Data compression: methods and theory
Data compression: methods and theory
Sending compressed messages to a learned receiver on a bidirectional line
Information Processing Letters
Efficient randomized pattern-matching algorithms
IBM Journal of Research and Development - Mathematics and computing
SFCS '84 Proceedings of the 25th Annual Symposium onFoundations of Computer Science, 1984
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State of the art lossless data compressors are very efficient. While it is not possible to prove that they always achieve their theoretical limit (i.e. the source entropy), their effective performances for specific data types are often very close to this limit. If we have already compressed a large number of source messages in the past, then we can use this previous knowledge of the source to increase the compression of the current message and we can design algorithms that efficiently compress and decompress given this previous knowledge. By doing this in the fundamental source coding theorem we substitute entropy with conditional entropy and we have a new theoretical limit that allows for better compression. Moreover, if we assume the possibility of interaction between the compressor and the decompressor then we can exploit the previous knowledge they have of the source. The price we might pay is a very low possibility of communication errors. In this paper we study interactive data compression and present experimental results on the interactive compression of textual data.