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1995
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Variable resolution Markov modelling of signal data for image compression

10 years 5 months ago
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 codelength 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.
Mark Trumbo, Jacques Vaisey
Added 29 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 1995
Where ICIP
Authors Mark Trumbo, Jacques Vaisey
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