Prediction by Partial Matching (PPM) is an adaptive statistical data compression technique based on context modeling and prediction. PPM models use a set of previous symbols in the uncompressed symbol stream to predict the next symbol in the stream.
Predictions are usually reduced to symbol rankings. The number of previous symbols, n, determines the order of the PPM model which is denoted as PPM(n). Unbounded variants where the context has no len...
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Prediction by Partial Matching (PPM) is an adaptive statistical data compression technique based on context modeling and prediction. PPM models use a set of previous symbols in the uncompressed symbol stream to predict the next symbol in the stream.
Predictions are usually reduced to symbol rankings. The number of previous symbols, n, determines the order of the PPM model which is denoted as PPM(n). Unbounded variants where the context has no length limitations also exist and are denoted as PPM*. If no prediction can be made based on all n context symbols a prediction is attempted with n-1 symbols. This process is repeated until a match is found or no more symbols remain in context. At that point a fixed prediction is made. This process is complementary to that followed by Dynamic Markov Compression (DMC) which builds up from a zero-order model.
Much of the work in optimizing a PPM model is handling inputs that have not already occurred in the input stream. The obvious way to handle...
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