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Intelligent Image and Video Compression - 2nd Edition - ISBN: 9780128203538

Intelligent Image and Video Compression

2nd Edition

Communicating Pictures

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Authors: David Bull Fan Zhang
Paperback ISBN: 9780128203538
Imprint: Academic Press
Published Date: 1st March 2021
Page Count: 522
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Description

Intelligent Image and Video Compression: Communicating Pictures, 2nd edition explains the requirements, analysis, design and application of a modern video coding system. It draws on the authors’ extensive academic and professional experience in this field to deliver a text that is algorithmically rigorous yet accessible, relevant to modern standards and practical. It builds on a thorough grounding in mathematical foundations and visual perception to demonstrate how modern image and video compression methods can be designed to meet the rate-quality performance levels demanded by today's applications and users, in the context of prevailing network constraints.

Key Features

Key Features

  • An approach that combines algorithmic rigor with practical implementation using numerous worked examples.
  • Explains how video compression methods exploit statistical redundancies, natural correlations and knowledge of human perception to improve performance.
  • Uses contemporary video coding standards (AVC and HEVC) as a vehicle for explaining block-based compression.
  • Provides broad coverage of important topics such as visual quality assessment and video streaming.

New to this edition:

  • Coverage of new more immersive applications, explaining compression requirements and solutions for HDR and UHDTV, VR, AR and MR.
  • Description of how we can measure viewer engagement with these applications.
  • An introduction to machine learning algorithms and coverage of how these can optimize compression tools and their performance.
  • Inclusion of the latest advances in perceptual metrics, such as VMAF.
  • Description of new and extended databases for video quality evaluation and for training machine learning systems.
  • Coverage of recent innovations and standards to support adaptive video streaming.
  • A review of the perceptual influences of dynamic range including descriptions of perceptual quantization and new formats.
  • A comprehensive update on recent compression standards to include the key attributes of new and emerging standards such as AV1, VVC and AV2.

Readership

Researchers, advanced undergraduate and graduate students in the field of image and video processing, computer vision

Table of Contents

1 INTRODUCTION 1

1.1 Communicating pictures: the need for compression . . . . . . . . . . . . . . 1

1.1.1 What is compression? . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.2 Why do we need compression? . . . . . . . . . . . . . . . . . . . . . 2

1.2 Applications and Drivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.2.1 Generic drivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.2.2 Application drivers and markets . . . . . . . . . . . . . . . . . . . . 6

1.3 Requirements and trade-offs in a compression system . . . . . . . . . . . . 8

1.3.1 The benefits of a digital solution . . . . . . . . . . . . . . . . . . . . 8

1.3.2 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.3.3 Trade-offs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

1.4 The Basics of Compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.4.1 Still image encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.4.2 Encoding video . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

1.4.3 Measuring visual quality . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.5 The Need for Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.5.1 Some basic facts about standards . . . . . . . . . . . . . . . . . . . . 11

1.5.2 A brief history of video encoding standards . . . . . . . . . . . . . . 12

1.6 The creative continuum: an interdisciplinary approach . . . . . . . . . . . . 13

1.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2 THE HUMAN VISUAL SYSTEM 17

2.1 Principles and theories of human vision . . . . . . . . . . . . . . . . . . . . 17

2.2 Acquisition: the human eye . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.2.1 Retinal tissue layers . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.2.2 Optical processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.2.3 Retinal photoreceptors and their distribution . . . . . . . . . . . . . 22

2.2.4 Visual processing in the retina . . . . . . . . . . . . . . . . . . . . . 24

2.3 The visual cortex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.3.1 Opponent processes . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.3.2 Biased competition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.3.3 Adaptation processes . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

ii

CONTENTS iii

2.3.4 V1 - the primary visual cortex . . . . . . . . . . . . . . . . . . . . . 27

2.3.5 V2 - the prestriate cortex . . . . . . . . . . . . . . . . . . . . . . . . 28

2.3.6 Dorsal and Ventral Streams . . . . . . . . . . . . . . . . . . . . . . . 28

2.3.7 Extrastriate Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

2.4 Visual fields and acuity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.4.1 Field of view . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.4.2 Acuity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

2.4.3 Light, luminance and brightness . . . . . . . . . . . . . . . . . . . . 30

2.4.4 Light level adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.5 Color processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.5.1 Opponent theories of color . . . . . . . . . . . . . . . . . . . . . . . . 34

2.5.2 CIE 1931 chromaticity chart . . . . . . . . . . . . . . . . . . . . . . . 36

2.6 Spatial processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.6.1 Just noticeable difference, contrast and Weber’s Law . . . . . . . . . 36

