数字视频处理

Contents
Preface
About the Author
About the Notation
REPRESENTATION OF DIGITAL VIDEO
1 BASICS OF VIDEO
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Contents
Preface
About the Author
About the Notation
REPRESENTATION OF DIGITAL VIDEO
1 BASICS OF VIDEO
1.1 Analog Video
1.1.1 Analog Video Signal
1.1.2 Analog Video Standards
1.1.3 Analog Video Equipment
1.2 Digital Video
1.2.1 Digital Video Signal
1.2.2 Digital Video Standards
1.2.3 Why Digital Video?
1.3 Digital Video Processing
2 TIME-VARYING IMAGE FORMATION MODELS
2.1 Three-Dimensional Motion Models
2.1.1 Rigid Motion in the Cartesian Coordinates
2.1.2 Rigid Motion in the Homogeneous Coordinates
2.1.3 Deformable Motion
2.2 Geometric Image Formation
2.2.1 Perspective Projection
2.2.2 Orthographic Projection
2.3 Photometric Image Formation
2.3.1 Lambertian Reflectance Model
2.3.2 Photometric Effects of 3-D Motion
2.4 Observation Noise
2.5 Exercises
3 SPATIO-TEMPORAL SAMPLING
3.1 Sampling for Analog and Digital Video
3.1.1 Sampling Structures for Analog Video
3.1.2 Sampling Structures for Digital Video
3.2 Two-Dimensional Rectangular Sampling
3.2.1 2-D Fourier Transform Relations
3.2.2 Spectrum of the Sampled Signal
3.3 Two-Dimensional Periodic Sampling
3.3.1 Sampling Geometry
3.3.2 2-D Fourier Transform Relations in Vector Form
3.3.3 Spectrum of the Sampled Signal
3.4 Sampling on 3-D Structures
3.4.1 Sampling on a Lattice
3.4.2 Fourier Transform on a Lattice
3.4.3 Spectrum of Signals Sampled on a Lattice
3.4.4 Other Sampling Structures
3.5 Reconstruction from Samples
3.5.1 Reconstruction from Rectangular Samples
3.5.2 Reconstruction from Samples on a Lattice
3.6 Exercises
4 SAMPLING STRUCTURE CONVERSION
4.1 Sampling Rate Change for l-D Signals
4.1.1 Interpolation of l-D Signals
4.1.2 Decimation of l-D Signals
4.1.3 Sampling Rate Change by a Rational Factor
4.2 Sampling Lattice Conversion
4.3 Exercises
5 TWO-DIMENSIONAL MOTION ESTIMATION
OPTICAL FLOW METHODS
5.1 2-D Motion vs. Apparent Motion
5.1.1 2-D Motion
5.1.2 Correspondence and Optical Flow
5.2 2-D Motion Estimation
5.2.1 The Occlusion Problem
5.2.2 The Aperture Problem
5.2.3 Two-Dimensional Motion Field Models
5.3 Methods Using the Optical Flow Equation
5.3.1 The Optical Flow Equation
5.3.2 Second-Order Differential Methods
5.3.3 Block Motion Model
5.3.4 Horn and Schunck Method
5.3.5 Estimation of the Gradients
5.3.6 Adaptive Methods
5.4 Examples
5.5 Exercises
6 BLOCK-BASED METHODS
6.1 Block-Motion Models
6.1.1 Translational Block Motion
6.1.2 Generalized/Deformable Block Motion
6.2 Phase-Correlation Method
6.2.1 The Phase-Correlation Function
6.2.2 Implementation Issues
6.3 Block-Matching Method
6.3.1 Matching Criteria
6.3.2 Search Procedures
6.4 Hierarchical Motion Estimation
6.5 Generalized Block-Motion Estimation
6.5.1 Postprocessing for Improved Motion Compensation
6.5.2 Deformable Block Matching
6.6 Examples
6.7 Exercises
7 PEL-RECURSIVE METHODS
7.1 Displaced Frame Difference
7.2 Gradient-Based Optimization
