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.
Author(s): Feng Wu
Series: Multimedia Computing, Communication and Intelligence
Publisher: CRC Press/Taylor & Francis
Year: 2015
Language: English
Pages: xxxii+481
City: Boca Raton, FL
Tags: Информатика и вычислительная техника;Обработка медиа-данных;Обработка изображений;
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
