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

Yuta Nakahara*, Toshiyasu Matsushima

*この研究の対応する著者

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルInternational Workshop on Advanced Imaging Technology, IWAIT 2020
編集者Phooi Yee Lau, Mohammad Shobri
出版社SPIE
ISBN(電子版)9781510638358
DOI
出版ステータスPublished - 2020
イベントInternational Workshop on Advanced Imaging Technology, IWAIT 2020 - Yogyakarta, Indonesia
継続期間: 2020 1 52020 1 7

出版物シリーズ

名前Proceedings of SPIE - The International Society for Optical Engineering
11515
ISSN(印刷版)0277-786X
ISSN(電子版)1996-756X

Conference

ConferenceInternational Workshop on Advanced Imaging Technology, IWAIT 2020
国/地域Indonesia
CityYogyakarta
Period20/1/520/1/7

ASJC Scopus subject areas

  • 電子材料、光学材料、および磁性材料
  • 凝縮系物理学
  • コンピュータ サイエンスの応用
  • 応用数学
  • 電子工学および電気工学

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