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Advances in Visual Data Compression and Communication: Meeting the Requirements of New Applications [Kõva köide]

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  • Formaat: Hardback, 513 pages, kõrgus x laius: 234x156 mm, kaal: 839 g, 33 Tables, black and white; 198 Illustrations, black and white
  • Sari: Multimedia Computing, Communication and Intelligence
  • Ilmumisaeg: 25-Jul-2014
  • Kirjastus: Apple Academic Press Inc.
  • ISBN-10: 1482234130
  • ISBN-13: 9781482234138
Teised raamatud teemal:
Advances in Visual Data Compression and Communication: Meeting the Requirements of New ApplicationsSuurem pilt
  • Formaat: Hardback, 513 pages, kõrgus x laius: 234x156 mm, kaal: 839 g, 33 Tables, black and white; 198 Illustrations, black and white
  • Sari: Multimedia Computing, Communication and Intelligence
  • Ilmumisaeg: 25-Jul-2014
  • Kirjastus: Apple Academic Press Inc.
  • ISBN-10: 1482234130
  • ISBN-13: 9781482234138
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781482234138.html
Märksõnad:
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 authors 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 authors 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 synthesisfrom 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.
Preface xvii
Acknowledgments xxv
Acronyms xxix
Part I Basis for Compression and Communication
1 Information Theory
3(18)
1.1 Introduction
3(4)
1.2 Source Coding
7(7)
1.2.1 Huffman Coding
8(1)
1.2.2 Arithmetic Coding
8(3)
1.2.3 Rate Distortion Theory
11(3)
1.3 Channel Coding
14(5)
1.3.1 Capacity
14(2)
1.3.2 Coding Theorem
16(1)
1.3.3 Hamming Codes
17(2)
1.4 Joint Source and Channel Coding
19(2)
2 Hybrid Video Coding
21(26)
2.1 Hybrid Coding Framework
21(5)
2.2 Technical Evolution
26(8)
2.2.1 H.261
26(1)
2.2.2 MPEG-1
26(1)
2.2.3 MPEG-2
27(1)
2.2.4 MPEG-4
28(1)
2.2.5 H.264/MPEG-4 AVC
28(2)
2.2.6 HEVC
30(1)
2.2.7 Performance versus Encoding Complexity
31(3)
2.3 H.264 Standard
34(6)
2.3.1 Motion Compensation
34(2)
2.3.2 Intra Prediction
36(1)
2.3.3 Transform and Quantization
36(1)
2.3.4 Entropy Coding
37(1)
2.3.5 Deblocking Filtering
38(1)
2.3.6 Rate Distortion Optimization
39(1)
2.4 HEVC Standard
40(7)
2.4.1 Motion Compensation
40(2)
2.4.2 Intra Prediction
42(1)
2.4.3 Transform and Quantization
43(2)
2.4.4 Sample Adaptive Offset Filter
45(2)
3 Communication
47(18)
3.1 Analog Communication
47(4)
3.1.1 Analog Modulation
48(1)
3.1.2 Multiplexing
49(2)
3.2 Digital Communication
51(14)
3.2.1 Low-Density Parity-Check (LDPC) Codes
51(4)
3.2.2 Turbo Codes
55(5)
3.2.3 Digital Modulation
60(5)
Part II Scalable Video Coding
4 Progressive Fine Granularity Scalable (PFGS) Coding
65(26)
4.1 Introduction
65(1)
4.2 Fine Granularity Scalable Video Coding
66(3)
4.3 Basic PFGS Framework
69(4)
4.3.1 Basic Ideas to Build the PFGS Framework
70(2)
4.3.2 The Simplified PFGS Framework
72(1)
4.4 Improvements to the PFGS Framework
73(6)
4.4.1 Potential Coding Inefficiency Due to Two References
73(3)
4.4.2 A More Efficient PFGS Framework
76(3)
4.5 Implementation of the PFGS Encoder and Decoder
79(3)
4.6 Experimental Results and Analyses
82(3)
4.7 Simulation of Streaming PFGS Video over Wireless Channels
85(5)
4.8 Summary
90(1)
5 Motion Threading for 3D Wavelet Coding
91(20)
