Bayes Code for 2-dimensional auto-regressive Hidden Markov model and its application to lossless image compression

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For general lossless data compression in information theory, researchers have repeated expansion of stochastic models to express target data and design of codes for the expanded models. In this paper, we apply this approach to lossless image compression. We expand an auto-regressive hidden Markov model to a 2-dimensional model to express images containing single diagonal edge. Then, we design a Bayes code with an approximative parameter estimation by variational Bayesian methods. Experimental results for synthetic images show that the proposed model is sufficiently flexible for the target images and the parameter estimation is accurate enough. We also confirm the behavior of the proposed method on real images.

Original languageEnglish
Title of host publicationInternational Workshop on Advanced Imaging Technology, IWAIT 2020
EditorsPhooi Yee Lau, Mohammad Shobri
PublisherSPIE
ISBN (Electronic)9781510638358
DOIs
Publication statusPublished - 2020
EventInternational Workshop on Advanced Imaging Technology, IWAIT 2020 - Yogyakarta, Indonesia
Duration: 2020 Jan 52020 Jan 7

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11515
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceInternational Workshop on Advanced Imaging Technology, IWAIT 2020
CountryIndonesia
CityYogyakarta
Period20/1/520/1/7

Keywords

  • Auto-regressive hidden Markov model
  • Bayes code
  • Generative model
  • Lossless image compression
  • Variational Bayesian methods

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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