Title :
Combining Non-stationary Prediction, Optimization and Mixing for Data Compression
Author :
Mattern, Christopher
Author_Institution :
Fak. fur Inf. und Automatisierung, Tech. Univ. Ilmenau, Ilmenau, Germany
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.
Keywords :
Laplace equations; data compression; optimisation; Burrows-Wheeler-Transform output; Krichevsky-Trofimov estimators; Laplace estimators; data compression; nonstationary prediction; parameter optimization; Approximation methods; Compression algorithms; Context; Numerical models; Optimization; Predictive models; Switches; combining models; data compression; ensemble prediction; mixing; numerical optimization; parameter optimization; sequential prediction;
Conference_Titel :
Data Compression, Communications and Processing (CCP), 2011 First International Conference on
Conference_Location :
Palinuro
Print_ISBN :
978-1-4577-1458-0
Electronic_ISBN :
978-0-7695-4528-8
DOI :
10.1109/CCP.2011.22
