品牌：萨尤得
基本信息·出版社：人民邮电出版社
·页码：680 页
·出版日期：2009年
·ISBN：9787115195203
·条形码：9787115195203
·版本：1版
·装帧：平装
·开本：16
·英语：英语
·丛书名：图灵原版计算机科学系列
·外文书名：Introduction to Data Compression Third Edition
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内容简介《数据压缩导论(英文版.第3版)》是数据压缩方面的经典著作，介绍了各种类型的压缩模式。书中首先介绍了基本压缩方法(包括无损压缩和有损压缩)中涉及的数学知识，为常见的压缩形式打牢了信息论基础，然后从无损压缩体制开始，依次讲述了霍夫曼编码、算术编码以及字典编码技术等，对于有损压缩，还讨论了使用量化的模式，描述了标量、矢量以及微分编码和分形压缩技术，最后重点介绍了视频加密。《数据压缩导论(英文版.第3版)》不但分析了各种压缩模式及其优缺点，而且还说明了它们最适合处理哪种内容。
《数据压缩导论(英文版.第3版)》非常适合从事数据压缩相关工作的专业技术人员、软硬件工程师、学生等阅读，数字图书馆、多媒体等领域的技术人员也可参考。
作者简介Khalid Sayood，著名数据压缩技术专家，内布拉斯加大学教授得克萨斯A&M大学电气工程专业博士。他的研究方向包括数据压缩、信源信道联合编码和生物信息学。
媒体推荐“从各方面来看，本书都无愧于数据压缩圣经的称号。新版本内容及时、精益求精。”
——Amazon读者评论
编辑推荐数据压缩技术在网络、通信、图像处理、多媒体、数据库等诸多领域应用广泛，在现实需求推动下，近年来发展尤为迅速。
《数据压缩导论(英文版.第3版)》是数据压缩领域毋庸置疑的权威指南，以内容全面、新颖而著称。书中不仅深入地阐述了各种压缩技术背后的理论、优缺点和适用范围，更通过丰富实例，详细讨论了各自的应用。书中提供了许多工具，读者足以由此自己开发出完整的压缩方案。
《数据压缩导论(英文版.第3版)》特色
涵盖各种常用和重要的视频、音频、文本以及传真的压缩标准。
包括有损压缩和无损压缩技术在图像、语音、文本、音频以及视频压缩中的应用。
增加了新的一章，讨论音频压缩，包括MP3算法。
讨论了视频编码新标准，包括H.264、MPEG-4等。
每个新概念或算法都辅有详细的例子。
配套网站http：／／www.elsevierdirect.com／companion.jsp？ISBN=9780126208627提供软件实现源代码和实验数据。
目录
1 Introduction 1
1.1 Compression Techniques 3
1.1.1 Lossless Compression 4
1.1.2 Lossy Compression 5
1.1.3 Measures of Performance 5
1.2 Modeling and Coding 6
1.3 Summary 10
1.4 Projects and Problems 11
2 Mathematical Preliminaries for Lossless Compression 13
2.1 Overview 13
2.2 A Brief Introduction to Information Theory 13
2.2.1 Derivation of Average Information 18
2.3 Models 23
2.3.1 Physical Models 23
2.3.2 Probability Models 23
2.3.3 Markov Models 24
2.3.4 Composite Source Model 27
2.4 Coding 27
2.4.1 Uniquely Decodable Codes 28
2.4.2 Prefix Codes 31
2.4.3 The Kraft-McMillan Inequality 32
2.5 Algorithmic Information Theory 35
2.6 Minimum Description Length Principle 36
2.7 Summary 37
2.8 Projects and Problems 38
3 Huffman Coding 41
3.1 Overview 41
3.2 The Huffman Coding Algorithm 41
3.2.1 Minimum Variance Huffman Codes 46
3.2.2 Optimality of Huffman Codes 48
3.2.3 Length of Huffman Codes 49
3.2.4 Extended Huffman Codes 51
3.3 Nonbinary Huffman Codes 55
3.4 Adaptive Huffman Coding 58
3.4.1 Update Procedure 59
3.4.2 Encoding Procedure 62
3.4.3 Decoding Procedure 63
3.5 Golomb Codes 65
3.6 Rice Codes 67
3.6.1 CCSDS Recommendation for Lossless Compression 67
3.7 Tunstall Codes 69
3.8 Applications of Huffman Coding 72
3.8.1 Lossless Image Compression 72
3.8.2 Text Compression 74
3.8.3 Audio Compression 75
3.9 Summary 77
3.10 Projects and Problems 77
4 Arithmetic Coding 81
4.1 Overview 81
4.2 Introduction 81
4.3 Coding a Sequence 83
4.3.1 Generating a Tag 84
4.3.2 Deciphering the Tag 91
4.4 Generating a Binary Code 92
4.4.1 Uniqueness and Efficiency of the Arithmetic Code 93
4.4.2 Algorithm Implementation 96
4.4.3 Integer Implementation 102
4.5 Comparison of Huffman and Arithmetic Coding 109
4.6 Adaptive Arithmetic Coding 112
4.7 Applications 112
4.8 Summary 113
4.9 Projects and Problems 114
5 Dictionary Techniques 117
5.1 Overview 117
5.2 Introduction 117
5.3 Static Dictionary 118
5.3.1 Digram Coding 119
