Robert M. Gray Knihy

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. Inhaltsverzeichnis 1 Introduction.- 1.1 Signals, Coding, and Compression.- 1.2 Optimality.- 1.3 How to Use this Book.- 1.4 Related Reading.- I Basic Tools.- 2 Random Processes and Linear Systems.- 3 Sampling.- 4 Linear Prediction.- II Scalar Coding.- 5 Scalar Quantization I.- 6 Scalar Quantization II.- 7 Predictive Quantization.- 8 Bit Allocation and Transform Coding.- 9 Entropy Coding.- III Vector Coding.- 10 Vector Quantization I.- 11 Vector Quantization II.- 12 Constrained Vector Quantization.- 13 Predictive Vector Quantization.- 14 Finite-State Vector Quantization.- 15 Tree and Trellis Encoding.- 16 Adaptive Vector Quantization.- 17 Variable Rate Vector Quantization.

The book delves into source coding theory, focusing on optimizing performance in digital communication systems. It emphasizes the importance of achieving high fidelity in information transmission, balancing constraints like rate and complexity. Key historical contributions from Shannon on noiseless coding and rate-distortion theory are highlighted, showcasing the blend of probabilistic information and ergodic theory. Additionally, the text discusses Bennett's asymptotic quantization theory, which prioritizes delay constraints while accommodating higher transmission rates.

Devoted to the theory of probabilistic information measures and their application to coding theorems for information sources and noisy channels, this volume also covers the methods required to prove the coding theorems of Shannon's mathematical theory.

Focusing on the challenges of image distribution and utilization in the digital age, this book explores effective solutions through advanced image processing techniques. Key topics include segmentation and compression, which address the critical issues stemming from the proliferation of digital images. The content is aimed at enhancing understanding and application of these techniques in various contexts.