5.1 Introduction
91(1)
5.2 Motion Threading
92(2)
5.3 Advanced Motion Threading
94(4)
5.3.1 Lifting-Based Motion Threading
94(3)
5.3.2 Many-to-One Mapping and Non-Referred Pixels
97(1)
5.4 Multi-Layer Motion Threading
98(3)
5.5 Correlated Motion Estimation with R-D Optimization
101(4)
5.5.1 Definition of the Mode Types
102(2)
5.5.2 R-D Optimized Mode Decision
104(1)
5.6 Experimental Results
105(4)
5.6.1 Coding Performance Comparison
105(1)
5.6.2 Macroblock Mode Distribution
106(3)
5.7 Summary
109(2)
6 Barbell-Lifting Based 3D Wavelet Coding
111(24)
6.1 Introduction
111(1)
6.2 Barbell-Lifting Coding Scheme
112(8)
6.2.1 Barbell Lifting
113(4)
6.2.2 Layered Motion Coding
117(1)
6.2.3 Entropy Coding in Brief
118(1)
6.2.4 Base Layer Embedding
119(1)
6.3 Comparisons with SVC
120(3)
6.3.1 Coding Framework
120(1)
6.3.2 Temporal Decorrelation
121(1)
6.3.3 Spatial Scalability
122(1)
6.3.4 Intra Prediction
123(1)
6.4 Advances in 3D Wavelet Video Coding
123(4)
6.4.1 In-Scale MCTF
123(3)
6.4.2 Subband Adaptive MCTF
126(1)
6.5 Experimental Results
127(5)
6.5.1 Comparison with Motion Compensated Embedded Zero Block Coding (MC-EZBC)
127(2)
6.5.2 Comparison with Scalable Video Coding (SVC) for Signal-to-Noise Ratio (SNR) Scalability
129(1)
6.5.3 Comparison with SVC for Combined Scalability
130(2)
6.6 Summary
132(3)
Part III Directional Transforms
7 Directional Wavelet Transform
135(18)
7.1 Introduction
135(3)
7.2 2D Wavelet Transform via Adaptive Directional Lifting
138(6)
7.2.1 ADL Structure
138(5)
7.2.2 Subpixel Interpolation
143(1)
7.3 R-D Optimized Segmentation for ADL
144(1)
7.4 Experimental Results and Observations
145(7)
7.5 Summary
152(1)
8 Directional DCT Transform
153(20)
8.1 Introduction
153(1)
8.2 Lifting-Based Directional DCT-Like Transform
154(7)
8.2.1 Lifting Structure of Discrete Cosine Transform (DCT)
154(3)
8.2.2 Directional DCT-Like Transform
157(2)
8.2.3 Comparison with Rotated DCT
159(2)
8.3 Image Coding with Proposed Directional Transform
161(5)
8.3.1 Direction Transition on Block Boundary
162(2)
8.3.2 Direction Selection
164(2)
8.4 Experimental Results
166(5)
8.5 Summary
171(2)
9 Directional Filtering Transform
173(22)
9.1 Introduction
173(2)
9.2 Adaptive Directional Lifting-Based 2D Wavelet Transform
175(1)
9.3 Mathematical Analysis
176(9)
9.3.1 Coding Gain of ADL
177(4)
9.3.2 Numerical Analysis
181(4)
9.4 Directional Filtering Transform
185(4)
9.4.1 Proposed Intra-Coding Scheme
185(1)
9.4.2 Directional Filtering
186(2)
9.4.3 Optional Transform
188(1)
9.5 Experimental Results
189(3)
9.6 Summary
192(3)
Part IV Vision-Based Compression
10 Edge-Based Inpainting
195(26)
10.1 Introduction
195(2)
10.2 The Proposed Framework
197(4)
10.3 Edge Extraction and Exemplar Selection
201(5)
10.3.1 Edge Extraction
202(2)
10.3.2 Exemplar Selection
204(2)
10.4 Edge-Based Image Inpainting
206(5)
10.4.1 Structure Propagation
207(3)
10.4.2 Texture Synthesis
210(1)
10.5 Experimental Results
211(8)
10.5.1 Implementation
211(1)
10.5.2 Test Results
212(3)
10.5.3 Discussions
215(4)
10.6 Summary
219(2)
11 Cloud-Based Image Compression
221(24)
11.1 Introduction
221(1)
11.2 Related Work
222(3)
11.2.1 Visual Content Generation
222(1)
11.2.2 Local Feature Compression
223(1)
11.2.3 Image Reconstruction
224(1)
11.3 The Proposed SIFT-Based Image Coding
225(1)
