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E-raamat: Digital Signal Compression: Principles and Practice

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(Hewlett-Packard Laboratories, Palo Alto, California), (Rensselaer Polytechnic Institute, New York)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 27-Oct-2011
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781139118675
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Digital Signal Compression: Principles and PracticeSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 27-Oct-2011
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781139118675
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"With clear and easy-to-understand explanations, this book covers the fundamental concepts and coding methods of signal compression, whilst still retaining technical depth and rigor. It contains a wealth of illustrations, step-by-step descriptions of algorithms, examples and practice problems, which make it an ideal textbook for senior undergraduate and graduate students, as well as a useful self-study tool for researchers and professionals. Principles of lossless compression are covered, as are various entropy coding techniques, including Huffman coding, arithmetic coding and Lempel-Ziv coding. Scalar and vector quantization and trellis coding are thoroughly explained, and a full chapter is devoted to mathematical transformations including the KLT, DCT and wavelet transforms. The workings of transform and subband/wavelet coding systems, including JPEG2000 and SBHP image compression and H.264/AVC video compression, are explained and a unique chapter is provided on set partition coding, shedding new lighton SPIHT, SPECK, EZW and related methods"--

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Provides clear and easily understandable coverage of the fundamental concepts and coding methods, whilst retaining technical depth and rigor.
Preface xv
Acknowledgments xix
1 Motivation
1(9)
1.1 The importance of compression
1(1)
1.2 Data types
2(2)
1.2.1 Symbolic information
2(1)
1.2.2 Numerical information
3(1)
1.3 Basic compression process
4(1)
1.4 Compression applications
5(1)
1.5 Design of compression methods
6(2)
1.6 Multi-disciplinary aspect
8(2)
Note
8(1)
References
9(1)
2 Book overview
10(13)
2.1 Entropy and lossless coding
10(1)
2.2 Quantization
11(1)
2.3 Source transformations
12(4)
2.3.1 Prediction
12(1)
2.3.2 Transforms
13(3)
2.4 Set partition coding
16(1)
2.5 Coding systems
17(3)
2.5.1 Performance criteria
17(1)
2.5.2 Transform coding systems
18(1)
2.5.3 Subband coding systems
19(1)
2.6 Distributed source coding
20(3)
Notes
21(1)
References
22(1)
3 Principles of lossless compression
23(18)
3.1 Introduction
23(1)
3.2 Lossless source coding and entropy
23(5)
3.3 Variable length codes
28(4)
3.3.1 Unique decodability and prefix-free codes
28(1)
3.3.2 Construction of prefix-free codes
28(2)
3.3.3 Kraft inequality
30(2)
3.4 Opfimalily of prefix-free codes
32(5)
3.4.1 Sources with memory
36(1)
3.5 Concluding remarks
37(4)
Problems
37(3)
References
40(1)
4 Entropy coding techniques
41(36)
4.1 Introduction
41(1)
4.2 Huffman codes
41(6)
4.3 Shannon-Fano-Elias codes
47(3)
4.3.1 SFE code examples
48(1)
4.3.2 Decoding the SFE code
49(1)
4.4 Arithmetic code
50(5)
4.4.1 Preliminaries
50(1)
4.4.2 Arithmetic encoding
51(2)
4.4.3 Arithmetic decoding
53(2)
4.5 Run-length codes
55(2)
4.6 Alphabet partitioning: modified Huffman codes
57(3)
4.6.1 Modified Huffman codes
57(1)
4.6.2 Alphabet partitioning
58(2)
4.7 Golomb code
60(3)
4.8 Dictionary coding
63(9)
4.8.1 The LZ78 code
64(1)
4.8.2 The LZW algorithm
65(2)
4.8.3 The LZ77 coding method
67(5)
4.9 Summary remarks
72(5)
Problems
72(3)
Notes
75(1)
References
76(1)
5 Lossy compression of scalar sources
77(39)
5.1 Introduction
77(1)
5.2 Quantization
77(10)
5.2.1 Scalar quantization
77(4)
5.2.2 Uniform quantization
81(6)
5.3 Non-uniform quantization
87(4)
5.3.1 High rate approximations
89(2)
5.4 Companding
91(4)
