Image and Video Compression for Multimedia Engineering Fundamentals Algorithms and Standards 3rd Edition by Yun Qing Shi, Huifang Sun ISBN 1138299596 9781138299597 by Shi, Yun Qing; Sun, Huifang 9781138299597, 1138299596 instant download after payment.

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ISBN 10: 1138299596 
ISBN 13: 9781138299597
Author: Yun Qing Shi, Huifang Sun

The latest edition provides a comprehensive foundation for image and video compression. It covers HEVC/H.265 and future video coding activities, in addition to Internet Video Coding. The book features updated chapters and content, along with several new chapters and sections. It adheres to the current international standards, including the JPEG standard.

Image and Video Compression for Multimedia Engineering Fundamentals Algorithms and Standards 3rd Table of contents:

Part I Fundamentals
1. Introduction
1.1 Practical Needs for Image and Video Compression
1.2 Feasibility of Image and Video Compression
1.2.1 Statistical Redundancy
1.2.1.1 Spatial Redundancy
1.2.1.2 Temporal Redundancy
1.2.1.3 Coding Redundancy
1.2.2 Psychovisual Redundancy
1.2.2.1 Luminance Masking
1.2.2.2 Texture Masking
1.2.2.3 Frequency Masking
1.2.2.4 Temporal Masking
1.2.2.5 Color Masking
1.2.2.6 Color Masking and Its Application in Video Compression
1.2.2.7 Summary: Differential Sensitivity
1.3 Visual Quality Measurement
1.3.1 Subjective Quality Measurement
1.3.2 Objective Quality Measurement
1.3.2.1 Signal-to-Noise Ratio
1.3.2.2 An Objective Quality Measure Based on Human Visual Perception
1.4 Information Theory Results
1.4.1 Entropy
1.4.1.1 Information Measure
1.4.1.2 Average Information per Symbol
1.4.2 Shannon’s Noiseless Source Coding Theorem
1.4.3 Shannon’s Noisy Channel Coding Theorem
1.4.4 Shannon’s Source Coding Theorem
1.4.5 Information Transmission Theorem
1.5 Summary
Exercises
References
2. Quantization
2.1 Quantization and the Source Encoder
2.2 Uniform Quantization
2.2.1 Basics
2.2.1.1 Definitions
2.2.1.2 Quantization Distortion
2.2.1.3 Quantizer Design
2.2.2 Optimum Uniform Quantizer
2.2.2.1 Uniform Quantizer with Uniformly Distributed Input
2.2.2.2 Conditions of Optimum Quantization
2.2.2.3 Optimum Uniform Quantizer with Different Input Distributions
2.3 Nonuniform Quantization
2.3.1 Optimum (Nonuniform) Quantization
2.3.2 Companding Quantization
2.4 Adaptive Quantization
2.4.1 Forward Adaptive Quantization
2.4.2 Backward Adaptive Quantization
2.4.3 Adaptive Quantization with a One-Word Memory
2.4.4 Switched Quantization
2.5 PCM
2.6 Summary
Exercises
References
3. Differential Coding
3.1 Introduction to DPCM
3.1.1 Simple Pixel-to-Pixel DPCM
3.1.2 General DPCM Systems
3.2 Optimum Linear Prediction
3.2.1 Formulation
3.2.2 Orthogonality Condition and Minimum Mean Square Error
3.2.3 Solution to Yule-Walker Equations
3.3 Some Issues in the Implementation of DPCM
3.3.1 Optimum DPCM System
3.3.2 1-D, 2-D, and 3-D DPCM
3.3.3 Order of Predictor
3.3.4 Adaptive Prediction
3.3.5 Effect of Transmission Errors
3.4 Delta Modulation
3.5 Interframe Differential Coding
3.5.1 Conditional Replenishment
3.5.2 3-D DPCM
3.5.3 Motion-Compensated Predictive Coding
3.6 Information-Preserving Differential Coding
3.7 Summary
Exercises
References
4. Transform Coding
4.1 Introduction
4.1.1 Hotelling Transform
4.1.2 Statistical Interpretation
4.1.3 Geometrical Interpretation
4.1.4 Basis Vector Interpretation
4.1.5 Procedures of Transform Coding
4.2 Linear Transforms
4.2.1 2-D Image Transformation Kernel
4.2.1.1 Separability
4.2.1.2 Symmetry
4.2.1.3 Matrix Form
