Coding with partially hidden Markov models

Søren Forchhammer, J. Rissanen

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    Partially hidden Markov models (PHMM) are introduced. They are a variation of the hidden Markov models (HMM) combining the power of explicit conditioning on past observations and the power of using hidden states. (P)HMM may be combined with arithmetic coding for lossless data compression. A general 2-part coding scheme for given model order but unknown parameters based on PHMM is presented. A forward-backward reestimation of parameters with a redefined backward variable is given for these models and used for estimating the unknown parameters. Proof of convergence of this reestimation is given. The PHMM structure and the conditions of the convergence proof allows for application of the PHMM to image coding. Relations between the PHMM and hidden Markov models (HMM) are treated. Results of coding bi-level images with the PHMM coding scheme is given. The results indicate that the PHMM can adapt to instationarities in the images
    Original languageEnglish
    Title of host publicationProceedings of Data Compression Conference
    Publication date1995
    ISBN (Print)08-18-67012-6
    Publication statusPublished - 1995
    EventData Compression Conference 1995 - Snowbird, UT, United States
    Duration: 28 Mar 199530 Mar 1995


    ConferenceData Compression Conference 1995
    Country/TerritoryUnited States
    CitySnowbird, UT
    Internet address

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