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.
Researchers and engineers involved with visual data compression and communication.
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