Compression by model combination

Abstract

In the probabilistic framework for data compression, a model of the probability distribution of a data source is constructed, and the predicted probability is entropy coded. To achieve better compression, most traditional methods resort to higher order models. However, this approach is limited by memory and often suffers from the context dilution problem. In this paper, we present methods that allow us to combine a few low order models to achieve equivalent or better compression of a high order model. We show that when applying our techniques to bi-level images, we are able to achieve the state of the art compression within the probabilistic framework.