2.6.2 Frequency-dependent contrast sensitivity . . . . . . . . . . . . . . . . 37

2.6.3 Multiscale edges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

2.6.4 Perception of textures . . . . . . . . . . . . . . . . . . . . . . . . . . 40

2.6.5 Shape and Object Recognition . . . . . . . . . . . . . . . . . . . . . 42

2.6.6 The importance of phase information . . . . . . . . . . . . . . . . . . 43

2.7 Perception of Scale and depth . . . . . . . . . . . . . . . . . . . . . . . . . . 43

2.7.1 Size or Scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

2.7.2 Depth cues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

2.7.3 Depth cues and 3-D entertainment . . . . . . . . . . . . . . . . . . . 46

2.8 Temporal and spatio-temporal response . . . . . . . . . . . . . . . . . . . . 47

2.8.1 Temporal CSF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

2.8.2 Spatio-temporal CSF . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

2.8.3 Flicker and peripheral vision . . . . . . . . . . . . . . . . . . . . . . 48

2.9 Attention and eye movements . . . . . . . . . . . . . . . . . . . . . . . . . . 49

2.9.1 Saliency and attention . . . . . . . . . . . . . . . . . . . . . . . . . . 50

2.9.2 Eye movements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

2.10 Visual masking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

2.10.1 Texture masking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

2.10.2 Edge masking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

2.10.3 Temporal masking . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

2.11 A perceptual basis for image and video compression . . . . . . . . . . . . . . 53

3 SIGNAL PROCESSING AND INFORMATION THEORY FUNDAMENTALS

60

3.1 Signal and picture sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

3.1.1 The sampling theorem . . . . . . . . . . . . . . . . . . . . . . . . . . 61

3.1.2 Multidimensional sampling lattices . . . . . . . . . . . . . . . . . . . 64

3.2 Statistics of images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

CONTENTS iv

3.2.1 Histograms and distributions . . . . . . . . . . . . . . . . . . . . . . 67

3.2.2 Mean values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

3.2.3 Correlation in natural images . . . . . . . . . . . . . . . . . . . . . . 68

3.3 Filtering and transforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

3.3.1 Discrete time linear systems . . . . . . . . . . . . . . . . . . . . . . . 74

3.3.2 Convolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

3.3.3 Linear filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

3.3.4 Filter frequency response . . . . . . . . . . . . . . . . . . . . . . . . 77

3.3.5 Examples of practical filters . . . . . . . . . . . . . . . . . . . . . . . 78

3.3.6 Non-linear filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

3.3.7 Linear transforms and the DFT . . . . . . . . . . . . . . . . . . . . . 80

3.4 Quantization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

3.4.1 Basic theory of quantization . . . . . . . . . . . . . . . . . . . . . . . 82

3.4.2 Adaptation to signal statistics . . . . . . . . . . . . . . . . . . . . . . 84

3.4.3 HVS weighting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

3.4.4 Vector quantization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

3.5 Linear prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

3.5.1 Basic feedforward linear predictive coding . . . . . . . . . . . . . . . 84

3.5.2 Linear prediction with the predictor in the feedback loop . . . . . . . 88

3.5.3 Wiener Hopf equations and the Wiener filter . . . . . . . . . . . . . 90

3.6 Information and entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

3.6.1 Self information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

3.6.2 Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

3.6.3 Symbols and statistics . . . . . . . . . . . . . . . . . . . . . . . . . . 94

3.7 Machine learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

3.7.1 An overview of AI and machine learning . . . . . . . . . . . . . . . . 95

3.7.2 Neural networks and error backpropagation . . . . . . . . . . . . . . 96

3.7.3 Deep neural networks . . . . . . . . . . . . . . . . . . . . . . . . . . 104

3.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

4 DIGITAL PICTURE FORMATS AND REPRESENTATIONS 112

4.1 Pixels, blocks and pictures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

4.1.1 Pixels, samples or pels . . . . . . . . . . . . . . . . . . . . . . . . . . 112

4.1.2 Moving pictures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

4.1.3 Coding units and macroblocks . . . . . . . . . . . . . . . . . . . . . . 115

4.1.4 Picture types and Groups Of Pictures . . . . . . . . . . . . . . . . . 115

4.2 Formats and aspect ratios . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

4.2.1 Aspect ratios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119

4.2.2 Displaying different formats . . . . . . . . . . . . . . . . . . . . . . . 122

