數(shù)字視頻處理（英文版 第2版）

Contents
1 Multi-Dimensional Signals and Systems 1
1.1 Multi-Dimensional Signals 2
1.1.1 Finite-Extent Signals and Periodic Signals 2
1.1.2 Symmetric Signals 5
1.1.3 Special Multi-Dimensional Signals 5
1.2 Multi-Dimensional Transforms 8
1.2.1 Fourier Transform of Continuous Signals 8
1.2.2 Fourier Transform of Discrete Signals 12
1.2.3 Discrete Fourier Transform (DFT) 14
1.2.4 Discrete Cosine Transform (DCT) 18
1.3 Multi-Dimensional Systems 20
1.3.1 Impulse Response and 2D Convolution 20
1.3.2 Frequency Response 23
1.3.3 FIR Filters and Symmetry 25
1.3.4 IIR Filters and Partial Difference Equations 27
1.4 Multi-Dimensional Sampling Theory 30
1.4.1 Sampling on a Lattice 30
1.4.2 Spectrum of Signals Sampled on a Lattice 34
1.4.3 Nyquist Criterion for Sampling on a Lattice 36
1.4.4 Reconstruction from Samples on a Lattice 41
1.5 Sampling Structure Conversion 42
References 47
Exercises 48
Problem Set 1 48
MATLAB Exercises 50
2 Digital Images and Video 53
2.1 Human Visual System and Color 54
2.1.1 Color Vision and Models 54
2.1.2 Contrast Sensitivity 57
2.1.3 Spatio-Temporal Frequency Response 59
2.1.4 Stereo/Depth Perception 62
2.2 Digital Video 63
2.2.1 Spatial Resolution and Frame Rate 64
2.2.2 Color, Dynamic Range, and Bit-Depth 65
2.2.3 Color Image Processing 67
2.2.4 Digital-Video Standards 70
2.3 3D Video 75
2.3.1 3D-Display Technologies 75
2.3.2 Stereoscopic Video 79
2.3.3 Multi-View Video 79
2.4 Digital-Video Applications 81
2.4.1 Digital TV 81
2.4.2 Digital Cinema 85
2.4.3 Video Streaming over the Internet 88
2.4.4 Computer Vision and Scene/Activity Understanding 91
2.5 Image and Video Quality 92
2.5.1 Visual Artifacts 92
2.5.2 Subjective Quality Assessment 93
2.5.3 Objective Quality Assessment 94
References 96
Image Filtering 101
3.1 Image Smoothing 102
3.1.1 Linear Shift-Invariant Low-Pass Filtering 102
3.1.2 Bi-Lateral Filtering 105
3.2 Image Re-Sampling and Multi-Resolution Representations 106
3.2.1 Image Decimation 107
3.2.2 Interpolation 109
3.2.3 Multi-Resolution Pyramid Representations 116
3.2.4 Wavelet Representations 117
3.3 Image-Gradient Estimation, Edge and Feature Detection 123
3.3.1 Estimation of the Image Gradient 124
3.3.2 Estimation of the Laplacian 128
3.3.3 Canny Edge Detection 130
3.3.4 Harris Corner Detection 131
3.4 Image Enhancement 133
3.4.1 Pixel-Based Contrast Enhancement 133
3.4.2 Spatial Filtering for Tone Mapping and Image Sharpening 138
3.5 Image Denoising 143
3.5.1 Image and Noise Models 144
3.5.2 Linear Space-Invariant Filters in the DFT Domain 146
3.5.3 Local Adaptive Filtering 149
3.5.4 Nonlinear Filtering: Order-Statistics, Wavelet Shrinkage, and Bi-Lateral Filtering 154
3.5.5 Non-Local Filtering: NL-Means and BM3D 158
3.6 Image Restoration 160
3.6.1 Blur Models 161
3.6.2 Restoration of Images Degraded by Linear Space-Invariant Blurs 165
3.6.3 Blind Restoration – Blur Identification 171
3.6.4 Restoration of Images Degraded by Space-Varying Blurs 173
3.6.5 Image In-Painting 176
References 177
Exercises 182
Problem Set 3 182
MATLAB Exercises 185
MATLAB Resources 189
4 Motion Estimation 191
4.1 Image Formation 192
4.1.1 Camera Models 192
4.1.2 Photometric Effects of 3D Motion 197
4.2 Motion Models 198
4.2.1 Projected Motion vs. Apparent Motion 199
4.2.2 Projected 3D Rigid-Motion Models 203
4.2.3 2D Apparent-Motion Models 206
4.3 2D Apparent-Motion Estimation 210
4.3.1 Sparse Correspondence, Optical-Flow Estimation, and Image-Registration Problems 210
4.3.2 Optical-Flow Equation and Normal Flow 213
4.3.3 Displaced-Frame Difference 215
4.3.4 Motion Estimation is Ill-Posed: Occlusion and Aperture Problems 216
4.3.5 Hierarchical Motion Estimation 219
4.3.6 Performance Measures for Motion Estimation 220
4.4 Differential Methods 221
4.4.1 Lukas–Kanade Method 221
4.4.2 Horn–Schunk Motion Estimation 226
4.5 Matching Methods 229
4.5.1 Basic Block-Matching 230
4.5.2 Variable-Size Block-Matching 234
4.5.3 Hierarchical Block-Matching 236
4.5.4 Generalized Block-Matching – Local Deformable Motion 237
4.5.5 Homography Estimation from Feature Correspondences 239
4.6 Nonlinear Optimization Methods 241
4.6.1 Pel-Recursive Motion Estimation 241
4.6.2 Bayesian Motion Estimation 243
4.7 Transform-Domain Methods 245
4.7.1 Phase-Correlation Method 245
4.7.2 Space-Frequency Spectral Methods 247
4.8 3D Motion and Structure Estimation 247
4.8.1 Camera Calibration 248
4.8.2 Affine Reconstruction 249
4.8.3 Projective Reconstruction 251
4.8.4 Euclidean Reconstruction 256
4.8.5 Planar-Parallax and Relative Affine Structure Reconstruction 257
4.8.6 Dense Structure from Stereo 259
References 259
Exercises 264
Problem Set 4 264
MATLAB Exercises 266
MATLAB Resources 268
5 Video Segmentation and Tracking 269
5.1 Image Segmentation 271
5.1.1 Thresholding 271
5.1.2 Clustering 273
5.1.3 Bayesian Methods 277
5.1.4 Graph-Based Methods 281
5.1.5 Active-Contour Models 283
5.2 Change Detection 285
5.2.1 Shot-Boundary Detection 285
......