5.4 Adaptive Dictionary 121
5.4.1 The LZ7.7 Approach 121
5.4.2 The LZ78 Approach 125
5.5 Applications 133
5.5.1 File Compression-UNIX compress 133
5.5.2 Image Compression-The Graphics Interchange Format (GIF) 133
5.5.3 Image Compression-Portable Network Graphics (PNG) 134
5.5.4 Compression over Modems-V.42 bis 136
5.6 Summary 138
5.7 Projects and Problems 139
6 Context-Based Compression 141
6.1 Overview 141
6.2 Introduction 141
6.3 Prediction with Partial Match (ppm) 143
6.3.1 The Basic Algorithm 143
6.3.2 The Escape Symbol 149
6.3.3 Length of Context 150
6.3.4 The Exclusion Principle 151
6.4 The Burrows-Wheeler Transform 152
6.4.1 Move-to-Front Coding 156
6.5 Associative Coder of Buyanovsky (ACB) 157
6.6 Dynamic Markov Compression 158
6.7 Summary 160
6.8 Projects and Problems 161
7 Lossless Image Compression 163
7.1 Overview 163
7.2 Introduction 163
7.2.1 The Old JPEG Standard 164
7.3 CALIC 166
7.4 JPEG-LS 170
7.5 Multiresolution Approaches 172
7.5.1 Progressive Image Transmission 173
7.6 Facsimile Encoding 178
7.6.1 Run-Length Coding 179
7.6.2 CCITT Group 3 and 4-Recommendations T.4 and T.6 180
7.6.3 JBIG 183
7.6.4 JBIG2-T.88 189
7.7 MRC-T.44 190
7.8 Summary 193
7.9 Projects and Problems 193
8 Mathematical Preliminaries for Lossy Coding 195
8.1 Overview 195
8.2 Introduction 195
8.3 Distortion Criteria 197
8.3.1 The Human Visual System 199
8.3.2 Auditory Perception 200
8.4 Information Theory Revisited 201
8.4.1 Conditional Entropy 202
8.4.2 Average Mutual Information 204
8.4.3 Differential Entropy 205
8.5 Rate Distortion Theory 208
8.6 Models 215
8.6.1 Probability Models 216
8.6.2 Linear System Models 218
8.6.3 Physical Models 223
8.7 Summary 224
8.8 Projects and Problems 224
9 Scalar Quantization 227
9.1 Overview 227
9.2 Introduction 227
9.3 The Quantization Problem 228
9.4 Uniform Quantizer 233
9.5 Adaptive Quantization 244
9.5.1 Forward Adaptive Quantization 244
9.5.2 Backward Adaptive Quantization 246
9.6 Nonuniform Quantization 253
9.6.1 pdf-Optimized Quantization 253
9.6.2 Companded Quantization 257
9.7 Entropy-Coded Quantization 264
9.7.1 Entropy Coding of Lloyd-Max Quantizer Outputs 265
9.7.2 Entropy-Constrained Quantization 265
9.7.3 High-Rate Optimum Quantization 266
9.8 Summary 269
9.9 Projects and Problems 270
10 Vector Quantization 273
10.1 Overview 273
10.2 Introduction 273
10.3 Advantages of Vector Quantization over Scalar Quantization 276
10.4 The Linde-Buzo-Gray Algorithm 282
10.4.1 Initializing the LBG Algorithm 287
10.4.2 The Empty Cell Problem 294
10.4.3 Use of LBG for Image Compression 294
10.5 Tree-Structured Vector Quantizers 299
10.5.1 Design of Tree-Structured Vector Quantizers 302
10.5.2 Pruned Tree-Structured Vector Quantizers 303
10.6 Structured Vector Quantizers 303
10.6.1 Pyramid Vector Quantization 305
10.6.2 Polar and Spherical Vector Quantizers 306
10.6.3 Lattice Vector Quantizers 307
10.7 Variations on the Theme 311
10.7.1 Gain-Shape Vector Quantization 311
10.7.2 Mean-Removed Vector Quantization 312
10.7.3 Classified Vector Quantization 313