4.3 Picture scanning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123

4.3.1 Standards conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

CONTENTS v

4.4 Gamma correction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

4.5 Color spaces and color transformations . . . . . . . . . . . . . . . . . . . . . 127

4.5.1 Color descriptions and the HVS . . . . . . . . . . . . . . . . . . . . . 127

4.5.2 Sub-sampled color spaces . . . . . . . . . . . . . . . . . . . . . . . . 133

4.5.3 Color sensing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

4.6 Measuring and comparing picture quality . . . . . . . . . . . . . . . . . . . 138

4.6.1 Compression ratio and bit rate . . . . . . . . . . . . . . . . . . . . . 138

4.6.2 Objective distortion and quality metrics . . . . . . . . . . . . . . . . 139

4.6.3 Subjective assessment . . . . . . . . . . . . . . . . . . . . . . . . . . 143

4.7 Rates and distortions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

4.7.1 Rate-distortion characteristics . . . . . . . . . . . . . . . . . . . . . . 144

4.7.2 Rate distortion optimization . . . . . . . . . . . . . . . . . . . . . . . 146

4.7.3 Comparing video coding performance . . . . . . . . . . . . . . . . . . 146

4.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

5 TRANSFORMS FOR IMAGE AND VIDEO CODING 151

5.1 The Principles of decorrelating transforms . . . . . . . . . . . . . . . . . . . 151

5.1.1 The basic building blocks . . . . . . . . . . . . . . . . . . . . . . . . 151

5.1.2 Principle components and axis rotation . . . . . . . . . . . . . . . . 152

5.2 Unitary transforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

5.2.1 Basis functions and linear combinations . . . . . . . . . . . . . . . . 155

5.2.2 Orthogonality and normalization . . . . . . . . . . . . . . . . . . . . 156

5.2.3 Extension to 2-D . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

5.3 Basic transforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

5.3.1 The Haar transform . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

5.3.2 The Walsh-Hadamard transform . . . . . . . . . . . . . . . . . . . . 158

5.3.3 So why not use the discrete Fourier transform? . . . . . . . . . . . . 162

5.3.4 Desirable properties of an image transform . . . . . . . . . . . . . . . 162

5.4 Optimum transforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163

5.4.1 Discarding coefficients . . . . . . . . . . . . . . . . . . . . . . . . . . 163

5.4.2 The Karhunen-Loeve transform (KLT) . . . . . . . . . . . . . . . . . 165

5.4.3 The KLT in practice . . . . . . . . . . . . . . . . . . . . . . . . . . . 166

5.5 Discrete Cosine Transform (DCT) . . . . . . . . . . . . . . . . . . . . . . . . 167

5.5.1 Derivation of the DCT . . . . . . . . . . . . . . . . . . . . . . . . . . 167

5.5.2 DCT basis functions . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

5.5.3 Extension to 2-D: Separability . . . . . . . . . . . . . . . . . . . . . 172

5.5.4 Variants on sinusoidal transforms . . . . . . . . . . . . . . . . . . . . 174

5.6 Quantization of DCT coefficients . . . . . . . . . . . . . . . . . . . . . . . . 175

5.6.1 The basics of quantization . . . . . . . . . . . . . . . . . . . . . . . 175

5.6.2 Perceptually optimized quantization matrices . . . . . . . . . . . . . 178

5.7 Performance comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180

CONTENTS vi

5.7.1 DCT vs DFT revisited . . . . . . . . . . . . . . . . . . . . . . . . . . 180

5.7.2 Comparison of transforms . . . . . . . . . . . . . . . . . . . . . . . . 181

5.7.3 Rate-distortion performance of the DCT . . . . . . . . . . . . . . . . 181

5.8 DCT implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185

5.8.1 Choice of transform block size . . . . . . . . . . . . . . . . . . . . . . 185

5.8.2 DCT complexity reduction . . . . . . . . . . . . . . . . . . . . . . . . 185

5.8.3 Field vs frame encoding for interlaced sequences . . . . . . . . . . . 187

5.8.4 Integer transforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188

5.8.5 DCT DEMO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188

5.9 JPEG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188

5.10 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189

6 FILTER BANKS AND WAVELET COMPRESSION 192

6.1 Introduction to multiscale processing . . . . . . . . . . . . . . . . . . . . . . 192

6.1.1 The short time Fourier transform and the Gabor transform . . . . . 193

6.1.2 What is a wavelet? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

6.1.3 Wavelet and filter bank properties . . . . . . . . . . . . . . . . . . . 196

6.2 Perfect reconstruction filter banks . . . . . . . . . . . . . . . . . . . . . . . . 197

6.2.1 Filter and decomposition requirements . . . . . . . . . . . . . . . . . 197

6.2.2 The 1-D filter bank structure . . . . . . . . . . . . . . . . . . . . . . 198

6.3 Multirate filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202

6.3.1 Upsampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202

