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E-raamat: Introduction to Data Compression

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(Department of Electrical and Computer Engineering, University of Nebraska, Lincoln, Nebraska, USA)
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Each edition of Introduction to Data Compression has widely been considered the best introduction and reference text on the art and science of data compression, and the fourth edition continues in this tradition. Data compression techniques and technology are ever-evolving with new applications in image, speech, text, audio, and video. The fourth edition includes all the cutting edge updates the reader will need during the work day and in class.

Khalid Sayood provides an extensive introduction to the theory underlying today’s compression techniques with detailed instruction for their applications using several examples to explain the concepts. Encompassing the entire field of data compression, Introduction to Data Compression includes lossless and lossy compression, Huffman coding, arithmetic coding, dictionary techniques, context based compression, scalar and vector quantization. Khalid Sayood provides a working knowledge of data compression, giving the reader the tools to develop a complete and concise compression package upon completion of his book.
  • New content added to include a more detailed description of the JPEG 2000 standard

  • New content includes speech coding for internet applications

  • Explains established and emerging standards in depth including JPEG 2000, JPEG-LS, MPEG-2, H.264, JBIG 2, ADPCM, LPC, CELP, MELP, and iLBC

  • Source code provided via companion web site that gives readers the opportunity to build their own algorithms, choose and implement techniques in their own applications


Arvustused

"This text is a truly introductory treatment of the entire field of data compression, including lossless coding, speech coding, and audio coding, which are often neglected in other data compression books. Sayoods book has the very best tutorial treatment of lossless source coding anywhere, with detailed coverage of Lempel-Ziv, arithmetic, Golumb, and Tunstall coding, in addition to treatments of fixed and adaptive Huffman coding and context-based methods. Additionally, the book contains material on M-band quadrature mirror filter banks, the polyphase decomposition, and wavelets beyond what is normally found in any introductory text. I have used Sayoods book for a reference and as a text for a course on signal compression. I highly recommend it for adoption." --Jerry D. Gibson, Professor of Electrical and Computer Engineering, University of California, Santa Barbara"Khalid Sayood's book has long been the standard academic reference for those interested in Data Compression. I am very pleased to see his ongoing effort to keep the content timely with the release of the fourth edition this fall. If you want to be well versed in state of the art, ranging from simple lossless coding up to complex video compression, this is the only book I know that will stay with you on every step of the journey." --Mark Nelson, Engineer at Cisco Systems, Inc and Senior Member of IEEE

