Overview

The spreading of digital technology has resulted in a dramatic increase in the demand for data compression (DC) methods. At the same time, the appearance of highly integrated elements has made more and more com­ plicated algorithms feasible. It is in the fields of speech and image trans­ mission and the transmission and storage of biological signals (e.g., ECG, Body Surface Mapping) where the demand for DC algorithms is greatest. There is, however, a substantial gap between the theory and the practice of DC: an essentially nonconstructive information theoretical attitude and the attractive mathematics of source coding theory are contrasted with a mixture of ad hoc engineering methods. The classical Shannonian infor­ mation theory is fundamentally different from the world of practical pro­ cedures. Theory places great emphasis on block-coding while practice is overwhelmingly dominated by theoretically intractable, mostly differential­ predictive coding (DPC), algorithms. A dialogue between theory and practice has been hindered by two pro­ foundly different conceptions of a data source: practice, mostly because of speech compression considerations, favors non stationary models, while the theory deals mostly with stationary ones.

Full Product Details

Author:   G. Gabor ,  Z. Györfi
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 1986
Dimensions:   Width: 15.50cm , Height: 0.50cm , Length: 23.50cm
Weight:   0.185kg
ISBN:  

9781461386513

ISBN 10:   1461386519
Pages:   102
Publication Date:   11 November 2011
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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Table of Contents

1 The Fine-McMillan Recursive Quantizer Model.- 1.1 Source, Channel, Reproduction.- 1.2 The Linear Deltamodulator.- 1.3 The Definition of a Fine-McMillan Recursive Quantizer.- 1.4 The Design Problem.- 1.5 The Simple Quantizer.- 1.6 Theoretical Limits with Given Channel Capacity.- 2 Structural and Design Problems of a Recursive Quantizer.- 2.1 The McMillan Structure Problem.- 2.2 Fine’s Principle of Minimum Search.- 2.3 The Principle of Minimum Search and the Property of Equimemory.- 2.4 Optimality and the EM Property—the McMillan Structure Theorem.- 2.5 Strong-optimality, MS and EM Properties—the Reformulation of the McMillan Structure Theorem.- 2.6 The Proof of the Structure Theorem.- 2.7 Feed-forward Design for the Causal Case.- 2.8 Trellis Coders in Delayed Recursive Quantizers.- 3 Differential Predictive Quantizers.- 3.1 Additive Decoding.- 3.2 Additive Decoding, MS and EM Properties—the Definition of the Differential Predictive Quantizer.- 3.3 A Misunderstanding Concerning the Predictor.- 3.4 Additive Decoding and the Feed-forward Principle.- 4 Design Examples—Speech Compression.- 4.1 The Stationary Model of Speech.- 4.2 The Design of a DPC.- 4.3 The Design of a Fine-McMillan Type RQ.- References.- Appendix 1.- Appendix 2.- Appendix 3.

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