10.7.4 Multistage Vector Quantization 313
10.7.5 Adaptive Vector Quantization 315
10.8 Trellis-Coded Quantization 316
10.9 Summary 321
10.10 Projects and Problems 322
11 Differential Encoding 325
11.1 Overview 325
11.2 Introduction 325
11.3 The Basic Algorithm 328
11.4 Prediction in DPCM 332
11.5 Adaptive DPCM 337
11.5.1 Adaptive Quantization in DPCM 338
11.5.2 Adaptive Prediction in DPCM 339
11.6 Delta Modulation 342
11.6.1 Constant Factor Adaptive Delta Modulation (CFDM) 343
11.6.2 Continuously Variable Slope Delta Modulation 345
11.7 Speech Coding 345
11.7.1 G.726 347
11.8 Image Coding 349
11.9 Summary 351
11.10 Projects and Problems 352
12 Mathematical Preliminaries for Transforms, Subbands, and Wavelets 355
12.1 Overview 355
12.2 Introduction 355
12.3 Vector Spaces 356
12.3.1 Dot or Inner Product 357
12.3.2 Vector Space 357
12.3.3 Subspace 359
12.3.4 Basis 360
12.3.5 Inner Product-Formal Definition 361
12.3.6 Orthogonal and Orthonormal Sets 361
12.4 Fourier Series 362
12.5 Fourier Transform 365
12.5.1 Parseval’s Theorem 366
12.5.2 Modulation Property 366
12.5.3 Convolution Theorem 367
12.6 Linear Systems 368
12.6.1 Time Invariance 368
12.6.2 Transfer Function 368
12.6.3 Impulse Response 369
12.6.4 Filter 371
12.7 Sampling 372
12.7.1 Ideal Sampling-Frequency Domain View 373
12.7.2 Ideal Sampling-Time Domain View 375
12.8 Discrete Fourier Transform 376
12.9 Z-Transform 378
12.9.1 Tabular Method 381
12.9.2 Partial Fraction Expansion 382
12.9.3 Long Division 386
12.9.4 Z-Transform Properties 387
12.9.5 Discrete Convolution 387
12.10 Summary 389
12.11 Projects and Problems 390
13 Transform Coding 391
13.1 Overview 391
13.2 Introduction 391
13.3 The Transform 396
13.4 Transforms of Interest 400
13.4.1 Karhunen-Loéve Transform 401
13.4.2 Discrete Cosine Transform 402
13.4.3 Discrete Sine Transform 404
13.4.4 Discrete Walsh-Hadamard Transform 404
13.5 Quantization and Coding of Transform Coefficients 407
13.6 Application to Image Compression-JPEG 410
13.6.1 The Transform 410
13.6.2 Quantization 411
13.6.3 Coding 413
13.7 Application to Audio Compression-the MDCT 416
13.8 Summary 419
13.9 Projects and Problems 421
14 Subband Coding 423
14.1 Overview 423
14.2 Introduction 423
14.3 Filters 428
14.3.1 Some Filters Used in Subband Coding 432
14.4 The Basic Subband Coding Algorithm 436
14.4.1 Analysis 436
14.4.2 Quantization and Coding 437
14.4.3 Synthesis 437
14.5 Design of Filter Banks 438
14.5.1 Downsampling 440
14.5.2 Upsampling 443
14.6 Perfect Reconstruction Using Two-Channel Filter Banks 444
14.6.1 Two-Channel PR Quadrature Mirror Filters 447
14.6.2 Power Symmetric FIR Filters 449
14.7 M-Band QMF Filter Banks 451
14.8 The Polyphase Decomposition 454
14.9 Bit Allocation 459
14.10 Application to Speech Coding-G.722 461
14.11 Application to Audio Coding-MPEG Audio 462
14.12 Application to Image Compression 463
14.12.1 Decomposing an Image 465
14.12.2 Coding the Subbands 467
14.13 Summary 470
14.14 Projects and Problems 471
15 Wavelet-Based Compression 473
15.1 Overview 473
15.2 Introduction 473
15.3 Wavelets 476