Muu info

A concise and comprehensive guide to Data Compression.
Preface xvii
1 Introduction
1(12)
1.1 Compression Techniques
3(3)
1.1.1 Lossless Compression
4(1)
1.1.2 Lossy Compression
5(1)
1.1.3 Measures of Performance
5(1)
1.2 Modeling and Coding
6(4)
1.3 Summary
10(1)
1.4 Projects and Problems
10(3)
2 Mathematical Preliminaries for Lossless Compression
13(30)
2.1 Overview
13(1)
2.2 A Brief Introduction to Information Theory
13(12)
2.2.1 Derivation of Average Information
20(5)
2.3 Models
25(4)
2.3.1 Physical Models
25(1)
2.3.2 Probability Models
25(1)
2.3.3 Markov Models
26(3)
2.3.4 Composite Source Model
29(1)
2.4 Coding
29(8)
2.4.1 Uniquely Decodable Codes
30(3)
2.4.2 Prefix Codes
33(1)
2.4.3 The Kraft-McMillan Inequality
34(3)
2.5 Algorithmic Information Theory
37(1)
2.6 Minimum Description Length Principle
38(1)
2.7 Summary
39(1)
2.8 Projects and Problems
40(3)
3 Huffman Coding
43(48)
3.1 Overview
43(1)
3.2 The Huffman Coding Algorithm
43(22)
3.2.1 Minimum Variance Huffman Codes
47(3)
3.2.2 Canonical Huffman Codes
50(2)
3.2.3 Length-Limited Huffman Codes
52(3)
3.2.4 Optimality of Huffman Codes
55(1)
3.2.5 Length of Huffman Codes
56(2)
3.2.6 Extended Huffman Codes
58(3)
3.2.7 Implementation of Huffman Codes
61(4)
3.3 Nonbinary Huffman Codes
65(2)
3.4 Adaptive Huffman Coding
67(8)
3.4.1 Update Procedure
68(3)
3.4.2 Encoding Procedure
71(2)
3.4.3 Decoding Procedure
73(2)
3.5 Golomb Codes
75(1)
3.6 Rice Codes
76(3)
3.6.1 CCSDS Recommendation for Lossless Compression
77(2)
3.7 Tunstall Codes
79(2)
3.8 Applications of Huffman Coding
81(5)
3.8.1 Lossless Image Compression
81(2)
3.8.2 Text Compression
83(2)
3.8.3 Audio Compression
85(1)
3.9 Summary
86(1)
3.10 Projects and Problems
87(4)
4 Arithmetic Coding
91(44)
4.1 Overview
91(1)
4.2 Introduction
91(2)
4.3 Coding a Sequence
93(9)
4.3.1 Generating a Tag
94(7)
4.3.2 Deciphering the Tag
101(1)
4.4 Generating a Binary Code
102(17)
4.4.1 Uniqueness and Efficiency of the Arithmetic Code
103(3)
4.4.2 Algorithm Implementation
106(5)
4.4.3 Integer Implementation
111(8)
4.5 Adaptive Arithmetic Coding
119(1)
4.6 Binary Arithmetic Coding
120(7)
4.6.1 The QM Coder
125(1)
4.6.2 The MQ Coder
125(1)
4.6.3 The M Coder
126(1)
4.7 Comparison of Huffman and Arithmetic Coding
127(3)
4.8 Applications
130(1)
4.9 Summary
131(1)
4.10 Projects and Problems
131(4)
5 Dictionary Techniques
135(28)
5.1 Overview
135(1)
5.2 Introduction
135(1)
5.3 Static Dictionary
136(3)
5.3.1 Digram Coding
137(2)
5.4 Adaptive Dictionary
139(11)
5.4.1 The LZ77 Approach
139(4)
5.4.2 The LZ78 Approach
143(7)
5.5 Applications
150(6)
5.5.1 File Compression-UNIX compress
151(1)
5.5.2 Image Compression-The Graphics Interchange Format (GIF)
151(1)
5.5.3 Image Compression-Portable Network Graphics (PNG)
152(1)
5.5.4 Compression over Modems-V.42 bis
153(3)
5.6 Beyond Compression--Lempel-Ziv Complexity
156(2)
5.7 Summary
158(1)
5.8 Projects and Problems
159(4)
6 Context-Based Compression
163(20)
6.1 Overview
163(1)
6.2 Introduction
163(2)
6.3 Prediction with Partial Match (ppm)
165(9)
6.3.1 The Basic Algorithm
165(5)
6.3.2 The Escape Symbol
170(2)
6.3.3 Length of Context
172(1)
6.3.4 The Exclusion Principle
173(1)
6.4 The Burrows-Wheeler Transform
174(4)
6.4.1 Move-to-Front Coding
177(1)
6.5 Associative Coder of Buyanovsky (ACB)
178(1)
6.6 Dynamic Markov Compression
179(3)
6.7 Summary
182(1)
6.8 Projects and Problems
182(1)
7 Lossless Image Compression
183(34)
7.1 Overview
183(1)
7.2 Introduction
183(3)
7.2.1 The Old JPEG Standard
184(2)
7.3 CALIC