4.2.1.4 Orthogonality
4.2.2 Basis Image Interpretation
4.2.3 Sub-image Size Selection
4.3 Transforms of Particular Interest
4.3.1 Discrete Fourier Transform
4.3.2 Discrete Walsh Transform
4.3.3 Discrete Hadamard Transform
4.3.4 Discrete Cosine Transform
4.3.4.1 Background
4.3.4.2 Transformation Kernel
4.3.4.3 Relationship with DFT
4.3.5 Performance Comparison
4.3.5.1 Energy Compaction
4.3.5.2 Mean Square Reconstruction Error
4.3.5.3 Computational Complexity
4.3.5.4 Summary
4.4 Bit Allocation
4.4.1 Zonal Coding
4.4.2 Threshold Coding
4.4.2.1 Thresholding and Shifting
4.4.2.2 Normalization and Roundoff
4.4.2.3 Zigzag Scan
4.4.2.4 Huffman Coding
4.4.2.5 Special Codewords
4.4.2.6 Rate Buffer Feedback and Equalization
4.5 Some Issues
4.5.1 Effect of Transmission Error
4.5.2 Reconstruction Error Sources
4.5.3 Comparison Between DPCM and TC
4.5.4 Hybrid Coding
4.6 Summary
Exercises
References
5. Variable-Length Coding: Information Theory Results (II)
5.1 Some Fundamental Results
5.1.1 Coding an Information Source
5.1.2 Some Desired Characteristics
5.1.2.1 Block Code
5.1.2.2 Uniquely Decodable Code
5.1.2.3 Instantaneous Codes
5.1.2.4 Compact Code
5.1.3 Discrete Memoryless Sources
5.1.4 Extensions of a Discrete Memoryless Source
5.1.4.1 Definition
5.1.4.2 Entropy
5.1.4.3 Noiseless Source Coding Theorem
5.2 Huffman Codes
5.2.1 Required Rules for Optimum Instantaneous Codes
5.2.2 Huffman Coding Algorithm
5.2.2.1 Procedures
5.2.2.2 Comments
5.2.2.3 Applications
5.3 Modified Huffman Codes
5.3.1 Motivation
5.3.2 Algorithm
5.3.3 Codebook Memory Requirement
5.3.4 Bounds on Average Codeword Length
5.4 Arithmetic Codes
5.4.1 Limitations of Huffman Coding
5.4.2 The Principle of Arithmetic Coding
5.4.2.1 Dividing Interval (0,1) into Subintervals
5.4.2.2 Encoding
5.4.2.3 Decoding
5.4.2.4 Observations
5.4.3 Implementation Issues
5.4.3.1 Incremental Implementation
5.4.3.2 Finite Precision
5.4.3.3 Other Issues
5.4.4 History
5.4.5 Applications
5.5 Summary
Exercises
References
6. Run-Length and Dictionary Coding: Information Theory Results (III)
6.1 Markov Source Model
6.1.1 Discrete Markov Source
6.1.2 Extensions of a Discrete Markov Source
6.1.2.1 Definition
6.1.2.2 Entropy
6.1.3 Autoregressive Model
6.2 Run-Length Coding
6.2.1 1-D Run-Length Coding
6.2.2 2-D Run-Length Coding
6.2.2.1 Five Changing Pixels
6.2.2.2 Three Coding Modes
6.2.3 Effect of Transmission Error and Uncompressed Mode
6.2.3.1 Error Effect in the 1-D RLC Case
6.2.3.2 Error Effect in the 2-D RLC Case
6.2.3.3 Uncompressed Mode
6.3 Digital Facsimile Coding Standards
6.4 Dictionary Coding
6.4.1 Formulation of Dictionary Coding
6.4.2 Categorization of Dictionary-Based Coding Techniques
6.4.2.1 Static Dictionary Coding
6.4.2.2 Adaptive Dictionary Coding
6.4.3 Parsing Strategy
6.4.4 Sliding Window (LZ77) Algorithms
6.4.4.1 Introduction
6.4.4.2 Encoding and Decoding
6.4.4.3 Summary of the LZ77 Approach
6.4.5 LZ78 Algorithms
6.4.5.1 Introduction
6.4.5.2 Encoding and Decoding
6.4.5.3 LZW Algorithm
6.4.5.4 Summary
6.4.5.5 Applications
6.5 International Standards for Lossless Still Image Compression
6.5.1 Lossless Bilevel Still Image Compression
6.5.1.1 Algorithms
6.5.1.2 Performance Comparison
6.5.2 Lossless Multilevel Still Image Compression
6.5.2.1 Algorithms
6.5.2.2 Performance Comparison
6.6 Summary
Exercises
References
7. Some Material Related to Multimedia Engineering
7.1 Digital Watermarking
7.1.1 Where to Embed Digital Watermark
7.1.2 Watermark Signal with One Random Binary Sequence
7.1.3 Challenge Faced by Digital Watermarking
7.1.4 Watermark Embedded into the DC Component
