增强的基于单词的块排序文本压缩

R. Isal, Alistair Moffat, A. Ngai
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引用次数: 25

摘要

Burrows和Wheeler的块排序过程可以应用于任何符号互为条件(或可能互为条件)的序列。特别是,可以将文本解析为单词流,然后使用块排序来识别并利用单词之间的任何条件作用关系。在本文中,我们在两位作者之前的工作的基础上,描述了几个进一步的最近秩变换,并考虑了熵编码器的作用。通过将最好的新近因变换与熵编码器(根据以前的总体特征进行排序)相结合,我们能够在典型文本文件上获得改进的压缩。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced Word-Based Block-Sorting Text Compression
The Block Sorting process of Burrows and Wheeler can be applied to any sequence in which symbols are (or might be) conditioned upon each other. In particular, it is possible to parse text into a stream of words, and then employ block sorting to identify and so exploit any conditioning relationships between words. In this paper we build upon the previous work of two of the authors, describing several further recency rank transformations, and considering also the role of the entropy coder. By combining the best of the new recency transformations with an entropy coder that conditions ranks upon gross characteristics of previous ones, we are able to obtain improved compression on typical text files.
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