11.4 Extraction of Image Description
226(3)
11.5 Compression of Image Descriptors
229(3)
11.5.1 Prediction Evaluation
229(1)
11.5.2 Compression of SIFT Descriptors
230(2)
11.6 Image Reconstruction
232(3)
11.6.1 Patch Retrieval
232(1)
11.6.2 Patch Transformation
233(1)
11.6.3 Patch Stitching
234(1)
11.7 Experimental Results and Analyses
235(6)
11.7.1 Compression Ratio
235(1)
11.7.2 Visual Quality
236(1)
11.7.3 Highly Correlated Image
237(3)
11.7.4 Complexity Analyses
240(1)
11.7.5 Comparison with SIFT Feature Vector Coding
240(1)
11.8 Further Discussion
241(2)
11.8.1 Typical Applications
241(1)
11.8.2 Limitations
242(1)
11.8.3 Future Work
242(1)
11.9 Summary
243(2)
12 Compression for Cloud Photo Storage
245(24)
12.1 Introduction
245(2)
12.2 Related Work
247(2)
12.2.1 Image Set Compression
247(1)
12.2.2 Local Feature Descriptors
248(1)
12.3 Proposed Scheme
249(1)
12.4 Feature-Based Prediction Structure
250(4)
12.4.1 Graph Building
250(2)
12.4.2 Feature-Based Minimum Spanning Tree
252(1)
12.4.3 Prediction Structure
253(1)
12.5 Feature-Based Inter-Image Prediction
254(4)
12.5.1 Feature-Based Geometric Deformations
254(3)
12.5.2 Feature-Based Photometric Transformation
257(1)
12.5.3 Block-Based Motion Compensation
258(1)
12.6 Experimental Results
258(6)
12.6.1 Efficiency of Multi-Model Prediction
260(1)
12.6.2 Efficiency of Photometric Transformation
261(1)
12.6.3 Overall Performance
262(1)
12.6.4 Complexity
263(1)
12.7 Our Conjecture on Cloud Storage
264(1)
12.8 Summary
265(4)
Part V Compressive Communication
13 Compressive Data Gathering
269(26)
13.1 Introduction
269(2)
13.2 Related Work
271(3)
13.2.1 Conventional Compression
271(1)
13.2.2 Distributed Source Coding
272(1)
13.2.3 Compressive Sensing
273(1)
13.3 Compressive Data Gathering
274(5)
13.3.1 Data Gathering
274(2)
13.3.2 Data Recovery
276(3)
13.4 Network Capacity of Compressive Data Gathering
279(9)
13.4.1 Network Capacity Analysis
279(5)
13.4.2 NS-2 Simulation
284(4)
13.5 Experiments on Real Data Sets
288(4)
13.5.1 CTD Data from the Ocean
288(1)
13.5.2 Temperature in the Data Center
289(3)
13.6 Summary
292(3)
14 Compressive Modulation
295(22)
14.1 Introduction
295(1)
14.2 Background
296(4)
14.2.1 Rate Adaptation
296(2)
14.2.2 Mismatched Decoding Problem
298(2)
14.3 Compressive Modulation
300(7)
14.3.1 Coding and Modulation
300(2)
14.3.2 Soft Demodulation and Decoding
302(3)
14.3.3 Design RP Codes
305(2)
14.4 Simulation Study
307(2)
14.4.1 Rate Adaptation Performance
307(2)
14.4.2 Sensitivity to SNR Estimation
309(1)
14.5 Testbed Evaluation
309(4)
14.5.1 Comparison to Oracle
311(1)
14.5.2 Comparison to ADM
312(1)
14.6 Related Work
313(2)
14.6.1 Coded Modulation
313(1)
14.6.2 Compressive Sensing
314(1)
14.7 Summary
315(2)
15 Joint Source and Channel Coding
317(24)
15.1 Introduction
317(2)
15.2 Related Work and Background
319(3)
15.2.1 Joint Source-Channel Coding
319(1)
15.2.2 Coded Modulation
320(1)
15.2.3 Rate Adaptation
320(1)
15.2.4 Compressive Sensing
321(1)
15.3 Compressive Modulation (CM) for Sparse Binary Sources
322(7)
15.3.1 Design Principles
323(2)
15.3.2 Weight Selection
325(2)
15.3.3 Encoding Matrix Construction
327(2)
15.4 Belief Propagation Decoding
329(3)
15.5 Performance Evaluation
332(5)
15.5.1 Implementation
333(1)
15.5.2 Simulations over an AWGN Channel
334(1)