6.3.2 Downsampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

6.3.3 System transfer function . . . . . . . . . . . . . . . . . . . . . . . . . 207

6.3.4 Perfect reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . 209

6.3.5 Spectral effects of the two-channel decomposition . . . . . . . . . . . 209

6.4 Useful filters and filter banks . . . . . . . . . . . . . . . . . . . . . . . . . . 210

6.4.1 Quadrature mirror filters . . . . . . . . . . . . . . . . . . . . . . . . . 210

6.4.2 Wavelet filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213

6.4.3 Multistage (multiscale) decompositions . . . . . . . . . . . . . . . . . 216

6.4.4 Separability and extension to 2-D . . . . . . . . . . . . . . . . . . . . 217

6.4.5 Finite length sequences, edge artifacts and boundary extension . . . 220

6.4.6 Wavelet compression performance . . . . . . . . . . . . . . . . . . . . 224

6.5 Coefficient quantization and bit allocation . . . . . . . . . . . . . . . . . . . 224

6.5.1 Bit allocation and zonal coding . . . . . . . . . . . . . . . . . . . . . 224

6.5.2 Hierarchical coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224

6.6 JPEG2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227

6.6.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227

6.6.2 Architecture - bit planes and scalable coding . . . . . . . . . . . . . 227

6.6.3 Coding performance . . . . . . . . . . . . . . . . . . . . . . . . . . . 228

6.6.4 Region of interest coding . . . . . . . . . . . . . . . . . . . . . . . . . 228

6.6.5 Benefits and status . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230

6.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230

CONTENTS vii

7 LOSSLESS COMPRESSION METHODS 233

7.1 Motivation for lossless image compression . . . . . . . . . . . . . . . . . . . 233

7.1.1 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 234

7.1.2 Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235

7.1.3 Dictionary methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 236

7.2 Symbol encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236

7.2.1 A generic model for lossless compression . . . . . . . . . . . . . . . . 236

7.2.2 Entropy, efficiency and redundancy . . . . . . . . . . . . . . . . . . . 236

7.2.3 Prefix codes and unique decodability . . . . . . . . . . . . . . . . . . 237

7.3 Huffman coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239

7.3.1 The basic algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 239

7.3.2 Minimum variance Huffman coding . . . . . . . . . . . . . . . . . . . 241

7.3.3 Huffman decoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243

7.3.4 Modified Huffman coding . . . . . . . . . . . . . . . . . . . . . . . . 243

7.3.5 Properties of Huffman codes . . . . . . . . . . . . . . . . . . . . . . . 244

7.3.6 Adaptive methods for Huffman encoding . . . . . . . . . . . . . . . . 245

7.4 Symbol formation and encoding . . . . . . . . . . . . . . . . . . . . . . . . . 248

7.4.1 Dealing with sparse matrices . . . . . . . . . . . . . . . . . . . . . . 248

7.4.2 Symbol encoding in JPEG . . . . . . . . . . . . . . . . . . . . . . . . 249

7.4.3 JPEG performance examples . . . . . . . . . . . . . . . . . . . . . . 254

7.4.4 Lossless JPEG mode . . . . . . . . . . . . . . . . . . . . . . . . . . . 254

7.4.5 Context Adaptive Variable Length Coding (CAVLC) in H.264/AVC 257

7.5 Golomb coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258

7.5.1 Unary codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258

7.5.2 Golomb and Golomb-Rice codes . . . . . . . . . . . . . . . . . . . . . 258

7.5.3 Exponential Golomb codes . . . . . . . . . . . . . . . . . . . . . . . . 258

7.6 Arithmetic coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259

7.6.1 The basic arithmetic encoding algorithm . . . . . . . . . . . . . . . . 260

7.6.2 The basic arithmetic decoding algorithm . . . . . . . . . . . . . . . . 263

7.6.3 Advantages and properties of arithmetic coding . . . . . . . . . . . 265

7.6.4 Binary implementations of arithmetic coding . . . . . . . . . . . . . 267

7.6.5 Tag generation with scaling . . . . . . . . . . . . . . . . . . . . . . . 269

7.6.6 Context-adaptive arithmetic coding . . . . . . . . . . . . . . . . . . . 271

7.6.7 Arithmetic coding with binary symbols . . . . . . . . . . . . . . . . . 272

7.7 Performance comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277

7.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278

8 CODING MOVING PICTURES: MOTION PREDICTION 281

8.1 Temporal correlation and exploiting temporal redundancy . . . . . . . . . . 281

8.1.1 Why motion estimation? . . . . . . . . . . . . . . . . . . . . . . . . . 281

8.1.2 Projected motion and apparent motion . . . . . . . . . . . . . . . . . 283

CONTENTS viii

8.1.3 Understanding temporal correlation . . . . . . . . . . . . . . . . . . 284

8.1.4 How to form the prediction . . . . . . . . . . . . . . . . . . . . . . . 284

8.1.5 Understanding temporal correlation . . . . . . . . . . . . . . . . . . 287

