Variable to fixed-length codes for Markov Sources
IEEE Transactions on Information Theory
Variable to Fixed Length Codes for Predictable Sources
DCC '98 Proceedings of the Conference on Data Compression
Generalized Tunstall codes for sources with memory
IEEE Transactions on Information Theory
Variable-to-fixed length codes and the conservation of entropy
IEEE Transactions on Information Theory
Bytecode compression via profiled grammar rewriting
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
Efficient implementation of the generalized Tunstall code generation algorithm
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
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An n-state Markov model for symbol occurrences is extended to an equivalent source for variable length strings of symbols in a dictionary at every state i, which are to be encoded with the string index in the dictionary. The algorithm for building the n dictionaries optimizes the rate subject to a given total number of entries in the dictionaries, and it is practical even for Markov sources with thousand states.The speed of the algorithm stems from encoding by table look-ups of the strings instead of single symbols. For this the n dictionaries need be known both to the encoder and the decoder. A static version of the algorithm is very well suited for creation of compressed files with random access. An adaptive version is shown to be faster than the methods in the PPM class, while providing only slightly lower compression ratios.