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P1: IML/FFX MOBK010-FM WangBovik MOBK010-WangBovik.cls QC: IML/FFX January 28, 2006 14:16 P2: IML/FFX T1: IML MODERN IMAGE QUALITY ASSESSMENT i

P1: IML/FFX MOBK010-FM WangBovik MOBK010-WangBovik.cls QC: IML/FFX January 28, 2006 14:16 P2: IML/FFX T1: IML Copyright © 2006 by Morgan & Claypool All rights reserved. No part of this publication may be reproduced, stored in a retrieval sys- tem, or transmitted in any form or by any means—electronic, mechanical, photocopy, recording, or any other except for brief quotations in printed reviews, without the prior permission of the publisher. Modern Image Quality Assessment Zhou Wang and Alan C. Bovik www.morganclaypool.com ISBN: 1598290223 ISBN: 1598290231 paper Wang/Bovik ebook Wang/Bovik Modern Image Quality Assessment Modern Image Quality Assessment doi 10.2200/S00010ED1V01Y2005081VM003 Library of Congress Cataloging-in-Publication Data 10 9 8 7 6 5 4 3 2 1 Printed in the United States of America ii

P1: IML/FFX MOBK010-FM WangBovik MOBK010-WangBovik.cls QC: IML/FFX January 28, 2006 14:16 P2: IML/FFX T1: IML MODERN IMAGE QUALITY ASSESSMENT Zhou Wang The University of Texas at Arlington Alan C. Bovik The University of Texas at Austin M&C Morgan & Claypool Publishers iii

P1: IML/FFX MOBK010-FM WangBovik MOBK010-WangBovik.cls QC: IML/FFX January 28, 2006 14:16 P2: IML/FFX T1: IML iv ABSTRACT This Lecture book is about objective image quality assessment—where the aim is to provide computational models that can automatically predict perceptual image qual- ity. The early years of the 21st century have witnessed a tremendous growth in the use of digital images as a means for representing and communicating information. A considerable percentage of this literature is devoted to methods for improving the appearance of images, or for maintaining the appearance of images that are processed. Nevertheless, the quality of digital images, processed or otherwise, is rarely perfect. Images are subject to distortions during acquisition, compression, transmission, processing, and reproduction. To maintain, control, and enhance the quality of images, it is important for image acquisition, management, communi- cation, and processing systems to be able to identify and quantify image quality degradations. The goals of this book are as follows; a) to introduce the fundamentals of image quality assessment, and to explain the relevant engineering problems, b) to give a broad treatment of the current state-of-the-art in image quality assessment, by describing leading algorithms that address these engineering problems, and c) to provide new directions for future research, by introducing recent models and paradigms that signiﬁcantly differ from those used in the past. The book is written to be accessible to university students curious about the state-of-the-art of image quality assessment, expert industrial R&D engineers seeking to implement image/video quality assessment systems for speciﬁc applica- tions, and academic theorists interested in developing new algorithms for image quality assessment or using existing algorithms to design or optimize other image processing applications. KEYWORDS Image quality assessment, Perceptual image processing, Visual perception, Computer vision, Computational vision

P1: IML/FFX MOBK010-FM WangBovik MOBK010-WangBovik.cls QC: IML/FFX P2: IML/FFX T1: IML January 28, 2006 14:16 v 1. 2. Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Subjective vs. Objective Image Quality Measures . . . . . . . . . . . . . . . . . 1 1.2 What’s Wrong with the MSE? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Classiﬁcation of Objective Image Quality Measures . . . . . . . . . . . . . 11 1.3.1 Full-Reference, No-Reference and Reduced-Reference Image Quality Measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12 1.3.2 General-Purpose and Application-Speciﬁc Image Quality Measures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13 1.3.3 Bottom-Up and Top-Down Image Quality Measures . . . . 14 1.4 Organization of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3 Bottom-Up Approaches for Full-Reference Image Quality Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1 General Philosophy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.2 The Human Visual System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.1 Anatomy of the Early HVS . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.2 Psychophysical HVS Features . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Framework of Error Visibility Methods . . . . . . . . . . . . . . . . . . . . . . . . 26 2.3.1 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.3.2 Channel Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.3.3 Error Normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.3.4 Error Pooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Image Quality Assessment Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.4.1 Daly Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.4.2 Lubin Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2.4.3 Safranek–Johnson Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 2.4.4 Teo–Heeger Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 2.4.5 Watson’s DCT Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .35 2.4.6 Watson’s Wavelet Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 2.5 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37 2.5.1 The Quality Deﬁnition Problem . . . . . . . . . . . . . . . . . . . . . . . 37 2.4

