Combining non-stationary prediction, optimization and mixing for data compression

Combining non-stationary prediction, optimization and mixing for data compression

In this paper an approach to modelling nonstationary binary sequences, i.e., predicting the probability of upcoming symbols, is presented. After studying the prediction model we evaluate its performance in two non-artificial test cases. First the model is compared to the Laplace and Krichevsky-Trofi...

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Veröffentlicht in:arXiv.org 2013-02
1. Verfasser: Mattern, Christopher
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Sprache:eng
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Burrows-Wheeler transform
Compression tests
Computer Science - Information Theory
Data compression
Mathematical models
Mathematics - Information Theory
Optimization
Prediction models
Statistical analysis
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description In this paper an approach to modelling nonstationary binary sequences, i.e., predicting the probability of upcoming symbols, is presented. After studying the prediction model we evaluate its performance in two non-artificial test cases. First the model is compared to the Laplace and Krichevsky-Trofimov estimators. Secondly a statistical ensemble model for compressing Burrows-Wheeler-Transform output is worked out and evaluated. A systematic approach to the parameter optimization of an individual model and the ensemble model is stated.
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subjects Burrows-Wheeler transform
Compression tests
Computer Science - Information Theory
Data compression
Mathematical models
Mathematics - Information Theory
Optimization
Prediction models
Statistical analysis
title Combining non-stationary prediction, optimization and mixing for data compression
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