186(4)
7.4 JPEG-LS
190(2)
7.5 Prediction Using Conditional Averages
192(1)
7.6 Multiresolution Approaches
193(5)
7.6.1 Progressive Image Transmission
193(5)
7.7 Facsimile Encoding
198(13)
7.7.1 Run-Length Coding
199(1)
7.7.2 CCITT Group 3 and 4-Recommendations T.4 and T.6
200(3)
7.7.3 JBIG
203(6)
7.7.4 JBIG2-T.88
209(2)
7.8 MRC-T.44
211(2)
7.9 Summary
213(1)
7.10 Projects and Problems
214(3)
8 Mathematical Preliminaries for Lossy Coding
217(34)
8.1 Overview
217(1)
8.2 Introduction
217(3)
8.3 Distortion Criteria
220(5)
8.3.1 The Human Visual System
223(1)
8.3.2 Auditory Perception
224(1)
8.4 Information Theory Revisited
225(7)
8.4.1 Conditional Entropy
225(3)
8.4.2 Average Mutual Information
228(1)
8.4.3 Differential Entropy
229(3)
8.5 Rate Distortion Theory
232(8)
8.6 Models
240(8)
8.6.1 Probability Models
240(3)
8.6.2 Linear System Models
243(5)
8.6.3 Physical Models
248(1)
8.7 Summary
248(1)
8.8 Projects and Problems
249(2)
9 Scalar Quantization
251(44)
9.1 Overview
251(1)
9.2 Introduction
251(1)
9.3 The Quantization Problem
252(5)
9.4 Uniform Quantizer
257(11)
9.5 Adaptive Quantization
268(9)
9.5.1 Forward Adaptive Quantization
269(2)
9.5.2 Backward Adaptive Quantization
271(6)
9.6 Nonuniform Quantization
277(10)
9.6.1 pdf-Optimized Quantization
278(4)
9.6.2 Companded Quantization
282(5)
9.7 Entropy-Coded Quantization
287(5)
9.7.1 Entropy Coding of Lloyd-Max Quantizer Outputs
288(1)
9.7.2 Entropy-Constrained Quantization
289(1)
9.7.3 High-Rate Optimum Quantization
289(3)
9.8 Summary
292(1)
9.9 Projects and Problems
293(2)
10 Vector Quantization
295(50)
10.1 Overview
295(1)
10.2 Introduction
295(3)
10.3 Advantages of Vector Quantization over Scalar Quantization
298(6)
10.4 The Linde-Buzo-Gray Algorithm
304(16)
10.4.1 Initializing the LBG Algorithm
309(6)
10.4.2 The Empty Cell Problem
315(1)
10.4.3 Use of LBG for Image Compression
315(5)
10.5 Tree-Structured Vector Quantizers
320(4)
10.5.1 Design of Tree-Structured Vector Quantizers
323(1)
10.5.2 Pruned Tree-Structured Vector Quantizers
324(1)
10.6 Structured Vector Quantizers
324(8)
10.6.1 Pyramid Vector Quantization
326(1)
10.6.2 Polar and Spherical Vector Quantizers
327(1)
10.6.3 Lattice Vector Quantizers
328(4)
10.7 Variations on the Theme
332(5)
10.7.1 Gain-Shape Vector Quantization
332(1)
10.7.2 Mean-Removed Vector Quantization
332(1)
10.7.3 Classified Vector Quantization
333(1)
10.7.4 Multistage Vector Quantization
334(1)
10.7.5 Adaptive Vector Quantization
335(2)
10.8 Trellis-Coded Quantization
337(3)
10.9 Summary
340(2)
10.10 Projects and Problems
342(3)
11 Differential Encoding
345(28)
11.1 Overview
345(1)
11.2 Introduction
345(3)
11.3 The Basic Algorithm
348(4)
11.4 Prediction in DPCM
352(5)
11.5 Adaptive DPCM
357(4)
11.5.1 Adaptive Quantization in DPCM
358(1)
11.5.2 Adaptive Prediction in DPCM
358(3)
11.6 Delta Modulation
361(4)
11.6.1 Constant Factor Adaptive Delta Modulation (CFDM)
363(1)
11.6.2 Continuously Variable Slope Delta Modulation
364(1)
11.7 Speech Coding
365(4)
11.7.1 G.726
366(3)
11.8 Image Coding
369(2)
11.9 Summary
371(1)
11.10 Projects and Problems
371(2)
12 Mathematical Preliminaries for Transforms, Subbands, and Wavelets
373(36)
12.1 Overview
373(1)
12.2 Introduction
373(1)
12.3 Vector Spaces
374(6)
12.3.1 Dot or Inner Product
375(1)
12.3.2 Vector Space
375(2)
12.3.3 Subspace
377(1)
12.3.4 Basis
377(2)
12.3.5 Inner Product-Formal Definition
379(1)
12.3.6 Orthogonal and Orthonormal Sets
379(1)
12.4 Fourier Series
380(2)
12.5 Fourier Transform
382(3)