7.1.5 Digital Watermark with Multiple Information Bits and Error Correction Coding
7.1.6 Conclusion
7.2 Reversible Data Hiding
7.3 Information Forensics
References
Part II Still Image Compression
8. Still Image Coding Standard—JPEG
8.1 Introduction
8.2 Sequential DCT-Based Encoding Algorithm
8.3 Progressive DCT-Based Encoding Algorithm
8.4 Lossless Coding Mode
8.5 Hierarchical Coding Mode
8.6 Summary
Exercises
References
9. Wavelet Transform for Image Coding: JPEG2000
9.1 A Review of Wavelet Transform
9.1.1 Definition and Comparison with Short-Time Fourier Transform
9.1.2 Discrete Wavelet Transform
9.1.3 Lifting Scheme
9.1.3.1 Three Steps in Forward Wavelet Transform
9.1.3.2 Inverse Transform
9.1.3.3 Lifting Version of CDF (2,2)
9.1.3.4 A Numerical Example
9.1.3.5 (5,3) Integer Wavelet Transform
9.1.3.6 A Demonstration Example of (5,3) IWT
9.1.3.7 Summary
9.2 Digital Wavelet Transform for Image Compression
9.2.1 Basic Concept of Image Wavelet Transform Coding
9.2.2 Embedded Image Wavelet Transform Coding Algorithms
9.2.2.1 Early Wavelet Image Coding Algorithms and Their Drawbacks
9.2.2.2 Modern Wavelet Image Coding
9.2.2.3 Embedded Zerotree Wavelet Coding
9.2.2.4 Set Partitioning in Hierarchical Trees Coding
9.3 Wavelet Transform for JPEG-2000
9.3.1 Introduction of JPEG2000
9.3.1.1 Requirements of JPEG-2000
9.3.1.2 Parts of JPEG-2000
9.3.2 Verification Model of JPEG2000
9.3.3 An Example of Performance Comparison between JPEG and JPEG2000
9.4 Summary
Exercises
References
10. Non-standardized Still Image Coding
10.1 Introduction
10.2 Vector Quantization
10.2.1 Basic Principle of Vector Quantization
10.2.1.1 Vector Formation
10.2.1.2 Training Set Generation
10.2.1.3 Codebook Generation
10.2.1.4 Quantization
10.2.2 Several Image Coding Schemes with Vector Quantization
10.2.2.1 Residual VQ
10.2.2.2 Classified VQ
10.2.2.3 Transform Domain VQ
10.2.2.4 Predictive VQ
10.2.2.5 Block Truncation Coding
10.2.3 Lattice VQ for Image Coding
10.3 Fractal Image Coding
10.3.1 Mathematical Foundation
10.3.2 IFS-Based Fractal Image Coding
10.3.3 Other Fractal Image Coding Methods
10.4 Model-Based Coding
10.4.1 Basic Concept
10.4.2 Image Modeling
10.5 Summary
Exercises
References
Part III Motion Estimation and Compensation
11. Motion Analysis and Motion Compensation
11.1 Image Sequences
11.2 Interframe Correlation
11.3 Frame Replenishment
11.4 Motion-Compensated Coding
11.5 Motion Analysis
11.5.1 Biological Vision Perspective
11.5.2 Computer Vision Perspective
11.5.3 Signal Processing Perspective
11.6 Motion Compensation for Image Sequence Processing
11.6.1 Motion-Compensated Interpolation
11.6.2 Motion-Compensated Enhancement
11.6.3 Motion-Compensated Restoration
11.6.4 Motion-Compensated Down-Conversion
11.7 Summary
Exercises
References
12. Block Matching
12.1 Non-overlapped, Equally Spaced, Fixed-Size, Small Rectangular Block Matching
12.2 Matching Criteria
12.3 Searching Procedures
12.3.1 Full Search
12.3.2 2-D Logarithm Search
12.3.3 Coarse-Fine Three-Step Search
12.3.4 Conjugate Direction Search
12.3.5 Subsampling in the Correlation Window
12.3.6 Multiresolution Block Matching
12.3.7 Thresholding Multiresolution Block Matching
12.3.7.1 Algorithm
12.3.7.2 Threshold Determination
12.3.7.3 Thresholding
12.3.7.4 Experiments
12.4 Matching Accuracy
12.5 Limitations with Block-Matching Techniques
12.6 New Improvements
12.6.1 Hierarchical Block Matching
12.6.2 Multigrid Block Matching
12.6.2.1 Thresholding Multigrid Block Matching
12.6.2.2 Optimal Multigrid Block Matching
12.6.3 Predictive Motion Field Segmentation
