www.data-compression.infoThe Data Compression Resource on the Internet The process of entropy coding can be split in two parts: modeling and coding. Modeling assigns probabilities to the symbols, and coding produces a bit sequence from these probabilities. As established in Shannon's source coding theorem, there is a relationship between the symbol probability and it's corresponding bit sequence. A symbol with probabilitiy p gets a bit sequence of length -log(p).
In order to achieve a good compression rate, an exact propability estimation is needed. Since the model is responsible for the probability of each symbol, modeling is one the most important tasks in data compression.

An adaptive model is a model, which changes the symbol probabilities during the compression process in order to adapt to the changing contexts during the process. Initially the compression process starts with a basic model, so the model doesn't need to be transmitted. During the process, the model gets adapted by the symbols, which have been transmitted already. It is important that the model gets adapted only by the symbols, which have been transmitted already, since the decoder needs to be able to adapt the model in the same way later at the decompression process. Since the decoder knows the before transmitted symbols, he can adapt the model in the same way than the coder did.

An interesting and nice overview concerning different techniques for using compression in cache and main memory systems is presented in a paper of Sparsh Mittal and Jeffrey Vetter.

 Publications
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