15.5.3 Emulation in Real Channel Environment
335(2)
15.6 Summary
337(4)
Part VI Pseudo-Analog Transmission
16 DCast: Distributed Video Multicast
341(28)
16.1 Introduction
341(1)
16.2 Related Works
342(3)
16.2.1 Distributed Video Coding
342(1)
16.2.2 Distributed Video Transmission
343(1)
16.2.3 SoftCast
344(1)
16.3 Proposed DCast
345(7)
16.3.1 Coset Coding
346(1)
16.3.2 Coset Quantization
347(1)
16.3.3 Power Allocation
348(2)
16.3.4 Packaging and Transmission
350(1)
16.3.5 LMMSE Decoding
351(1)
16.4 Power-Distortion Optimization
352(5)
16.4.1 Relationship between Variables
353(1)
16.4.2 MV Transmission Power and Distortion
353(1)
16.4.3 MV Distortion and Prediction Noise Variance
354(1)
16.4.4 Distortion Formulation
355(1)
16.4.5 Solution
356(1)
16.5 Experiments
357(10)
16.5.1 PDO Model Verification
358(2)
16.5.2 Unicast Performance
360(1)
16.5.3 Evaluation of Each Module
361(1)
16.5.4 Robustness Test
362(1)
16.5.5 Multicast Performance
363(2)
16.5.6 Complexity and Bit Rate
365(2)
16.6 Summary
367(2)
17 Denoising in Communications
369(26)
17.1 Introduction
369(1)
17.2 Background
370(3)
17.2.1 Image Denoising
370(1)
17.2.2 Video Compression
371(2)
17.3 System Design
373(6)
17.3.1 System Overview
373(2)
17.3.2 Sender Design
375(3)
17.3.3 Receiver Design
378(1)
17.4 Implementation
379(3)
17.4.1 Cactus Implementation
379(1)
17.4.2 GPU Implementation of BM3D
380(2)
17.5 Evaluation
382(10)
17.5.1 Settings
382(1)
17.5.2 Micro-Benchmarks
383(5)
17.5.3 Comparison against Reference Systems
388(2)
17.5.4 Transmitting High-Definition Videos
390(1)
17.5.5 Robustness to Packet Loss
391(1)
17.6 Related Work
392(1)
17.7 Summary
393(2)
18 MIMO Broadcasting with Receiver Antenna Heterogeneity
395(26)
18.1 Introduction
395(2)
18.2 Background and Related Work
397(3)
18.2.1 Multi-Antenna Systems
397(1)
18.2.2 Layered Source-Channel Schemes
398(1)
18.2.3 Compressive Sensing
399(1)
18.2.4 SoftCast
400(1)
18.3 Compressive Image Broadcasting System
400(2)
18.3.1 The Encoder and Decoder
401(1)
18.3.2 Addressing Heterogeneity
402(1)
18.4 Power Allocation
402(3)
18.4.1 Power Scaling Factors
403(1)
18.4.2 Aggregating Coefficients
404(1)
18.5 Compressive Sampling
405(1)
18.6 Amplitude Modulation and Transmission
406(1)
18.7 The CS Decoder
407(2)
18.8 Simulation Evaluation
409(9)
18.8.1 Micro-Benchmarks for Our System
409(3)
18.8.2 Performance Comparison with Other Broadcast Systems
412(6)
18.9 Summary
418(3)
Part VII Future Work
19 Computational Information Theory
421(18)
19.1 Introduction
421(1)
19.2 Cloud Sources
422(4)
19.3 Source Coding
426(6)
19.3.1 Coding of Metadata
426(1)
19.3.2 Coding of Cloud Image Sources
427(2)
19.3.3 Coding of Cloud Video Sources
429(1)
19.3.4 Distributed Coding Using Cloud Sources
430(2)
19.4 Channel Coding
432(4)
19.4.1 Power Allocation and Bandwidth Matching
432(2)
19.4.2 Multiple Level Channel Coding
434(1)
19.4.3 Channel Denoising
435(1)
19.5 Joint Source and Channel Coding
436(1)
19.6 Summary
437(2)
A Our Published Journal and Conference Papers Related to This Book 439(26)
A.1 Scalable Video Coding
439(1)
A.2 Directional Transforms
439(1)
A.3 Vision-Based Compression
440(1)
A.4 Compressive Communication
440(1)
A.5 Pseudo-Analog Transmission
441(2)
References
443(22)
Index 465
Researchers and engineers involved with visual data compression and communication.