HP Labs Technical Reports

Sequential Prediction and Ranking in Universal Context Modeling and Data Compression

Weinberger, Marcelo; Seroussi, Gadiel

HPL-94-111
December 06, 1994
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Abstract: We investigate the use of prediction as a means of reducing the model cost in lossless data compression. We provide a formal justification to the combination of this widely accepted tool with a universal code based on context modeling, by showing that a combined scheme may result in faster convergence rate to the source entropy. In deriving the main result, we develop the concept of sequential ranking, which can be seen as a generalization of sequential prediction, and we study its combinatorial and probabillistic properties.

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