7.2.1 Steepest-Descent Method
7.2.2 Newton-Raphson Method
7.2.3 Local vs. Global Minima
7.3 Steepest-Descent-Based Algorithms
7.3.1 Netravali-Robbins Algorithm
7.3.2 Walker-Rao Algorithm
7.3.3 Extension to the Block Motion Model
7.4 Wiener-Estimation-Based Algorithms
7.5 Examples
7.6 Exercises
8 BAYESIAN METHODS
8.1 Optimization Methods
8.1.1 Simulated Annealing
8.1.2 Iterated Conditional Modes
8.1.3 Mean Field Annealing
8.1.4 Highest Confidence First
8.2 Basics of MAP Motion Estimation
8.2.1 The Likelihood Model
8.2.2 The Prior Model
8.3 MAP Motion Estimation Algorithms
8.3.1 Formulation with Discontinuity Model,
8.3.2 Estimation with Local Outlier Rejection
8.3.3 Estimation with Region Labeling
8.4 Examples
8.5 Exercises
III THREE-DIMENSIONAL MOTION ESTIMATION
AND SEGMENTATION
9 METHODS USING POINT CORRESPONDENCES
9.1 Modeling the Projected Displacement Field
9.1.1 Orthographic Displacement Field Model
9.1.2 Perspective Displacement Field Model
9.2 Methods Based on the Orthographic Model
9.2.1 Two-Step Iteration Method from Two Views
9.2.2 An Improved Iterative Method
9.3 Methods Based on the Perspective Model
9.3.1 The Epipolar Constraint and Essential Parameters
9.3.2 Estimation ofthe Essential Pararneters
9.3.3 Decomposition of the E-Matrix
9.3.4 Algorithm
9.4 The Case of 3-D Planar Surfaces
9.4.1 The Pure Parameters
9.4.2 Estimation ofthe Pure Parameters
9.4.3 Estimation ofthe Motion and Structure Parameters
9.5 Examples
9.5.1 Numerical Simulations
9.5.2 Experiments with Two Frames of Miss America
9.6 Exercises
10 OPTICAL FLOW AND DIRECT METHODS
10.1 Modeling the Projected Velocity Field
10.1.1 Orthographic Velocity Field Model
10.1.2 Perspective Velocity Field Model
10.1.3 Perspective Velocity vs. Displacement Models
10.2 Focus of Expansion
10.3 Algebraic Methods Using Optical Flow
10.3.1 Uniqueness of the Solution
10.3.2 Affine Flow
10.3.3 Quadratic Flow
10.3.4 Arbitrary Flow
10.4 Optimization Methods Using Optical Flow
10.5 Direct Methods
10.5.1 Extension ofOptical Flow-Based Methods
10.5.2 Tsai-Huang Method
10.6 Examples
10.6.1 Numerical Simulations
10.6.2 Experiments with Two Frames of Miss America
10.7 Exercises
11 MOTION SEGMENTATION
11.1 Direct Methods
11.1.1 Thresholding for Change Detection
11.1.2 An Algorithm Using Mapping Parameters
11.1.3 Estimation of Model Parameters
11.2 Optical Flow Segmentation
11.2.1 Modified Hough Transform Method
11.2.2 Segmentation for Layered Video Representation .
11.2.3 Bayesian Segmentation
11.3 Simultaneous Estimation and Segmentation
11.3.1 Motion Field Model
11.3.2 Problem Formulation
11.3.3 The Algorithm
11.3.4 Relationship to Other Algorithms
11.4 Examples
11.5 Exercises
12 STEREO AND MOTION TRACKING
12.1 Motion and Structure from Stereo
12.1.1 Still-Frame Stereo Imaging
12.1.2 3-D Feature Matching fbr Motion Estimation
12.1.3 Stereo-Motion Fusion
12.1.4 Extension to Multiple Motion
12.2 Motion Tracking
12.2.1 Basic Principles
12.2.2 2-D Motion Tracking