15.4 Multiresolution Analysis and the Scaling Function 480
15.5 Implementation Using Filters 486
15.5.1 Scaling and Wavelet Coefficients 488
15.5.2 Families of Wavelets 491
15.6 Image Compression 494
15.7 Embedded Zerotree Coder 497
15.8 Set Partitioning in Hierarchical Trees 505
15.9 JPEG 2000 512
15.10 Summary 513
15.11 Projects and Problems 513
16 Audio Coding 515
16.1 Overview 515
16.2 Introduction 515
16.2.1 Spectral Masking 517
16.2.2 Temporal Masking 517
16.2.3 Psychoacoustic Model 518
16.3 MPEG Audio Coding 519
16.3.1 Layer I Coding 520
16.3.2 Layer II Coding 521
16.3.3 Layer III Coding-mp3 522
16.4 MPEG Advanced Audio Coding 527
16.4.1 MPEG-2 AAC 527
16.4.2 MPEG-4 AAC 532
16.5 Dolby AC3 (Dolby Digital) 533
16.5.1 Bit Allocation 534
16.6 Other Standards 535
16.7 Summary 536
17 Analysis/Synthesis and Analysis by Synthesis Schemes 537
17.1 Overview 537
17.2 Introduction 537
17.3 Speech Compression 539
17.3.1 The Channel Vocoder 539
17.3.2 The Linear Predictive Coder (Government Standard LPC-10) 542
17.3.3 Code Excited Linear Predicton (CELP) 549
17.3.4 Sinusoidal Coders 552
17.3.5 Mixed Excitation Linear Prediction (MELP) 555
17.4 Wideband Speech Compression-ITU-T G.722.2 558
17.5 Image Compression 559
17.5.1 Fractal Compression 560
17.6 Summary 568
17.7 Projects and Problems 569
18 Video Compression 571
18.1 Overview 571
18.2 Introduction 571
18.3 Motion Compensation 573
18.4 Video Signal Representation 576
18.5 ITU-T Recommendation H.261 582
18.5.1 Motion Compensation 583
18.5.2 The Loop Filter 584
18.5.3 The Transform 586
18.5.4 Quantization and Coding 586
18.5.5 Rate Control 588
18.6 Model-Based Coding 588
18.7 Asymmetric Applications 590
18.8 The MPEG-1 Video Standard 591
18.9 The MPEG-2 Video Standard-H.262 594
18.9.1 The Grand Alliance HDTV Proposal 597
18.10 ITU-T Recommendation H.263 598
18.10.1 Unrestricted Motion Vector Mode 600
18.10.2 Syntax-Based Arithmetic Coding Mode 600
18.10.3 Advanced Prediction Mode 600
18.10.4 PB-frames and Improved PB-frames Mode 600
18.10.5 Advanced Intra Coding Mode 600
18.10.6 Deblocking Filter Mode 601
18.10.7 Reference Picture Selection Mode 601
18.10.8 Temporal, SNR, and Spatial Scalability Mode 601
18.10.9 Reference Picture Resampling 601
18.10.10 Reduced-Resolution Update Mode 602
18.10.11 Alternative Inter VLC Mode 602
18.10.12 Modified Quantization Mode 602
18.10.13 Enhanced Reference Picture Selection Mode 603
18.11 ITU-T Recommendation H.264, MPEG-4 Part 10, Advanced Video Coding 603
18.11.1 Motion-Compensated Prediction 604
18.11.2 The Transform 605
18.11.3 Intra Prediction 605
18.11.4 Quantization 606
18.11.5 Coding 608
18.12 MPEG-4 Part 2 609
18.13 Packet Video 610
18.14 ATM Networks 610
18.14.1 Compression Issues in ATM Networks 611
18.14.2 Compression Algorithms for Packet Video 612
18.15 Summary 613
18.16 Projects and Problems 614
A Probability and Random Processes 615
A.1 Probability 615
A.1.1 Frequency of Occurrence 615
A.1.2 A Measure of Belief 616
A.1.3 The Axiomatic Approach 618
A.2 Random Variables 620