8.1.6 Approaches to motion estimation . . . . . . . . . . . . . . . . . . . . 287

8.2 Motion models and motion estimation . . . . . . . . . . . . . . . . . . . . . 288

8.2.1 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 288

8.2.2 Affine and high order models . . . . . . . . . . . . . . . . . . . . . . 288

8.2.3 Translation only models . . . . . . . . . . . . . . . . . . . . . . . . . 289

8.2.4 Pixel-recursive methods . . . . . . . . . . . . . . . . . . . . . . . . . 291

8.2.5 Frequency domain motion estimation using phase correlation (PhC) 292

8.3 Block Matching Motion Estimation (BMME) . . . . . . . . . . . . . . . . . 295

8.3.1 Translational block matching . . . . . . . . . . . . . . . . . . . . . . 295

8.3.2 Matching criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 296

8.3.3 Full search algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 297

8.3.4 Properties of block motion fields and error surfaces . . . . . . . . . 297

8.3.5 Motion failure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301

8.3.6 Restricted and unrestricted vectors . . . . . . . . . . . . . . . . . . . 302

8.4 Reduced complexity motion estimation . . . . . . . . . . . . . . . . . . . . . 303

8.4.1 Pixel grids and search grids . . . . . . . . . . . . . . . . . . . . . . . 303

8.4.2 Complexity of full search . . . . . . . . . . . . . . . . . . . . . . . . . 303

8.4.3 Reducing search complexity . . . . . . . . . . . . . . . . . . . . . . . 304

8.4.4 2-D Logarithmic Search (TDL) . . . . . . . . . . . . . . . . . . . . . 305

8.4.5 N-Step Search (NSS) . . . . . . . . . . . . . . . . . . . . . . . . . . . 305

8.4.6 Diamond search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308

8.4.7 Hexagonal search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309

8.4.8 Test Zone Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310

8.4.9 Initialization and termination criteria . . . . . . . . . . . . . . . . . . 311

8.4.10 Reduced complexity block matching algorithm performance comparisons

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313

8.5 Skip and merge modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 314

8.6 Motion vector coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315

8.6.1 Motion vector prediction . . . . . . . . . . . . . . . . . . . . . . . . . 315

8.6.2 Entropy coding of motion vectors . . . . . . . . . . . . . . . . . . . . 315

8.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316

9 THE BLOCK-BASED HYBRID VIDEO CODEC 319

9.1 The block-based hybrid model for video compression . . . . . . . . . . . . . 319

9.1.1 Picture types and prediction modes . . . . . . . . . . . . . . . . . . . 319

9.1.2 Properties of the DFD signal . . . . . . . . . . . . . . . . . . . . . . 321

9.1.3 Operation of the video encoding loop . . . . . . . . . . . . . . . . . . 322

9.1.4 Operation of the video decoder . . . . . . . . . . . . . . . . . . . . . 322

9.2 Intra-frame prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327

CONTENTS ix

9.2.1 Intra-prediction for small luminance blocks . . . . . . . . . . . . . . 328

9.2.2 Intra-prediction for larger blocks . . . . . . . . . . . . . . . . . . . . 329

9.3 Sub-pixel motion estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 330

9.3.1 Sub-pixel matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330

9.3.2 Interpolation methods . . . . . . . . . . . . . . . . . . . . . . . . . . 331

9.3.3 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 334

9.3.4 Interpolation-free methods . . . . . . . . . . . . . . . . . . . . . . . . 336

9.4 Multiple reference frame motion estimation . . . . . . . . . . . . . . . . . . 336

9.4.1 Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336

9.4.2 Properties, complexity and performance of MRF-ME . . . . . . . . . 337

9.4.3 Reduced complexity MRF-ME . . . . . . . . . . . . . . . . . . . . . 338

9.4.4 The use of multiple reference frames in current standards . . . . . . 338

9.5 Variable block sizes for motion estimation . . . . . . . . . . . . . . . . . . . 339

9.5.1 Influence of block size . . . . . . . . . . . . . . . . . . . . . . . . . . 339

9.5.2 Variable block sizes in practice . . . . . . . . . . . . . . . . . . . . . 339

9.6 Variable sized transforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340

9.6.1 Integer transforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340

9.6.2 DC coefficient transforms . . . . . . . . . . . . . . . . . . . . . . . . 342

9.7 In-loop deblocking operations . . . . . . . . . . . . . . . . . . . . . . . . . . 343

9.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343

10 MEASURING AND MANAGING PICTURE QUALITY 347

10.1 General considerations and influences . . . . . . . . . . . . . . . . . . . . . . 348

10.1.1 What do we want to assess? . . . . . . . . . . . . . . . . . . . . . . . 348

10.1.2 Noise, distortion and quality . . . . . . . . . . . . . . . . . . . . . . . 348

10.1.3 Influences on perceived quality . . . . . . . . . . . . . . . . . . . . . 349