12.5.1 Parseval's Theorem
384(1)
12.5.2 Modulation Property
384(1)
12.5.3 Convolution Theorem
385(1)
12.6 Linear Systems
385(5)
12.6.1 Time Invariance
386(1)
12.6.2 Transfer Function
386(1)
12.6.3 Impulse Response
387(1)
12.6.4 Filter
388(2)
12.7 Sampling
390(4)
12.7.1 Ideal Sampling-Frequency Domain View
390(2)
12.7.2 Ideal Sampling-Time Domain View
392(2)
12.8 Discrete Fourier Transform
394(2)
12.9 Z-Transform
396(10)
12.9.1 Tabular Method
399(1)
12.9.2 Partial Fraction Expansion
399(4)
12.9.3 Long Division
403(1)
12.9.4 Z-Transform Properties
404(1)
12.9.5 Discrete Convolution
404(2)
12.10 Summary
406(1)
12.11 Projects and Problems
407(2)
13 Transform Coding
409(38)
13.1 Overview
409(1)
13.2 Introduction
409(5)
13.3 The Transform
414(4)
13.4 Transforms of Interest
418(6)
13.4.1 Karhunen-Loeve Transform
418(2)
13.4.2 Discrete Cosine Transform
420(3)
13.4.3 Discrete Sine Transform
423(1)
13.4.4 Discrete Walsh-Hadamard Transform
423(1)
13.5 Quantization and Coding of Transform Coefficients
424(8)
13.5.1 Operational Rate-Distortion Bit Allocation
428(4)
13.6 Application to Image Compression--JPEG
432(8)
13.6.1 The Transform
432(1)
13.6.2 Quantization
432(2)
13.6.3 Coding
434(4)
13.6.4 Format--JFIF
438(2)
13.7 Application to Audio Compression--The MDCT
440(3)
13.8 Summary
443(1)
13.9 Projects and Problems
444(3)
14 Subband Coding
447(50)
14.1 Overview
447(1)
14.2 Introduction
447(5)
14.3 Filters
452(7)
14.3.1 Some Filters Used in Subband Coding
456(3)
14.4 The Basic Subband Coding Algorithm
459(3)
14.4.1 Analysis
459(2)
14.4.2 Quantization and Coding
461(1)
14.4.3 Synthesis
461(1)
14.5 Design of Filter Banks
462(5)
14.5.1 Downsampling
463(3)
14.5.2 Upsampling
466(1)
14.6 Perfect Reconstruction Using Two-Channel Filter Banks
467(7)
14.6.1 Two-Channel PR Quadrature Mirror Filters
470(2)
14.6.2 Power Symmetric FIR Filters
472(2)
14.7 M-Band Quadrature Mirror Filter Banks
474(3)
14.8 The Polyphase Decomposition
477(5)
14.9 Bit Allocation
482(2)
14.10 Application to Speech Coding---G.722
484(1)
14.11 Application to Audio Coding---MPEG Audio
485(1)
14.12 Application to Image Compression
486(6)
14.12.1 Decomposing an Image
488(2)
14.12.2 Coding the Subbands
490(2)
14.13 Summary
492(1)
14.14 Projects and Problems
493(4)
15 Wavelets
497(32)
15.1 Overview
497(1)
15.2 Introduction
497(3)
15.3 Wavelets
500(4)
15.4 Multiresolution Analysis and the Scaling Function
504(6)
15.5 Implementation Using Filters
510(6)
15.5.1 Scaling and Wavelet Coefficients
513(3)
15.5.2 Families of Wavelets
516(1)
15.6 Biorthogonal Wavelets
516(7)
15.7 Lifting
523(4)
15.8 Summary
527(1)
15.9 Projects and Problems
528(1)
16 Wavelet-Based Image Compression
529(40)
16.1 Overview
529(1)
16.2 Introduction
529(3)
16.3 Embedded Zerotree Coder
532(8)
16.4 Set Partitioning in Hierarchical Trees
540(7)
16.5 JPEG 2000
547(21)
16.5.1 Color Component Transform
548(1)
16.5.2 Tiling
549(1)
16.5.3 Wavelet Transform
549(2)
16.5.4 Quantization
551(1)
16.5.5 Tier I Coding
552(7)
16.5.6 Tier II Coding
559(2)
16.5.7 JPEG 2000 bitstream
561(7)
16.6 Summary
568(1)
16.7 Projects and Problems
568(1)
17 Audio Coding
569(22)
17.1 Overview
569(1)
17.2 Introduction
569(4)
17.2.1 Spectral Masking
570(1)
17.2.2 Temporal Masking
571(1)
17.2.3 Psychoacoustic Model
572(1)
17.3 MPEG Audio Coding
573(8)
17.3.1 Layer I Coding
573(2)
17.3.2 Layer II Coding
575(2)
17.3.3 Layer III Coding--mp3
577(4)
17.4 MPEG Advanced Audio Coding
581(6)
17.4.1 MPEG-2 AAC
582(4)
17.4.2 MPEG-4 AAC
586(1)
17.5 Dolby AC-3 (Dolby Digital)
