Coding with partially hidden Markov models

Søren Forchhammer, J. Rissanen

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Abstract

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
PublisherIEEE
Publication date1995
Pages92-101
ISBN (Print)08-18-67012-6
DOIs
Publication statusPublished - 1995
EventData Compression Conference 1995 - Snowbird, UT, United States
Duration: 28 Mar 199530 Mar 1995
http://www.informatik.uni-trier.de/~ley/db/conf/dcc/dcc95.html

Conference

ConferenceData Compression Conference 1995
CountryUnited States
CitySnowbird, UT
Period28/03/199530/03/1995
Internet address

Bibliographical note

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