12.2.3 3-D Rigid Motion Ttacking
12.3 Examples
12.4 Exercises
13 MOTION COMPENSATED FILTERING
13.1 Spatio-Temporal Fourier Spectrum
13.1.1 Global Motion with Constant Velocity
13.1.2 Global Motion with Acceleration
13.2 Sub-Nyquist Spatio-Temporal Sampling
13.2.1 Sampling in the Temporal Direction Only
13.2.2 Sampling on a Spatio-Temporal Lattice
13.2.3 Critical Velocities
13.3 Filtering Along Motion TRajectories
13.3.1 Arbitrary Motion Trajectories
13.3.2 Global Motion with Constant Velocity
13.3.3 Accelerated Motion
13.4 Applications
13.4.1 Motion-Compensated Noise Filtering
13.4.2 Motion-Compensated Reconstruction Filtering
13.5 Exercises
14 NOISE FILTERING
14.1 Intraframe Filtering
14.1.1 LMMSE Filtering
14.1.2 Adaptive (Local) LMMSE Filtering
14.1.3 Directional Filtering
14.1.4 Median and Weighted Median Filtering
14.2 Motion-Adaptive Filtering
14.2.1 Direct Filtering
14.2.2 Motion-Detection Based Filtering
14.3 Motion-Compenaated Filtering
14.3.1 Spatio-Temporal Adaptive LMMSE Filtering
14.3.2 Adaptive Weighted Averaging Filter
14.4 Examples
14.5 Exercises
15 RESTORATION
15.1 Modeling
15.1.1 Shift-Invariant Spatial Blurring
15.1.2 Shift-Varying Spatial Blurring
15.2 Intraframe Shift-Invariant Restoration
15.2.1 Pseudo Inverse Filtering
15.2.2 Constrained Least Squares and Wiener Filtering
15.3 Intraframe Shift-Varying Restoration
15.3.1 Overview ofthe POCS Method
15.3.2 Restoration Using POCS
15.4 Multiframe Restoration
15.4.1 Cross-Correlated Multiframe Filter
15.4.2 Motion-Compensated Multiframe Filter
15.5 Examples
15.6 Exercises
16 STANDARDS CONVERSION
16.1 Down-Conversion
16.1.1 Down-Conversion with Anti-Alias Filtering
16.1.2 Down-Conversion without Anti-Alias Filtering
16.2 Practical Up-Conversion Methods
16.2.1 Intraframe Filtering
16.2.2 Motion-Adaptive Filtering
16.3 Motion-Compensated Up-Conversion
16.3.1 Basic Principles
16.3.2 Global-Motion-Compensated De-interlacing
16.4 Examples
16.5 Exercises
17 SUPERRESOLUTION
17.1 Modeling
17.1.1 Continuous-Discrete Model
17.1.2 Discrete-Discrete Model
17.1.3 Problem Interrelations
17.2 Interpolation-Restoration Methods
17.2.1 Intraframe Methods
17.2.2 Multiframe Methods
17.3 A Frequency Domain Method
17.4 A Unifying POCS Method
17.5 Examples
17.6 Exercises
V STILL IMAGE COMPRESSION
18 LOSSLESS COMPRESSION
18.1 Basics of Image Compression
18.1.1 Elements of an Image Compression System
18.1.2 Information Theoretic Concepts
18.2 Symbol Coding
18.2.1 Fixed-Length Coding
18.2.2 Huffman Coding
18.2.3 Arithmetic Coding
18.3 Lossless Compression Methods
18.3.1 Lossless Predictive Coding
18.3.2 Run-Length Coding of Bit-Planes
18.3.3 Ziv-Lempel Coding
18.4 Exercises
19 DPCM AND TRANSFORM CODING
19.1 Quantization
19.1.1 Nonuniform Quantization
19.1.2 Uniform Quantization
19.2 Differential Pulse Code Modulation
19.2.1 Optimal Prediction
19.2.2 Quantization of the Prediction Error
19.2.3 Adaptive Quantization
19.2.4 Delta Modulation
19.3 Transform Coding
19.3.1 Discrete Cosine Transform
