1st Edition

Advances in Visual Data Compression and Communication
Meeting the Requirements of New Applications

By

Feng Wu




ISBN 9781482234138
Published July 25, 2014 by Auerbach Publications
513 Pages 198 B/W Illustrations

USD $115.00

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Book Description

Visual information is one of the richest and most bandwidth-consuming modes of communication. To meet the requirements of emerging applications, powerful data compression and transmission techniques are required to achieve highly efficient communication, even in the presence of growing communication channels that offer increased bandwidth.

Presenting the results of the author’s years of research on visual data compression and transmission, Advances in Visual Data Compression and Communication: Meeting the Requirements of New Applications provides a theoretical and technical basis for advanced research on visual data compression and communication.

The book studies the drifting problem in scalable video coding, analyzes the reasons causing the problem, and proposes various solutions to the problem. It explores the author’s Barbell-based lifting coding scheme that has been adopted as common software by MPEG. It also proposes a unified framework for deriving a directional transform from the nondirectional counterpart. The structure of the framework and the statistic distribution of coefficients are similar to those of the nondirectional transforms, which facilitates subsequent entropy coding.

Exploring the visual correlation that exists in media, the text extends the current coding framework from different aspects, including advanced image synthesis—from description and reconstruction to organizing correlated images as a pseudo sequence. It explains how to apply compressive sensing to solve the data compression problem during transmission and covers novel research on compressive sensor data gathering, random projection codes, and compressive modulation.

For analog and digital transmission technologies, the book develops the pseudo-analog transmission for media and explores cutting-edge research on distributed pseudo-analog transmission, denoising in pseudo-analog transmission, and supporting MIMO. It concludes by considering emerging developments of information theory for future applications.

Table of Contents

Acronyms

BASIS FOR COMPRESSION AND COMMUNICATION

Information Theory
Introduction
Source Coding
     Huffman Coding
     Arithmetic Coding
     Rate Distortion Theory
Channel Coding
     Capacity
     Coding Theorem
     Hamming Codes
Joint Source and Channel Coding

Hybrid Video Coding
Hybrid Coding Framework
Technical Evolution
     H.261
     MPEG-1
     MPEG-2
     MPEG-4
     H.264/MPEG-4 AVC
     HEVC
     Performance versus Encoding Complexity
H.264 Standard
     Motion Compensation
     Intra Prediction
     Transform and Quantization
     Entropy Coding
     Deblocking Filtering
     Rate Distortion Optimization
HEVC Standard
     Motion Compensation
     Intra Prediction
     Transform and Quantization
     Sample Adaptive Offset Filter

Communication
Analog Communication
     Analog Modulation
     Multiplexing
Digital Communication
     Low-Density Parity-Check (LDPC) Codes
     Turbo Codes
     Digital Modulation

SCALABLE VIDEO CODING

Progressive Fine Granularity Scalable (PFGS) Coding
Introduction
Fine Granularity Scalable Video Coding
Basic PFGS Framework
     Basic Ideas to Build the PFGS Framework
     The Simplified PFGS Framework
Improvements to the PFGS Framework
     Potential Coding Inefficiency Due to Two References
     A More Efficient PFGS Framework
Implementation of the PFGS Encoder and Decoder
Experimental Results and Analyses
Simulation of Streaming PFGS Video over Wireless Channels
Summary

Motion Threading for 3D Wavelet Coding
Introduction
Motion Threading
Advanced Motion Threading
     Lifting-Based Motion Threading
     Many-to-One Mapping and Non-Referred Pixels
Multi-Layer Motion-Threading
Correlated Motion Estimation with R-D Optimization
     Definition of the Mode Types
     R-D Optimized Mode Decision
Experimental Results
     Coding Performance Comparison  
     Macroblock Mode Distribution
Summary

Barbell-Lifting Based 3D Wavelet Coding
Introduction
Barbell-Lifting Coding Scheme
     Barbell Lifting
     Layered Motion Coding
     Entropy Coding in Brief
     Base Layer Embedding
Comparisons with SVC
     Coding Framework
     Temporal Decorrelation
     Spatial Scalability
     Intra Prediction
Advances in 3D Wavelet Video Coding
     In-Scale MCTF
     Subband Adaptive MCTF
Experimental Results
     Comparison with Motion Compensated Embedded Zero Block Coding (MC-EZBC) 
     Comparison with Scalable Video Coding (SVC) for Signal-to-Noise Ratio (SNR) Scalability
     Comparison with SVC for Combined Scalability
Summary

PART III DIRECTIONAL TRANSFORMS

DirectionalWavelet Transform
Introduction
2D Wavelet Transform via Adaptive Directional Lifting
     ADL Structure
     Subpixel Interpolation
R-D Optimized Segmentation for ADL
Experimental Results and Observations
Summary

Directional DCT Transform
Introduction
Lifting-Based Directional DCT-Like Transform
     Lifting Structure of Discrete Cosine Transform (DCT)
     Directional DCT-Like transform
     Comparison with Rotated DCT
Image Coding with Proposed Directional Transform
     Direction Transition on Block Boundary
     Direction Selection
Experimental Results
Summary

