Coding with partially hidden Markov models

Søren Forchhammer; J. Rissanen

doi:10.1109/DCC.1995.515499

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 language	English
Title of host publication	Proceedings of Data Compression Conference
Publisher	IEEE
Publication date	1995
Pages	92-101
ISBN (Print)	08-18-67012-6
DOIs	https://doi.org/10.1109/DCC.1995.515499
Publication status	Published - 1995
Event	Data Compression Conference 1995 - Snowbird, UT, United States Duration: 28 Mar 1995 → 30 Mar 1995 http://www.informatik.uni-trier.de/~ley/db/conf/dcc/dcc95.html

Conference

Conference	Data Compression Conference 1995
Country/Territory	United States
City	Snowbird, UT
Period	28/03/1995 → 30/03/1995
Internet address	http://www.informatik.uni-trier.de/~ley/db/conf/dcc/dcc95.html

Bibliographical note

Copyright: 1995 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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title = "Coding with partially hidden Markov models",

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",

author = "S{\o}ren Forchhammer and J. Rissanen",

note = "Copyright: 1995 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE; Data Compression Conference 1995, DCC '95 ; Conference date: 28-03-1995 Through 30-03-1995",

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AU - Forchhammer, Søren

AU - Rissanen, J.

N1 - Copyright: 1995 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

PY - 1995

Y1 - 1995

N2 - 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

AB - 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

U2 - 10.1109/DCC.1995.515499

DO - 10.1109/DCC.1995.515499

M3 - Article in proceedings

SN - 08-18-67012-6

SP - 92

EP - 101

BT - Proceedings of Data Compression Conference

PB - IEEE

T2 - Data Compression Conference 1995

Y2 - 28 March 1995 through 30 March 1995

ER -

Coding with partially hidden Markov models

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