Joint image compression and classification with vector quantization and a two dimensional hidden Markov model

Jia Li, Robert M. Gray, Richard Olshen

Research output: Contribution to journalConference articlepeer-review

13 Scopus citations

Abstract

We present an algorithm to achieve good compression and classification for images using vector quantization and a two dimensional hidden Markov model. The feature vectors of image blocks are assumed to be generated by a two dimensional hidden Markov model. We first estimate the parameters of the model, then design a vector quantizer to minimize a weighted sum of compression distortion and classification risk, the latter being defined as the negative of the maximum log likelihood of states and feature vectors. The algorithm is tested on both synthetic data and real image data. The extension to joint progressive compression and classification is discussed.

Original languageEnglish (US)
Pages (from-to)23-32
Number of pages10
JournalData Compression Conference Proceedings
StatePublished - Jan 1 1999
EventProceedings of the 1999 Data Compression Conference, DCC-99 - Snowbird, UT, USA
Duration: Mar 29 1999Mar 31 1999

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

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