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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    Title :

    Exaggerated consensus in lossless image compression


    Contributors:
    Popat, K. (author) / Picard, R.W. (author)


    Publication date :

    1994-01-01


    Size :

    545598 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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