Vector Quantization and Signal Compression

1. Front Matter

   Pages i-xxii

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2. Introduction

   1. Front Matter

      Pages 1-1

      PDF

   2. Introduction

      • Allen Gersho, Robert M. Gray

      Pages 1-13

3. Basic Tools

   1. Front Matter

      Pages 15-15

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   2. Random Processes and Linear Systems

      • Allen Gersho, Robert M. Gray

      Pages 17-47

   3. Sampling

      • Allen Gersho, Robert M. Gray

      Pages 49-81

   4. Linear Prediction

      • Allen Gersho, Robert M. Gray

      Pages 83-129

4. Scalar Coding

   1. Front Matter

      Pages 131-131

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   2. Scalar Quantization I: Structure and Performance

      • Allen Gersho, Robert M. Gray

      Pages 133-172

   3. Scalar Quantization II:Optimality and Design

      • Allen Gersho, Robert M. Gray

      Pages 173-202

   4. Predictive Quantization

      • Allen Gersho, Robert M. Gray

      Pages 203-223

   5. Bit Allocation and Transform Coding

      • Allen Gersho, Robert M. Gray

      Pages 225-257

   6. Entropy Coding

      • Allen Gersho, Robert M. Gray

      Pages 259-305

5. Vector Coding

   1. Front Matter

      Pages 307-307

      PDF

   2. Vector Quantization I:Structure and Performance

      • Allen Gersho, Robert M. Gray

      Pages 309-343

   3. Vector Quantization II:Optimality and Design

      • Allen Gersho, Robert M. Gray

      Pages 345-405

   4. Constrained Vector Quantization

      • Allen Gersho, Robert M. Gray

      Pages 407-485

   5. Predictive Vector Quantization

      • Allen Gersho, Robert M. Gray

      Pages 487-517

   6. Finite—State Vector Quantization

      • Allen Gersho, Robert M. Gray

      Pages 519-553

   7. Tree and Trellis Encoding

      • Allen Gersho, Robert M. Gray

      Pages 555-586

Herb Caen, a popular columnist for the San Francisco Chronicle, recently quoted a Voice of America press release as saying that it was reorganizing in order to "eliminate duplication and redundancy. " This quote both states a goal of data compression and illustrates its common need: the removal of duplication (or redundancy) can provide a more efficient representation of data and the quoted phrase is itself a candidate for such surgery. Not only can the number of words in the quote be reduced without losing informa­ tion, but the statement would actually be enhanced by such compression since it will no longer exemplify the wrong that the policy is supposed to correct. Here compression can streamline the phrase and minimize the em­ barassment while improving the English style. Compression in general is intended to provide efficient representations of data while preserving the essential information contained in the data. This book is devoted to the theory and practice of signal compression, i. e. , data compression applied to signals such as speech, audio, images, and video signals (excluding other data types such as financial data or general­ purpose computer data). The emphasis is on the conversion of analog waveforms into efficient digital representations and on the compression of digital information into the fewest possible bits. Both operations should yield the highest possible reconstruction fidelity subject to constraints on the bit rate and implementation complexity.

• Jitter
• Modulation
• Signal
• analog
• coding
• complexity
• computer
• data compression
• entropy
• information
• simulation

• University of California, Santa Barbara, USA

  Allen Gersho

• Stanford University, USA

  Robert M. Gray

Policies and ethics