The Community for Technology Leaders
Data Compression Conference (2008)
Mar. 25, 2008 to Mar. 27, 2008
ISSN: 1068-0314
ISBN: 978-0-7695-3121-2
pp: 162-171
Semistatic word-based byte-oriented compression codes are known to be attractive alternatives to compress natural language texts. With compression ratios around 30%, they allow direct pattern searching on the compressed text up to 8 times faster than on its uncompressed version. In this paper we reveal that these compressors have even more benefits. We show that most of the state-of-the-art compressors such as the block-wise bzip2, those from the Ziv-Lempel family, and the predictive ppm-based ones, can benefit from compressing not the original text, but its compressed representation obtained by a word-based byte-oriented statistical compressor.??In particular, our experimental results show that using Dense-Code-based compression as a preprocessing step to classical compressors like bzip2, gzip, or ppmdi, yields several important benefits. For example, the ppm family is known for achieving the best compression ratios. With a Dense coding preprocessing, ppmdi achieves even better compression ratios (the best we know of on natural language) and much faster compression/decompression than ppmdi alone.??Text indexing also profits from our preprocessing step. A compressed self-index achieves much better space and time performance when preceded by a semistatic word-based compression step. We show, for example, that the AF-FMindex coupled with Tagged Huffman coding is an attractive alternative index for natural language texts.
Text compression, compression boosting, indexing

A. Fari?, J. R. Param? and G. Navarro, "Word-Based Statistical Compressors as Natural Language Compression Boosters," 2008 Data Compression Conference(DCC), Snowbird, UT, 2008, pp. 162-171.
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