Efficient universal lossless data compression algorithms based on a greedy context-dependent sequential grammar transform

Proceedings. 2001 IEEE International Symposium on Information Theory (IEEE Cat. No.01CH37252) Pub Date : 2001-06-24 DOI:10.1109/ISIT.2001.935941

E. Yang, Dake He

引用次数: 2

Abstract

In many applications like compression of text files, Web page files, and Java applets, there exists some a-priori knowledge, which often takes form of context models, about the data to be compressed. The challenging problem is then how to efficiently utilize the context models to improve the compression performance. We address this problem by extending results of Yang and Kieffer (see IEEE Trans. Inform. Theory, vol.IT-46, p.755-88, 2000), particularly the greedy context-free sequential grammar transform and the corresponding compression algorithms, to the case of context models.

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基于贪婪上下文相关顺序语法转换的高效通用无损数据压缩算法

在许多应用程序中，如压缩文本文件、Web页面文件和Java applet，存在一些关于要压缩的数据的先验知识，这些知识通常以上下文模型的形式出现。如何有效地利用上下文模型来提高压缩性能是一个具有挑战性的问题。我们通过扩展Yang和Kieffer的结果来解决这个问题。通知。理论，vol.IT-46, p.755- 88,2000)，特别是贪婪上下文自由顺序语法转换和相应的压缩算法，以上下文模型的情况。

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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Proceedings. 2001 IEEE International Symposium on Information Theory (IEEE Cat. No.01CH37252)

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