Recursive Source Coding: A Theory for the Practice of Waveform Coding

106 Pages·1986·2.779 MB·English

by  G. Gabor, Z. Györfi (auth.)

Recursive Source Coding
G. Gabor Z.Gyorfi
Recursive
Source Coding
A Theory for the Practice of
Waveform Coding
Springer-Verlag
New York Berlin Heidelberg
London Paris Tokyo
G. Gabor Z. Gyorfi
Department of Mathematics. Institute of Telecommunication
Statistics and Computing Science Electronics
Dalhousie University Technical University of Budapest
Halifax. Nova Scotia Budapest
Canada Hungary
Library of Congress Cataloging in Publication Data
Gabor. G.
Recursive source coding.
Bibliography: p.
I. Data compression (Telecommunication) 2. Coding
theory. I. Gy6rfi, Z. II. Title.
TK5102.5.G33 1986 005.74'6 86-6564
© 1986 by Springer-Verlag New York Inc.
Softcover reprint ofthe hardcover 1st edition 1986
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ISBN-13: 978-\-4613-8651-3 e-ISBN-13: 978-1-4613-8649-0
DOl: 10.1007/978-1-4613-8649-0
To Wives, Julie and Eva
OUf
Preface
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.
The work of R.M. Gray started a slow process of reconciliation between
the theory and practice of DC in the beginning of the 1970s, when the
theoretical investigation of the recursive methods began. Constructive re
sults, however, are yet to be seen. The first attempts to apply block-coding
methods took place at the same time, and the revision of the nonstationary
model of a data source has also started.
Classical information theory so far has had very little to say about the
most widely used DPC's. The purpose of this work is to present a thorough
viii Introduction
criticism of the field of DPC by creating a general model of recursive
coding. It will be shown that the existing DPC algorithms are burdened
with unnecessary structural restrictions. Moreover, even if the ideas be
hind these methods are accepted, the usual design routines are based on
a strange misunderstanding, namely, the idea of the minimization of the
prediction-error.
In the first chapter we introduce a generalization of the DPC which is
capable of dealing with arbitrary structural constraints and does not ex
clude, at least in theory, reaching the theoretical limits established by the
theory of information.
The second chapter deals with structural and design problems of the
general model and investigates whether the two apparently natural prop
erties of the most popular methods, the so-called "equimemory" (EM)
and "minimum search" (MS) properties, are theoretically necessary con
ditions of optimality. An equimemorized, or EM, recursive quantizer (RQ)
in which the memories of the coder and the decoder are identical ignores
its coder's access to the past of the source. The MS property simply means
that, given the input, the coder's choice of code-word is always the one
which is closest to the input in the decoder's reproduction.
The third chapter returns to the investigation of the DPC's and the fourth
chapter illustrates the results on real-life design examples.
Contents
CHAPTER I
The Fine-McMillan Recursive Quantizer Model
I. I Source, Channel, Reproduction I
1.2 The Linear Deltamodulator 2
1.3 The Definition of a Fine-McMillan Recursive Quantizer 3
1.4 The Design Problem 6
1.5 The Simple Quantizer 9
1.6 Theoretical Limits with Given Channel Capacity 12
CHAPTER 2
Structural and Design Problems of a Recursive Quantizer 14
2.1 The McMillan Structure Problem 14
2.2 Fine's Principle of Minimum Search 15
2.3 The Principle of Minimum Search and the Property of Equimemory 20
2.4 Optimality and the EM Property-the McMillan Structure Theorem 25
2.5 Strong-optimality, MS and EM Properties-the Reformulation of the
McMillan Structure Theorem 28
2.6 The Proof of the Structure Theorem 30
2.7 Feed-forward Design for the Causal Case 35
2.8 Trellis Coders in Delayed Recursive Quantizers 39
CHAPTER 3
Differential Predictive Quantizers 43
3.1 Additive Decoding 43
3.2 Additive Decoding, MS and EM Properties-the Definition of the
Differential Predictive Quantizer 48
3.3 A Misunderstanding Concerning the Predictor 54
3.4 Additive Decoding and the Feed-forward Principle 61
x Contents
CHAPTER 4
Design ExampJes-Speech Compression 67
4.1 The Stationary Model of Speech 68
4.2 The Design of a DPC 70
4.3 The Design of a Fine-McMillan Type RQ 74
References 76
Appendix 1 81
Appendix 2 89
Appendix 3 96
CHAPTER 1
The Fine-McMillan Recursive
Quantizer Model
In this chapter we shall formulate a source coding model which, we hope, will
shed new light on the waveform coding techniques presently in'use. Before
we give the definition of the general model, however, we would like to briefly
look at (using the well-known deltamodulator as an example) the type of
problems and concepts that will be the main subject of this work.
An important special case, the simple quantizer, will also be discussed. An
analysis of the theoretical limits in terms of our model will close this chapter.
1.1 Source, Channel, Reproduction
We assume that the double infinite sequence of random variables (r.v.)
{Xn}~oo = { ... ,X-1,XO,X1,···} = {Xn}' the message-to-be-coded or source, is
stationary and E\Xo\ < + 00. The coded message {Pn}~oo = {Pn} is a sequence
of B valued r.v.'s, where B = W!), ... ,b (M)} is the channel alphabet. The
reproduction of {Xn}, or decoded message, {Xn}~oo = {Xn} is also a sequence
of r.v.'s. The channel is assumed to be noiseless. The fidelity of the reproduc
tion is defined and measured by the sequence {E[1/I(Xn-d,Xn)]}~oo' where
1/1: Rl x Rl ~ R+ is the distortion function and d is the delay of the system.
If the sequence {Xn,Xn}~oo is stationary, then the single number
±
n = 0, 1, ...
qualifies the system.
Finally, we assume that there is an x* E Rl such that
(1.1.1)
2 1 The Fine-McMillan Recursive Quantizer Model
1.2 The Linear Deltamodulator
The linear deltamodulator (LDM) may be considered as the prototype of the
family of differential predictive (OPC) techniques which almost exclusively
rule the field of waveform coding. A brief look at the LDM will give us an
opportunity to point out, without going into unnecessary detailes, those,
sometimes quite restrictive, properties of the DPC techniques which have, in
our view, rigidified into a kind of dogma and prompted us to formulate a more
general model.
Let the channel alphabet be B = {I, -I}, i.e., let the capacity of the noise
less channel be 1 bit. The coder of the LDM, defined as
n = 0, ±1, ... ,
where c is some constant, codes the difference of the actual input and its
"prediction" into 1 bit. The decoder, defined as
gIl = Cg"-l + LP", n = 0, ± 1, ... ,
where L is also some constant, corrects the prediction accord.ing to the coded
message (see Figure 1). The convention that in the first step the "previous
reproduction" in the coder and the decoder are arbitrary but identical is an
organic, though not explicitly stated, part of the definition of the LOM.
The OPC procedures are more general only so far as they apply more
complex rules of quantization for the prediction error as well as for the
prediction. They are variants of the LDM with somewhat weaker constraints
on their structure. The LDM, which is a causal, Fine-McMillan RQ (see
Section 1.3) with log2 M = 1 bit channel capacity, used to play an important
role in the practice of data compression (see [1], [4], [7], [48], [60]).
We will now point out a few distinctive features of, and formulate some
questions about the LOM.
(i) Note that the codeword sent by the coder of the LDM is always the one
for which the decoder's answer is closest to the actual input. Although
=* "
xn xn
cX" n_1
one step one step
delay delay
Figure 1