NDLI: Stochastic Automata-Based Estimators for Adaptively Compressing Files With Nonstationary Distributions

Content Provider	IEEE Xplore Digital Library
Author	Rueda, L. Oommen, B.J.
Copyright Year	1996
Abstract	This correspondence shows that learning automata techniques, which have been useful in developing weak estimators, can be applied to data compression applications in which the data distributions are nonstationary. The adaptive coding scheme utilizes stochastic learning- based weak estimation techniques to adaptively update the probabilities of the source symbols, and this is done without resorting to either maximum likelihood, Bayesian, or sliding-window methods. The authors have incorporated the estimator in the adaptive Fano coding scheme and in an adaptive entropy-based scheme that "resembles" the well-known arithmetic coding. The empirical results obtained for both of these adaptive methods are obtained on real-life files that possess a fair degree of nonstationarity. From these results, it can be seen that the proposed schemes compress nearly 10% more than their respective adaptive methods that use maximum-likelihood estimator-based estimates
Page Count	5
File Size	198328
Starting Page	1196
Ending Page	1200
File Format	PDF
ISSN	10834419
Volume Number	36
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-10-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic processes Encoding Adaptive coding Data compression Maximum likelihood estimation Arithmetic Technological innovation Computer science Predictive models Learning automata weak estimators Fano coding nonstationary sources online data compression
Content Type	Text
Resource Type	Article
Subject	Control and Systems Engineering Information Systems Electrical and Electronic Engineering Human-Computer Interaction Computer Science Applications Software

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