结合非平稳预测、优化和混合的数据压缩

2011 First International Conference on Data Compression, Communications and Processing Pub Date : 2011-06-21 DOI:10.1109/CCP.2011.22

Christopher Mattern

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引用次数: 5

摘要

本文提出了一种非平稳二值序列的建模方法，即预测即将出现的符号的概率。在研究了预测模型之后，我们在两个非人工的测试用例中对其性能进行了评估。首先，将模型与拉普拉斯估计和克里切夫斯基-特罗菲莫夫估计进行比较。其次，提出了一种用于压缩Burrows-Wheeler-Transform输出的统计集成模型，并对其进行了评价。对单个模型和集成模型的参数优化提出了一种系统的方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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-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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2011 First International Conference on Data Compression, Communications and Processing

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