5.4.1 Distortion at high rates
93(2)
5.5 Entropy coding of quantizer outputs
95(6)
5.5.1 Entropy coded quantizer characteristics
98(1)
5.5.2 Null-zone quantization
99(2)
5.6 Bounds on optimal performance
101(6)
5.6.1 Rate-distortion theory
102(2)
5.6.2 The Gish-Pierce bound
104(3)
5.7 Concluding remarks
107(1)
5.8 Appendix: quantization tables
107(9)
Problems
109(4)
Note
113(1)
References
114(2)
6 Coding of sources with memory
116(50)
6.1 Introduction
116(1)
6.2 Predictive coding
116(6)
6.2.1 Optimal linear prediction
117(3)
6.2.2 DPCM system description
120(1)
6.2.3 DPCM coding error and gain
121(1)
6.3 Vector coding
122(19)
6.3.1 Optimal performance bounds
122(7)
6.3.2 Vector (block) quantization (VQ)
129(6)
6.3.3 Entropy constrained vector quantization
135(6)
6.4 Tree-structured vector quantization
141(5)
6.4.1 Variable length TSVQ coding
144(1)
6.4.2 Pruned TSVQ
145(1)
6.5 Tree and trellis codes
146(6)
6.5.1 Trellis codes
148(2)
6.5.2 Encoding and decoding of trellis codes
150(2)
6.5.3 Codevector alphabets
152(1)
6.6 Trellis coded quantization (TCQ)
152(3)
6.6.1 Entropy-coded TCQ
154(1)
6.6.2 Improving low-rate performance in TCQ
155(1)
6.7 Search algorithms
155(5)
6.7.1 M-algorithm
155(3)
6.7.2 The Viterbi algorithm
158(2)
6.8 Concluding remarks
160(6)
Problems
160(3)
Notes
163(1)
References
164(2)
7 Mathematical transformations
166(52)
7.1 Introduction
166(5)
7.1.1 Transform coding gain
169(2)
7.2 The optimal Karhunen-Loeve transform
171(1)
7.2.1 Optimal transform coding gain
172(1)
7.3 Suboptimal transforms
172(3)
7.3.1 The discrete Fourier transform
172(1)
7.3.2 The discrete cosine transform
173(1)
7.3.3 The Hadamard-Walsh transform
174(1)
7.4 Lapped orthogonal transform
175(4)
7.4.1 Example of calculation of transform coding gain
178(1)
7.5 Transforms via filter banks
179(2)
7.6 Two-dimensional transforms for images
181(3)
7.7 Subband transforms
184(27)
7.7.1 Introduction
184(3)
7.7.2 Coding gain of subband transformation
187(5)
7.7.3 Realizable perfect reconstruction filters
192(2)
7.7.4 Orthogonal wavelet transform
194(5)
7.7.5 Biorthogonal wavelet transform
199(5)
7.7.6 Useful biorthogonal filters
204(1)
7.7.7 The lifting scheme
205(3)
7.7.8 Transforms with integer output
208(3)
7.8 Concluding remarks
211(7)
Problems
212(2)
Notes
214(2)
References
216(2)
8 Rate control in transform coding systems
218(27)
8.1 Rate allocation
218(15)
8.1.1 Optimal rate allocation for known quantizer characteristics
220(3)
8.1.2 Realizing the optimal rate allocation
223(2)
8.1.3 Fixed level quantization
225(1)
8.1.4 Optimal bit allocation for arbitrary set of quantizers
226(2)
8.1.5 Building up to optimal rates for arbitrary quantizers
228(2)
8.1.6 Transform coding gain
230(3)
8.2 Subband rate allocation
233(8)
8.2.1 Practical issues
237(2)
8.2.2 Subband coding gain
239(2)
8.3 Algorithms for rate allocation to subbands
241(1)
8.4 Conclusions
242(3)
Problems
242(1)
Notes
243(1)
References
244(1)
9 Transform coding systems
245(20)
9.1 Introduction
245(1)
9.2 Application of source transformations
245(6)
9.2.1 Model-based image transform coding
246(3)
9.2.2 Encoding transform coefficients
249(2)
9.3 The JPEG standard
251(8)
9.3.1 The JPEG baseline system
252(4)
9.3.2 Detailed example of JPEG standard method
256(3)
9.4 Advanced image transform coding: H.264/AVC intra coding
259(3)
9.5 Concluding remarks
262(3)
Problems
262(1)
Notes
263(1)
References
264(1)
10 Set partition coding
265(48)
10.1 Principles
265(11)
10.1.1 Partitioning data according to value
267(3)
10.1.2 Forming partitions recursively: square blocks
270(4)
10.1.3 Binary splitting
274(2)
10.1.4 One-dimensional signals
276(1)
10.2 Tree-structured sets
276(9)
10.2.1 A different wavelet transform partition
279(3)
10.2.2 Data-dependent thresholds
282(1)
10.2.3 Adaptive partitions
283(2)
10.3 Progressive transmission and bitplane coding
285(1)
10.4 Applications to image transform coding
286(20)
10.4.1 Block partition coding and amplitude and group partitioning (AGP)
287(2)
10.4.2 Enhancements via entropy coding