10.2 Subjective testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350

10.2.1 Justification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 350

10.2.2 Test sequences and conditions . . . . . . . . . . . . . . . . . . . . . 351

10.2.3 Choosing subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . 352

10.2.4 Testing environment . . . . . . . . . . . . . . . . . . . . . . . . . . . 353

10.2.5 Testing methodology and recording of results . . . . . . . . . . . . . 353

10.2.6 Statistical analysis and significance testing . . . . . . . . . . . . . . . 357

10.2.7 The complete evaluation process . . . . . . . . . . . . . . . . . . . . 358

10.2.8 Subjective testing through crowdsourcing . . . . . . . . . . . . . . . 358

10.2.9 Measuring immersion in visual content . . . . . . . . . . . . . . . . . 360

10.3 Test data sets and how to use them . . . . . . . . . . . . . . . . . . . . . . . 361

10.3.1 Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361

10.3.2 The relationship between Mean Opinion Score and an objective metric363

10.3.3 Evaluating metrics using public (or private) databases . . . . . . . . 363

10.4 Objective quality metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365

CONTENTS x

10.4.1 Why do we need quality metrics? . . . . . . . . . . . . . . . . . . . . 365

10.4.2 A characterization of PSNR . . . . . . . . . . . . . . . . . . . . . . . 367

10.4.3 A perceptual basis for metric development . . . . . . . . . . . . . . 368

10.4.4 Perception-based image and video quality metrics . . . . . . . . . . 370

10.4.5 The future of metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 376

10.5 Rate-distortion optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . 378

10.5.1 Classical rate-distortion theory . . . . . . . . . . . . . . . . . . . . . 378

10.5.2 Practical rate distortion optimization . . . . . . . . . . . . . . . . . . 380

10.5.3 The influence of additional coding modes and parameters . . . . . . 382

10.5.4 From Rate-Distortion to Rate-Quality Optimization . . . . . . . . . 387

10.6 Rate control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388

10.6.1 Buffering and HRD . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388

10.6.2 Rate control in practice . . . . . . . . . . . . . . . . . . . . . . . . . 391

10.6.3 Regions of interest and rate control . . . . . . . . . . . . . . . . . . . 394

10.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395

11 COMMUNICATING PICTURES: DELIVERY ACROSS NETWORKS401

11.1 The operating environment . . . . . . . . . . . . . . . . . . . . . . . . . . . 401

11.1.1 Characteristics of modern networks . . . . . . . . . . . . . . . . . . . 401

11.1.2 Transmission types . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402

11.1.3 Operating constraints . . . . . . . . . . . . . . . . . . . . . . . . . . 403

11.1.4 Error characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . 404

11.1.5 The challenges and a solution framework . . . . . . . . . . . . . . . . 406

11.2 The effects of loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407

11.2.1 Synchronization failure . . . . . . . . . . . . . . . . . . . . . . . . . . 407

11.2.2 Header loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 408

11.2.3 Spatial error propagation . . . . . . . . . . . . . . . . . . . . . . . . 408

11.2.4 Temporal error propagation . . . . . . . . . . . . . . . . . . . . . . . 410

11.3 Mitigating the effect of bitstream errors . . . . . . . . . . . . . . . . . . . . 411

11.3.1 Video is not the same as data! . . . . . . . . . . . . . . . . . . . . . 411

11.3.2 Error resilient solutions . . . . . . . . . . . . . . . . . . . . . . . . . 412

11.4 Transport layer solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 413

11.4.1 Automatic Repeat Request (ARQ) . . . . . . . . . . . . . . . . . . . 414

11.4.2 FEC Channel coding . . . . . . . . . . . . . . . . . . . . . . . . . . . 415

11.4.3 Hybrid ARQ (HARQ) . . . . . . . . . . . . . . . . . . . . . . . . . . 417

11.4.4 Packetization strategies . . . . . . . . . . . . . . . . . . . . . . . . . 417

11.5 Application Layer solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . 418

11.5.1 Network abstraction . . . . . . . . . . . . . . . . . . . . . . . . . . . 418

11.5.2 The influence of frame type . . . . . . . . . . . . . . . . . . . . . . . 418

11.5.3 Synchronization codewords . . . . . . . . . . . . . . . . . . . . . . . 420

11.5.4 Reversible VLC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 420

CONTENTS xi

11.5.5 Slice structuring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 421

11.5.6 Error tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 422

11.5.7 Redundant motion vectors . . . . . . . . . . . . . . . . . . . . . . . . 424

11.6 Cross layer solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424

11.6.1 Link adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424

11.7 Inherently robust coding strategies . . . . . . . . . . . . . . . . . . . . . . . 427