Directional Filtering Transform
Introduction
Adaptive Directional Lifting-Based 2D Wavelet Transform
Mathematical Analysis
     Coding Gain of ADL
     Numerical Analysis
Directional Filtering Transform 
     Proposed Intra-Coding Scheme
     Directional Filtering
     Optional Transform
Experimental Results
Summary

VISION-BASED COMPRESSION

Edge-Based Inpainting
Introduction
The Proposed Framework
Edge Extraction and Exemplar Selection
Edge-Based Image Inpainting
     Structure
Experimental Results
Summary

Cloud-Based Image Compression
Introduction
Related Work
     Visual Content Generation
     Local Feature Compression
      Image Reconstruction
The Proposed SIFT-Based Image Coding
Extraction of Image Description
Compression of Image Descriptors
     Prediction Evaluation
     Compression of SIFT Descriptors
Image Reconstruction
     Patch Retrieval
     Patch Transformation
     Patch Stitching
Experimental Results and Analyses
     Compression Ratio
     Visual Quality
     Highly Correlated Image
      Complexity Analyses
     Comparison with SIFT Feature Vector Coding
Further Discussion
     Typical Applications
     Limitations
     Future Work
Summary

Compression for Cloud Photo Storage

Introduction
Related Work
     Image Set Compression
     Local Feature Descriptors
Proposed Scheme
Feature-Based Prediction Structure
     Graph Building
     Feature-Based Minimum Spanning Tree
     Prediction Structure
Feature-Based Inter-Image Prediction
     Feature-Based Geometric Deformations
     Feature-Based Photometric Transformation
     Block-Based Motion Compensation
Experimental Results 
     Efficiency of Multi-Model Prediction
     Efficiency of Photometric Transformation
     Overall Performance
     Complexity
Our Conjecture on Cloud Storage
Summary

COMPRESSIVE COMMUNICATION

Compressive Data Gathering
Introduction
Related Work
     Conventional Compression
     Distributed Source Coding 
     Compressive Sensing
Compressive Data Gathering
     Data Gathering
     Data Recovery
Network Capacity of Compressive Data Gathering
     Network Capacity Analysis
     NS-2 Simulation
Experiments on Real Data Sets
     CTD Data from the Ocean
     Temperature in the Data Center
Summary

Compressive Modulation
Introduction
Background
     Rate Adaptation
     Mismatched Decoding Problem
Compressive Modulation
     Coding and Modulation
     Soft Demodulation and Decoding
     Design RP Codes
Simulation Study
     Rate Adaptation Performance
      Sensitivity to SNR Estimation
Testbed Evaluation
     Comparison to Oracle
     Comparison to ADM
Related Work
     Coded Modulation
     Compressive Sensing
Summary

Joint Source and Channel Coding

Introduction
Related Work and Background
     Joint Source-Channel Coding
     Coded Modulation
     Rate Adaptation
     Compressive Sensing
Compressive Modulation (CM) for Sparse Binary Sources
     Design Principles
     Weight Selection
     Encoding Matrix Construction
Belief Propagation Decoding
Performance Evaluation
     Implementation
     Simulations over an AWGN Channel
     Emulation in Real Channel Environment
Summary

PSEUDO-ANALOG tRANSMISSION

DCast: Distributed Video Multicast
Introduction
Related Works
     Distributed Video Coding
     Distributed Video Transmission
     SoftCast
Proposed DCast
     Coset Coding
     Coset Quantization
     Power Allocation
     Packaging and Transmission
     LMMSE Decoding
Power-Distortion Optimization
     Relationship between Variables
     MV Transmission Power and Distortion
     MV Distortion and Prediction Noise Variance
      Distortion Formulation
     Solution
Experiments
     PDO Model Verification
      Unicast Performance
     Evaluation of Each Module
     Robustness Test
     Multicast Performance
     Complexity and Bit-Rate
Summary

Denoising in Communication
Introduction
Background
     Image Denoising
     Video Compression
System Design
     System Overview 
     Sender Design
     Receiver Design
Implementation
     Cactus Implementation 
     GPU Implementation of BM3D
Evaluation
     Settings
     Micro-Benchmarks
     Comparison against Reference Systems
     Transmitting High-Definition Videos
     Robustness to Packet Loss
Related Work
Summary

MIMO Broadcasting with Receiver Antenna Heterogeneity

Introduction
Background and Related Work
     Multi-Antenna Systems
     Layered Source-Channel Schemes
     Compressive Sensing
     SoftCast
Compressive Image Broadcasting System
     The Encoder and Decoder
     Addressing Heterogeneity
Power Allocation
     Power Scaling Factors
     Aggregating Coefficients
Compressive Sampling
Amplitude Modulation and Transmission
The CS Decoder
Simulation Evaluation
     Micro-Benchmarks for Our System
     Performance Comparison with Other Broadcast Systems
Summary

FUTURE WORK

Computational Information Theory
Introduction
Cloud Sources
Source Coding
     Coding of Metadata
     Coding of Cloud Image Sources
     Coding of Cloud Video Sources
     Distributed Coding Using Cloud Sources
Channel Coding
     Power Allocation and Bandwidth Matching
     Multiple Level Channel Coding
     Channel Denoising
Joint Source and Channel Coding
Summary

Appendix:

Published Journal and Conference Papers Related to This Book
Scalable Video Coding
Directional Transforms
Vision-Based Compression
Compressive Communication
Pseudo-Analog Transmission

References
Index

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