The context-tree weighting method: basic properties

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The context-tree weighting method: basic properties

We describe a sequential universal data compression procedure for binary tree sources that performs the " double mixture. " Using a context tree, this method weights in an efficient recursive way the coding distributions corresponding to all bounded memory tree sources, and achieves a desirable coding distribution for tree sources with an unknown model and unknown parameters. Computational and ...

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Reflections on “The Context-Tree Weighting Method: Basic Properties”

Copyright c ©1997 IEEE. Reprinted from IEEE Information Theory Society Newsletter, Vol. 47, No. 1, March 1997. This material is posted here with permission of the IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obt...

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We describe a sequential universal data compression procedure for binary tree sources that performs the \double mixture". Using a context tree, this method weights in an eecient recursive way the coding distributions corresponding to all bounded memory tree sources, and achieves a desirable coding distribution for tree sources with an unknown model and unknown parameters. Computational and stor...

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The Context-Tree Weighting Method : Extensions

First we modify the basic (binary) context-tree weighting method such that the past symbols x1 D; x2 D; ; x0 are not needed by the encoder and the decoder. Then we describe how to make the context-tree depth D infinite, which results in optimal redundancy behavior for all tree sources, while the number of records in the context tree is not larger than 2T 1: Here T is the length of the source se...

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Implementing the Context - Tree Weighting Method : Arithmetic Coding

The context-tree weighting algorithm 6] is an eecient universal source coding method for tree sources. Although a nite accuracy version of this algorithm has been analysed in 8], it is better to implement the algorithm as proposed in 7]. There it was suggested to store in each node s of the context tree, instead of an estimated probability P s e and a weighted probability P s w , the (logarithm...

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ژورنال

عنوان ژورنال: IEEE Transactions on Information Theory

سال: 1995

ISSN: 0018-9448

DOI: 10.1109/18.382012