A.3 Distribution Functions 621
A.4 Expectation 623
A.4.1 Mean 624
A.4.2 Second Moment 625
A.4.3 Variance 625
A.5 Types of Distribution 625
A.5.1 Uniform Distribution 625
A.5.2 Gaussian Distribution 626
A.5.3 Laplacian Distribution 626
A.5.4 Gamma Distribution 626
A.6 Stochastic Process 626
A.7 Projects and Problems 629
B A Brief Review of Matrix Concepts 631
B.1 A Matrix 631
B.2 Matrix Operations 632
C The Root Lattices 637
Bibliography 639
Index 655
……[看更多目录]
序言Within the last decade the use of data compression has become ubiquitous. From mp3 players whose headphones seem to adorn the ears of most young （and some not so young） people, to cell phones, to DVDs, to digital television, data compression is an integral part of almost all information technology. This incorporation of compression into more and more of our lives also points to a certain degree of maturation of the technology. This maturity is reflected in the fact that there are fewer differences between this and the previous edition of this book than there were between the second and first editions. In the second edition we had added new techniques that had been developed since the first edition of this book came out. In this edition our purpose is more to include some important topics, such as audio compression, that had not been adequately covered in the second edition. During this time the field has not entirely stood still and we have tried to include information about new developments. We have added a new chapter on audio compression （including a description of the mp3 algorithm）. We have added information on new standards such as the new video coding standard and the new facsimile standard. We have reorganized some of the material in the book, collecting together various lossless image compression techniques and standards into a single chapter, and we have updated a number of chapters, adding information that perhaps should have been there from the beginning.
All this has yet again enlarged the book. However, the intent remains the same: to provide an introduction to the art or science of data compression. There is a tutorial description of most of the popular compression techniques followed by a description of how these techniques are used for image, speech, text, audio, and video compression.
Given the pace of developments in this area, there are bound to be new ones that are not reflected in this book. In order to keep you informed of these developments, we will periodically provide updates at http://www.mkp.com.
文摘Example 9.6.1:
Suppose we have a source that can be modeled as a random variable taking values in the interval [-4，4] with more probability mass near the origin than away from it. We want to quantize this using the quantizer of Figure 9.3. Let us try to flatten out this distribution using the following compander, and then compare the companded quantization with straightforward uniform quantization. The compressor characteristic we will use is given by the following equation：
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