Combining non-stationary prediction, optimization and mixing for data compression
Information Theory
2013-02-13 v1 math.IT
Abstract
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.
Cite
@article{arxiv.1302.2856,
title = {Combining non-stationary prediction, optimization and mixing for data compression},
author = {Christopher Mattern},
journal= {arXiv preprint arXiv:1302.2856},
year = {2013}
}
Comments
International Conference on Data Compression, Communication and Processing (CCP) 2011