Text compression
A Space-Economical Suffix Tree Construction Algorithm
Journal of the ACM (JACM)
Linear Algorithm for Data Compression via String Matching
Journal of the ACM (JACM)
Universal Lossless Source Coding with the Burrows Wheeler Transform
DCC '99 Proceedings of the Conference on Data Compression
Unbounded length contexts for PPM
DCC '95 Proceedings of the Conference on Data Compression
IBM Journal of Research and Development
Word-based block-sorting text compression
ACSC '01 Proceedings of the 24th Australasian conference on Computer science
Word-based text compression using the Burrows-Wheeler transform
Information Processing and Management: an International Journal
Segmentation-based multilayer diagnosis lossless medical image compression
VIP '05 Proceedings of the Pan-Sydney area workshop on Visual information processing
Energy-aware lossless data compression
ACM Transactions on Computer Systems (TOCS)
Word-based text compression using the Burrows-Wheeler transform
Information Processing and Management: an International Journal
Revisiting bounded context block-sorting transformations
Software—Practice & Experience
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This work combines a new fast context-search algorithm with the lossless source coding models of PPM to achieve a lossless data compression algorithm with the linear context-search complexity and memory of BWT and Ziv-Lempel codes and the compression performance of PPM-based algorithms. Both sequential and non-sequential encoding are considered. The proposed algorithm yields an average rate of 2.27 bits per character (bpc) on the Calgary corpus, comparing favorably to the 2.33 and 2.34 bpc of PPM5 and PPM* and the 2.43 bpc of BW94 but not matching the 2.12 bpc of PPMZ9, which, at the time of this publication, gives the greatest compression of all algorithms reported on the Calgary corpus results page. The proposed algorithm gives an average rate of 2.14 bpc on the Canterbury corpus. The Canterbury corpus web page gives average rates of 1.99 bpc for PPMZ9, 2.11 bpc for PPM5, 2.15 bpc for PPM7, and 2.23 bpc for BZIP2 (a BWT-based code) on the same data set.