11.7.1 Error Resilient Entropy Coding (EREC) . . . . . . . . . . . . . . . . 427

11.7.2 Pyramid vector quantization (PVQ) . . . . . . . . . . . . . . . . . . 431

11.8 Error concealment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433

11.8.1 Detecting missing information . . . . . . . . . . . . . . . . . . . . . . 434

11.8.2 Spatial error concealment (SEC) . . . . . . . . . . . . . . . . . . . . 435

11.8.3 Temporal error concealment . . . . . . . . . . . . . . . . . . . . . . . 436

11.8.4 Hybrid methods with mode selection . . . . . . . . . . . . . . . . . . 440

11.8.5 Intelligent error concealment . . . . . . . . . . . . . . . . . . . . . . 442

11.9 Congestion management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442

11.9.1 HTTP Adaptive Streaming (HAS) . . . . . . . . . . . . . . . . . . . 442

11.9.2 Scalable video encoding . . . . . . . . . . . . . . . . . . . . . . . . . 445

11.9.3 Multiple description coding (MDC) . . . . . . . . . . . . . . . . . . . 447

11.10Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447

12 VIDEO CODING STANDARDS AND FORMATS 453

12.1 The need for and role of standards . . . . . . . . . . . . . . . . . . . . . . . 453

12.1.1 The focus of video standardization . . . . . . . . . . . . . . . . . . . 453

12.1.2 The standardization process . . . . . . . . . . . . . . . . . . . . . . . 454

12.1.3 Intellectual property and licensing . . . . . . . . . . . . . . . . . . . 455

12.2 H.120 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455

12.2.1 Brief history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 455

12.2.2 Primary features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456

12.3 H.261 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456

12.3.1 Brief history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456

12.3.2 Picture types and primary features . . . . . . . . . . . . . . . . . . . 456

12.4 MPEG-2/DVB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458

12.4.1 Brief History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 458

12.4.2 Picture types and primary features . . . . . . . . . . . . . . . . . . . 458

12.4.3 MPEG-2 profiles and levels . . . . . . . . . . . . . . . . . . . . . . . 459

12.5 H.263 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460

12.5.1 Brief history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 460

12.5.2 Picture types and primary features . . . . . . . . . . . . . . . . . . . 460

12.6 MPEG-4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465

12.6.1 Brief history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465

12.6.2 Picture types and primary features . . . . . . . . . . . . . . . . . . . 465

CONTENTS xii

12.7 H.264/AVC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466

12.7.1 Brief history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 466

12.7.2 Primary features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467

12.7.3 Network abstraction and bitstream syntax . . . . . . . . . . . . . . . 468

12.7.4 Pictures and partitions . . . . . . . . . . . . . . . . . . . . . . . . . . 469

12.7.5 The Video coding layer . . . . . . . . . . . . . . . . . . . . . . . . . 472

12.7.6 Profiles and levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474

12.7.7 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476

12.7.8 Scalable extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476

12.7.9 Multiview extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . 476

12.8 H.265/HEVC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 478

12.8.1 Brief background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 478

12.8.2 Primary features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 478

12.8.3 Network abstraction and high level syntax . . . . . . . . . . . . . . . 479

12.8.4 Pictures and partitions . . . . . . . . . . . . . . . . . . . . . . . . . . 480

12.8.5 The Video Coding Layer (VCL) . . . . . . . . . . . . . . . . . . . . . 482

12.8.6 Profiles and Levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . 489

12.8.7 Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 490

12.8.8 Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491

12.9 H.266/VVC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491

12.9.1 Brief background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491

12.9.2 Primary features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491

12.9.3 High-level syntax . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 492

12.9.4 Picture partitioning . . . . . . . . . . . . . . . . . . . . . . . . . . . 492

12.9.5 Intra prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493

12.9.6 Inter prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495

12.9.7 Transformation and quantization . . . . . . . . . . . . . . . . . . . . 496

12.9.8 Entropy coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497

12.9.9 In-loop filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497

12.9.10Coding tools for 360 video . . . . . . . . . . . . . . . . . . . . . . . 499

