Advances in Visual Data Compression and Communication: Meeting the Requirements of New Applications, 1st Edition (Hardback) book cover

Advances in Visual Data Compression and Communication

Meeting the Requirements of New Applications, 1st Edition

By Feng Wu

Auerbach Publications

513 pages | 198 B/W Illus.

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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

About the Series

Multimedia Computing, Communication and Intelligence

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
COM032000
COMPUTERS / Information Technology
TEC007000
TECHNOLOGY & ENGINEERING / Electrical
TEC015000
TECHNOLOGY & ENGINEERING / Imaging Systems
TEC061000
TECHNOLOGY & ENGINEERING / Mobile & Wireless Communications