12.6.4 Overlapped Block Matching
12.7 Summary
Exercises
References
13. Pel-Recursive Technique
13.1 Problem Formulation
13.2 Descent Methods
13.2.1 First-Order Necessary Conditions
13.2.2 Second-Order Sufficient Conditions
13.2.3 Underlying Strategy
13.2.4 Convergence Speed
13.2.4.1 Order of Convergence
13.2.4.2 Linear Convergence
13.2.5 Steepest Descent Method
13.2.5.1 Formulae
13.2.5.2 Convergence Speed
13.2.5.3 Selection of Step Size
13.2.6 Newton-Raphson’s Method
13.2.6.1 Formulae
13.2.6.2 Convergence Speed
13.2.6.3 Generalization and Improvements
13.2.7 Other Methods
13.3 Netravali-Robbins’ Pel-Recursive Algorithm
13.3.1 Inclusion of a Neighborhood Area
13.3.2 Interpolation
13.3.3 Simplification
13.3.4 Performance
13.4 Other Pel-Recursive Algorithms
13.4.1 Bergmann’s Algorithm (1982)
13.4.2 Bergmann’s Algorithm (1984)
13.4.3 Cafforio and Rocca’s Algorithm
13.4.4 Walker and Rao’s algorithm
13.5 Performance Comparison
13.6 Summary
Exercises
References
14. Optical Flow
14.1 Fundamentals
14.1.1 2-D Motion and Optical Flow
14.1.2 Aperture Problem
14.1.3 Ill-Posed Problem
14.1.4 Classification of Optical-Flow Techniques
14.2 Gradient-Based Approach
14.2.1 Horn and Schunck’s Method
14.2.1.1 Brightness Invariance Equation
14.2.1.2 Smoothness Constraint
14.2.1.3 Minimization
14.2.1.4 Iterative Algorithm
14.2.2 Modified Horn and Schunck Method
14.2.3 Lucas and Kanade’s Method
14.2.4 Nagel’s Method
14.2.5 Uras, Girosi, Verri, and Torre’s Method
14.3 Correlation-Based Approach
14.3.1 Anandan’s Method
14.3.2 Singh’s Method
14.3.2.1 Conservation Information
14.3.2.2 Neighborhood Information
14.3.2.3 Minimization and Iterative Algorithm
14.3.3 Pan, Shi, and Shu’s Method
14.3.3.1 Proposed Framework
14.3.3.2 Implementation and Experiments
14.3.3.3 Discussion and Conclusion
14.4 Multiple Attributes for Conservation Information
14.4.1 Weng, Ahuja, and Huang’s Method
14.4.2 Xia and Shi’s Method
14.4.2.1 Multiple Image Attributes
14.4.2.2 Conservation Stage
14.4.2.3 Propagation Stage
14.4.2.4 Outline of Algorithm
14.4.2.5 Experimental Results
14.4.2.6 Discussion and Conclusion
14.5 Summary
Exercises
References
15. Further Discussion and Summary on 2-D Motion Estimation
15.1 General Characterization
15.1.1 Aperture Problem
15.1.2 Ill-Posed Inverse Problem
15.1.3 Conservation Information and Neighborhood Information
15.1.4 Occlusion and Disocclusion
15.1.5 Rigid and Nonrigid Motion
15.2 Different Classifications
15.2.1 Deterministic Methods vs. Stochastic Methods
15.2.2 Spatial Domain Methods vs. Frequency Domain Methods
15.2.2.1 Optical-Flow Determination Using Gabor Energy Filters
15.2.3 Region-Based Approaches vs. Gradient-Based Approaches
15.2.4 Forward vs. Backward Motion Estimation
15.3 Performance Comparison between Three Major Approaches
15.3.1 Three Representatives
15.3.2 Algorithm Parameters
15.3.3 Experimental Results and Observations
15.4 New Trends
15.4.1 DCT-Based Motion Estimation
15.4.1.1 DCT and DST Pseudophases
15.4.1.2 Sinusoidal Orthogonal Principle
15.4.1.3 Performance Comparison
15.5 Summary
Exercises
References
Part IV Video Compression
16. Fundamentals of Digital Video Coding
16.1 Digital Video Representation
16.2 Information Theory Results: Rate Distortion Function of Video Signal
16.3 Digital Video Formats
16.3.1 Digital Video Color Systems
16.3.2 Progressive and Interlaced Video Signals
16.3.3 Video Formats Used by Video Industry
16.4 Current Status of Digital Video/Image Coding Standards
16.5 Summary

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Tags: Yun Qing Shi, Huifang Sun, Compression, Multimedia