TR#289: Exaggerated Consensus in Lossless Image Compression

Kris Popat and Rosalind W. Picard

Article available in:
Proc. of IEEE First Int. Conf.
on Image Proc.,
Austin, TX, Vol. III,
November, 1994, pp.846-850.

Good probabilistic models are needed in data compression and many other applications. A good model must exploit contextual information, which requires high-order conditioning. As the number of conditioning variables increases, direct estimation of the distribution becomes exponentially more difficult. To circumvent this, we consider a means of adaptively combining several low-order conditional probability distributions into a single higher-order estimate, based on their degree of agreement. Though the technique is broadly applicable, image compression is singled out as a testing ground of its abilities. Good performance is demonstrated by experimental results.

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