Abstract

uses considerably greater computational resources (both time and space). The next section describes the basic PPM compression scheme. Following that we motivate the use of contexts of unbounded length, introduce the new method, and show how it can be implemented using a trie data structure. Then we give some results that demonstrate an improvement of about 6% over the old method. Finally, a recently-published and seemingly unrelated compression scheme [2] is related to the unbounded-context idea that forms the essential innovation of PPM*. 1 PPM: Prediction by partial match The basic idea of PPM is to use the last few characters in the input stream to predict the upcoming one. Models that condition their predictions on a few immediately preceding symbols are called "finite-context" models of order k, where k is the number of preceding symbols used. PPM employs a suite of fixed-order context models with different values of k

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