Word-Based Statistical Compressors as Natural Language Compression Boosters

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 compre...

• Type: Conference
• Year: 2008
• Where: DCC
• Authors: Antonio Fariña, Gonzalo Navarro, José R. Paramá
• Keywords: Better Compression Ratios, Computer Graphics, DCC 2008, Semistatic Word-based Compression, Word-based Byte-oriented Compression