587(2)
17.5.1 Bit Allocation
588(1)
17.6 Other Standards
589(1)
17.7 Summary
589(2)
18 Analysis/Synthesis and Analysis by Synthesis Schemes
591(42)
18.1 Overview
591(1)
18.2 Introduction
591(2)
18.3 Speech Compression
593(18)
18.3.1 The Channel Vocoder
594(2)
18.3.2 The Linear Predictive Coder (Government Standard LPC-10)
596(7)
18.3.3 Code-Excited Linear Predicton (CELP)
603(3)
18.3.4 Sinusoidal Coders
606(2)
18.3.5 Mixed Excitation Linear Prediction (MELP)
608(3)
18.4 Wideband Speech Compression---ITU-T G.722.2
611(2)
18.5 Coding of Speech for Internet Applications
613(10)
18.5.1 iLBC
613(5)
18.5.2 G.729
618(3)
18.5.3 SILK
621(2)
18.6 Image Compression
623(8)
18.7 Summary
631(1)
18.8 Projects and Problems
632(1)
19 Viedo Compression
633(42)
19.1 Overview
633(1)
19.2 Introduction
633(2)
19.3 Motion Compensation
635(3)
19.4 Video Signal Representation
638(6)
19.5 ITU-T Recommendation H.261
644(5)
19.5.1 Motion Compensation
644(2)
19.5.2 The Loop Filter
646(1)
19.5.3 The Transform
647(1)
19.5.4 Quantization and Coding
647(2)
19.5.5 Rate Control
649(1)
19.6 Model-Based Coding
649(1)
19.7 Asymmetric Applications
650(2)
19.8 The MPEG-1 Video Standard
652(3)
19.9 The MPEG-2 Video Standard---H.262
655(3)
19.10 ITU-T Recommendation H.263
658(6)
19.10.1 Unrestricted Motion Vector Mode
660(1)
19.10.2 Syntax-Based Arithmetic Coding Mode
660(1)
19.10.3 Advanced Prediction Mode
661(1)
19.10.4 PB-Frames and Improved PB-Frames Mode
661(1)
19.10.5 Advanced Intra Coding Mode
661(1)
19.10.6 Deblocking Filter Mode
661(1)
19.10.7 Reference Picture Selection Mode
662(1)
19.10.8 Temporal, SNR, and Spatial Scalability Mode
662(1)
19.10.9 Reference Picture Resampling
662(1)
19.10.10 Reduced-Resolution Update Mode
662(1)
19.10.11 Alternative Inter VLC Mode
662(1)
19.10.12 Modified Quantization Mode
663(1)
19.10.13 Enhanced Reference Picture Selection Mode
663(1)
19.11 ITU-T Recommendation H.264, MPEG-4 Part 10, Advanced Video Coding
664(5)
19.11.1 Motion-Compensated Prediction
664(1)
19.11.2 The Transform
665(1)
19.11.3 Intra Prediction
666(1)
19.11.4 Quantization
666(2)
19.11.5 Coding
668(1)
19.12 MPEG-4 Part 2
669(1)
19.13 Packet Video
670(3)
19.13.1 ATM Networks
671(1)
19.13.2 Compression Issues in ATM Networks
671(1)
19.13.3 Compression Algorithms for Packet Video
672(1)
19.14 Summary
673(1)
19.15 Projects and Problems
674(1)
A Probability and Random Processes
675(16)
A.1 Probability
675(5)
A.1.1 Frequency of Occurrence
675(1)
A.1.2 A Measure of Belief
676(2)
A.1.3 The Axiomatic Approach
678(2)
A.2 Random Variables
680(1)
A.3 Distribution Functions
681(2)
A.4 Expectation
683(2)
A.4.1 Mean
685(1)
A.4.2 Second Moment
685(1)
A.4.3 Variance
685(1)
A.5 Types of Distribution
685(2)
A.5.1 Uniform Distribution
685(1)
A.5.2 Gaussian Distribution
686(1)
A.5.3 Laplacian Distribution
686(1)
A.5.4 Gamma Distribution
686(1)
A.6 Stochastic Process
687(2)
A.7 Projects and Problems
689(2)
B A Brief Review of Matrix Concepts
691(6)
B.1 A Matrix
691(1)
B.2 Matrix Operations
692(5)
C The Root Lattices
697(2)
Bibliography 699(18)
Index 717
Khalid Sayood received his BS and MS in Electrical Engineering from the University of Rochester in 1977 and 1979, respectively, and his Ph.D. in Electrical Engineering from Texas A&M University in 1982. In 1982, he joined the University of Nebraska, where he is the Heins Professor of Engineering. His research interests include data compression, joint source channel coding, and bioinformatics.
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