289(1)
10.4.3 Traversing the blocks
289(2)
10.4.4 Embedded block coding of image wavelet transforms
291(1)
10.4.5 A SPECK coding example
291(6)
10.4.6 Embedded tree-based image wavelet transform coding
297(2)
10.4.7 A SPIHT coding example
299(3)
10.4.8 Embedded zerotree wavelet (EZW) coding
302(4)
10.4.9 Group testing for image wavelet coding
306(1)
10.5 Conclusion
306(7)
Problems
307(3)
Notes
310(1)
References
311(2)
11 Subband/wavelet coding systems
313(48)
11.1 Wavelet transform coding systems
313(4)
11.2 Generic wavelet-based coding systems
317(1)
11.3 Compression methods in wavelet-based systems
318(2)
11.4 Block-based wavelet transform set partition coding
320(27)
11.4.1 Progressive resolution coding
321(2)
11.4.2 Quality-progressive coding
323(3)
11.4.3 Octave band partitioning
326(2)
11.4.4 Direct bit-embedded coding methods
328(1)
11.4.5 Lossless coding of quantizer levels with adaptive thresholds
329(2)
11.4.6 Tree-block coding
331(1)
11.4.7 Coding of subband subblocks
332(1)
11.4.8 Coding the initial thresholds
333(2)
11.4.9 The SBHP method
335(1)
11.4.10 JPEG2000 coding
336(7)
11.4.11 The embedded zero-block coder (EZBC)
343(4)
11.5 Tree-based wavelet transform coding systems
347(7)
11.5.1 Fully scalable SPIHT
347(2)
11.5.2 Resolution scalable SPIHT
349(3)
11.5.3 Block-oriented SPIHT coding
352(2)
11.6 Rate control for embedded block coders
354(2)
11.7 Conclusion
356(5)
Notes
357(2)
References
359(2)
12 Methods for lossless compression of images
361(12)
12.1 Introduction
361(1)
12.2 Lossless predictive coding
362(2)
12.2.1 Old JPEG standard for lossless image compression
362(2)
12.3 State-of-the-art lossless image coding and JPEG-LS
364(4)
12.3.1 The predictor
364(1)
12.3.2 The context
365(1)
12.3.3 Golomb-Rice coding
366(1)
12.3.4 Bias cancellation
366(1)
12.3.5 Run mode
367(1)
12.3.6 Near-lossless mode
368(1)
12.3.7 Remarks
368(1)
12.4 Multi-resolution methods
368(1)
12.5 Concluding remarks
369(4)
Problems
370(1)
Notes
371(1)
References
372(1)
13 Color and multi-component image and video coding
373(25)
13.1 Introduction
373(1)
13.2 Color image representation
374(4)
13.2.1 Chrominance subsampling
376(1)
13.2.2 Principal component space
377(1)
13.3 Color image coding
378(5)
13.3.1 Transform coding and JPEG
378(2)
13.3.2 Wavelet transform systems
380(3)
13.4 Multi-component image coding
383(12)
13.4.1 JPEG2000
383(1)
13.4.2 Three-dimensional wavelet transform coding
384(5)
13.4.3 Video coding
389(6)
13.5 Concluding remarks
395(3)
Notes
395(1)
References
396(2)
14 Distributed source coding
398(16)
14.1 Slepian-Wolf coding for lossless compression
398(6)
14.1.1 Practical Slepian-Wolf coding
400(4)
14.2 Wyner-Ziv coding for lossy compression
404(7)
14.2.1 Scalar Wyner-Ziv coding
406(1)
14.2.2 Probability of successful reconstruction
407(4)
14.3 Concluding remarks
411(3)
Problems
411(1)
Notes
412(1)
References
413(1)
Index 414
William A. Pearlman is a Professor in the Electrical, Computer and Systems Engineering Department at the Rensselaer Polytechnic Institute (RPI), where he has been a faculty member since 1979. He has more than 35 years of experience in teaching and researching in the fields of information theory, data compression, digital signal processing and digital communications theory. He is a Fellow of the IEEE and the SPIE, and is the co-inventor of two celebrated image compression algorithms: SPIHT and SPECK. Amir Said is currently a Master Researcher at Hewlett-Packard Laboratories, where he has worked since 1998. His research interests include multimedia communications, coding and information theory, image and video compression, signal processing and optimization, and he has more than 50 publications in these fields. He is co-inventor with Dr Pearlman of the SPIHT image compression algorithm and co-recipient, also with Dr Pearlman, of two Best Paper Awards, one from the IEEE Circuits and Systems Society and the other from the IEEE Signal Processing Society.
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