19.3.2 Quantization/Bit Allocation
19.3.3 Coding
19.3.4 Blocking Artifacts in Transform Coding
19.4 Exercises
20 STILL IMAGE COMPRESSION STANDARDS
20.1 Bilevel Image Compression Standards
20.1.1 One-Dimensional RLC
20.1.2 Two-Dimensional RLC
20.1.3 The JBIG Standard
20.2 The JPEG Standard
20.2.1 Baseline Algorithm
20.2.2 JPEG Progressive
20.2.3 JPEG Lossless
20.2.4 JPEG Hierarchical
20.2.5 ImplementationsofJPEG
20.3 Exercises
21 VECTOR QUANTIZATION, SUBBAND CODING
AND OTHER METHODS
21.1 Vector Quantization
21.1.1 Structure of a Vector Quantizer
21.1.2 VQ Codebook Design
21.1.3 Practical VQ Implementations
21.2 Fractal Compression
21.3 Subband Coding
21.3.1 Subband Decomposition
21.3.2 Coding of the Subbands
21.3.3 Relationship to Transform Coding
21.3.4 Relationship to Wavelet Transform Coding
21.4 Second-Generation Coding Methods
21.5 Exercises
VI VIDEO COMPRESSION
22 INTERFRAME COMPRESSION METHODS
22.1 Three-Dimensional Waveform Coding
22.1.1 3-D Transform Coding
22.1.2 3-D Subband Coding
22.2 Motion-Compensated Waveform Coding
22.2.1 MC Transform Coding
22.2.2 MC Vector Quantization
22.2.3 MC Subband Coding
22.3 Model-Based Coding
22.3.1 Object-Based Coding
22.3.2 Knowledge-Based and Semantic Coding
22.4 Exerclses
23 VIDEO COMPRESSION STANDARDS
23.1 The H.261 Standard
23.1.1 Input Image Formats
23.1.2 Video Multiplex
23.1.3 Video Compression Algorithm
23.2 The MPEG-l Standard
23.2.1 Features
23.2.2 Input Video Format
23.2.3 Data Structure and Compression Modes
23.2.4 Intraframe Compression Mode
23.2.5 Interframe Compression Modes
23.2.6 MPEG-l Encoder and Decoder
23.3 The MPEG-2 Standard
23.3.1 MPEG-2 Macroblocks
23.3.2 Coding Interlaced Video
23.3.3 Scalable Extensions
23.3.4 Other Improvements
23.3.5 Overview of Profiles and Levels
23.4 Software and Hardware Implementations
24 MODEL-BASED CODING
24.1 General Object-Based Methods
24.1.1 2-D/3-D Rigid Objects with 3-DMotion
24.1.2 2-D Flexible Objects with 2-D Motion
24.1.3 Affine Transformations with TRiangular Meshes
24.2 Knowledge-Based and Semantic Methods
24.2.1 General Principles
24.2.2 MBASIC Algorithm
24.2.3 Estimation Using a Flexible Wireframe Model
24.3 Examples
25 DIGITAL VIDEO SYSTEMS
25.1 Videoconferencing
25.2 Interactive Video and Multimedia
25.3 Digital Television
25.3.1 Oigital Studio Standards
25.3.2 Hybrid Advanced TV Systems
25.3.3 All-Oigital TV
25.4 Low-Bitrate Video and Videophone
25.4.1 The ITU Recommendation H.263
25.4.2 The ISO MPEG-4 Requirements
APPENDICES
A MARKOV AND GIBBS RANDOM FIELDS
A.l Definitions
A.l.l Markov Random Fields
A.1.2 Gibbs Random Fields
A.2 Equivalence of MRF and GRF
A.3Local Conditional Probabilities
B BASICS OF SEGMENTATION
B.l Thresholding
B.I.l Finding the Optimum Threshold(s)
B.2 Clustering
B.3 Bayesian Methods
B.3.1 The MAP Method
B.3.2 The Adaptive MAP Method
B.3.3 Vector Field Segmentation
C KALMAN FILTEMNG
C.l Linear State-Space Model
C.2 Extended Kalman Filtering
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