12.9.11 Profiles, tiers and levels . . . . . . . . . . . . . . . . . . . . . . . . . 499

12.9.12Performance gains for VVC over recent standards . . . . . . . . . . . 499

12.10The Alliance of Open Media (AOM) . . . . . . . . . . . . . . . . . . . . . . 501

12.10.1VP9 and VP10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501

12.10.2AV1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501

12.11Other standardized and proprietary codecs . . . . . . . . . . . . . . . . . . . 502

12.11.1VC-1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502

12.11.2 Dirac or VC-2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503

12.11.3 RealVideo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503

12.12Codec comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503

12.13Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504

CONTENTS xiii

13 COMMUNICATING PICTURES - THE FUTURE 509

13.1 The motivation: more immersive experiences . . . . . . . . . . . . . . . . . 509

13.2 New formats and extended video parameter spaces . . . . . . . . . . . . . . 510

13.2.1 Influences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 510

13.2.2 Spatial resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511

13.2.3 Temporal resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . 513

13.2.4 Dynamic range . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 516

13.2.5 360 degree video . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 520

13.2.6 Parameter interactions and the creative continuum . . . . . . . . . . 521

13.3 Intelligent video compression . . . . . . . . . . . . . . . . . . . . . . . . . . 522

13.3.1 Challenges for compression: understanding content and context . . 522

13.3.2 Parametric video compression . . . . . . . . . . . . . . . . . . . . . 525

13.3.3 Context based video compression . . . . . . . . . . . . . . . . . . . . 527

13.4 Deep video compression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527

13.4.1 The need for data - training datasets for compression . . . . . . . . . 528

13.4.2 Deep optimization of compression tools . . . . . . . . . . . . . . . . 529

13.4.3 End to end architectures for deep image compression . . . . . . . . 534

13.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535

Details

No. of pages:
522
Language:
English
Copyright:
© Academic Press 2021
Published:
1st March 2021
Imprint:
Academic Press
Paperback ISBN:
9780128203538

About the Authors

David Bull

David Bull

Professor David R. Bull PhD, FIET, FIEEE, CEng. obtained his PhD from the University of Cardiff in 1988. He currently holds the Chair in Signal Processing at the University of Bristol where he is head of the Visual Information Laboratory and Director of Bristol Vision Institute, a group of some 150 researchers in vision science, spanning engineering, psychology, biology, medicine and the creative arts. In 1996 David helped to establish the UK DTI Virtual Centre of Excellence in Digital Broadcasting and Multimedia Technology and was one of its Directors from 1997-2000. He has advised Government through membership of the UK Foresight Panel, DSAC and the HEFCE Research Evaluation Framework. He is also now Director of the UK Government’s new MyWorld Strength in Places programme. David has worked widely across image and video processing focused on streaming, broadcast and wireless applications. He has published over 600 academic papers, various articles and 4 books and has given numerous invited/keynote lectures and tutorials. He has also received awards including the IEE Ambrose Fleming Premium for his work on Primitive Operator Digital Filters and a best Paper Award for his work on Link Adaptation for Video Transmission. David’s work has been exploited commercially and he has acted as a consultant for companies and governments across the globe. In 2001, he co-founded ProVision Communication Technologies Ltd., who launched the world’s first robust multi-source wireless HD sender for consumer use. His recent award-winning and pioneering work on perceptual video compression using deep learning, has produced world-leading rate-quality performance.

Affiliations and Expertise

University of Bristol, UK

Fan Zhang

Dr. Fan (Aaron) Zhang PhD received the B.Sc. (Hons) and M.Sc. degrees from Shanghai Jiao Tong University (2005 and 2008 respectively), and his Ph.D from the University of Bristol (2012). He is currently a Research Fellow in the Visual Information Laboratory at the University of Bristol, working on video compression and immersive video processing. His research interests include perceptual video compression, video quality assessment and immersive video formats. Aaron has published over 30 academic papers and has contributed to two books previous books on video compression. His work on super-resolutionbased video compression, has contributed to international standardization processes and he was a co-winner of the 2017 IEEE Grand Challenge on Video Compression.

Affiliations and Expertise

School of Astronautics, Northwestern Polytechnical University, China

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