, Z Zero run length (ZRL), 401 ZRL See Zero run length (ZRL) ... Christine Fernandez- Maloigne Xlim-SIC Laboratory University of Poitiers 11 Bd Marie et Pierre Curie Futuroscope France ISBN 97 8-1 -4 41 9-6 18 9-1 ISBN 97 8-1 -4 41 9-6 19 0-7 (eBook) DOI 10.1007/97 8-1 -4 41 9-6 19 0-7 ... e-mail: jussi@monash.edu C Fernandez- Maloigne (ed.), Advanced Color Image Processing and Analysis, DOI 10.1007/97 8-1 -4 41 9-6 19 0-7 1, © Springer Science+Business Media New York 2013 M.J Shyu and. .. e-mail: M.R.Luo@Leeds.ac.uk C Li Liaoning University of Science and Technology, Anshan, China C Fernandez- Maloigne (ed.), Advanced Color Image Processing and Analysis, DOI 10.1007/97 8-1 -4 41 9-6 19 0-7