A new challenge for compression algorithms: genetic sequences
Information Processing and Management: an International Journal - Special issue: data compression
Arithmetic coding for data compression
Communications of the ACM
The entropy of English using PPM-based models
DCC '96 Proceedings of the Conference on Data Compression
Context-Tree Weighting Method for Text Generating Sources
DCC '97 Proceedings of the Conference on Data Compression
Text Compression by Context Tree Weighting
DCC '97 Proceedings of the Conference on Data Compression
Multialphabet Coding with Separate Alphabet Description
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
The Design and Analysis of Efficient Lossless Data Compression Systems
The Design and Analysis of Efficient Lossless Data Compression Systems
Switching Between Two Universal Source Coding Algorithms
DCC '98 Proceedings of the Conference on Data Compression
The context-tree weighting method: basic properties
IEEE Transactions on Information Theory
Data Compression Method Combining Properties of PPM and CTW
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Prototyping of Efficient Hardware Algorithms for Data Compression in Future Communication Systems
RSP '01 Proceedings of the 12th International Workshop on Rapid System Prototyping
Superior Guarantees for Sequential Prediction and Lossless Compression via Alphabet Decomposition
The Journal of Machine Learning Research
On prediction using variable order Markov models
Journal of Artificial Intelligence Research
Non-repetitive DNA sequence compression using memoization
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
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Context tree weighting method is a universal compression algorithm for FSMX sources. Though we expect that it will have good compression ratio in practice, it is difficult to implement it and in many cases the implementation is only for estimating compression ratio. Though Willems and Tjalkens showed practical implementation using not block probabilities but conditional probabilities, it is used for only binary alphabet sequences. We extend the method for multi-alphabet sequences and show a simple implementation using PPM techniques. We also propose a method to optimize a parameter of the context tree weighting for binary alphabet case. Experimental results on texts and DNA sequences show that the performance of PPM can be improved by combining the context tree weighting and that DNA sequences can be compressed in less than 2.0 bpc.