Variable resolution Markov modelling of signal data for image compression

Traditionally, Markov models have not been successfully used for compression of signal data other than binary image data. Due to the fact that exact substring matches in non-binary signal data are rare, using full resolution conditioning information generally tends to make Markov models learn slowly, yielding poor compression. However, as is shown in this paper, such models can be successfully applied to non-binary signal data compression by continually adjusting the resolution and order to minimize the code-length of the past samples in the hope that this choice will best compress the future samples as well, a technique inspired by Rissanen's Minimum Description Length (MDL) principle. Performance of this method meets or exceeds current approaches.