P1: IML/FFX MOBK010-FM WangBovik MOBK010-WangBovik.cls QC: IML/FFX January 28, 2006 14:16 P2: IML/FFX T1: IML vi CONTENTS 2.5.2 The Suprathreshold Problem . . . . . . . . . . . . . . . . . . . . . . . . . . 38 2.5.3 The Natural Image Complexity Problem . . . . . . . . . . . . . . . . 38 2.5.4 The Dependency Decoupling Problem. . . . . . . . . . . . . . . . . .39 2.5.5 The Cognitive Interaction Problem. . . . . . . . . . . . . . . . . . . . .39 Top-Down Approaches for Full-Reference Image Quality Assessment . . 41 3.1 General Philosophy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Structural Similarity Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.2 Structural Similarity and Image Quality. . . . . . . . . . . . . . . . .43 3.2.1 3.2.2 Spatial Domain Structural Similarity Index . . . . . . . . . . . . . 44 3.2.3 Complex Wavelet Domain Structural 3.3 Similarity Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.2.4 Remarks on Structural Similarity Indices . . . . . . . . . . . . . . . . 64 Information-Theoretic Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 3.3.1 Information Fidelity and Image Quality . . . . . . . . . . . . . . . . 65 3.3.2 The Visual Information Fidelity Measure . . . . . . . . . . . . . . . 66 3.3.3 Remarks on Information-Theoretic Indices. . . . . . . . . . . . . .73 3.4 Discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .74 No-Reference Image Quality Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.1 General Philosophy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 4.2 NR Measures for Block Image Compression . . . . . . . . . . . . . . . . . . . . 81 Spatial Domain Method. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .82 Frequency Domain Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 4.3 NR Measures for Wavelet Image Compression . . . . . . . . . . . . . . . . . . 93 4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100 4.2.1 4.2.2 Reduced-Reference Image Quality Assessment . . . . . . . . . . . . . . . . . . . . . . 103 5.1 General Philosophy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 5.2 Wavelet Domain RR Measure Based on Natural Image Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 5.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .121 6.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 6.2 Extensions and Future Directions. . . . . . . . . . . . . . . . . . . . . . . . . . . . .124 Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 3. 4. 5. 6.

P2: IML/FFX T1: IML P1: IML/FFX MOBK010-FM WangBovik MOBK010-WangBovik.cls QC: IML/FFX January 28, 2006 14:16 vii Preface Although the topic of image quality assessment has been around for more than four decades, there has, until recently, been relatively little published on the topic. Certainly this omission is not for lack of need or paucity of interest, since most image-processing algorithms and devices are, in fact, devoted to maintaining or improving the apparent quality of digitized images for human visual consumption. Traditionally, image quality has been evaluated by human subjects. This method, though reliable, is expensive and too slow for real-world applications. So this book is about objective image quality assessment, where the goal is to provide computational models that can automatically predict perceptual image quality. Per- haps the ﬁrst notable work in the ﬁeld of objective image quality assessment was the pioneering work of Mannos and Sakrison, who proposed image ﬁdelity criteria, taking into account human visual sensitivity as a function of spatial frequency [1]. Other important early work was documented in a book edited by Watson [2]. Yet, until the past few years, the ﬁeld of image quality assessment has received relatively little attention, despite its recognized importance. Indeed, image quality assessment has paradoxically remained not only both something of a Holy Grail for image-processing engineers and vision scientists but also a No-Man’s Land of research and development work. Indeed, a recent Web search, as we write this, reveals that 100 times as many articles are found for “image restoration” as for “image quality assessment” and nearly 400 times as many articles are found for “image enhancement.” How do we explain this discrepancy? It would seem that quality assessment should be a necessary ingredient in developing restoration and enhancement algorithms. Is image quality assessment such a daunting problem, or an insoluble one, that researchers have avoided wasting their efforts?

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