Bayes code for two-dimensional auto-regressive hidden Markov model and its application to lossless image compression

Yuta Nakahara; Toshiyasu Matsushima

doi:10.1117/12.2566943

1 June 2020 Bayes code for two-dimensional auto-regressive hidden Markov model and its application to lossless image compression

Yuta Nakahara, Toshiyasu Matsushima

Author Affiliations +

Proceedings Volume 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020; 115151T (2020) https://doi.org/10.1117/12.2566943
Event: International Workshop on Advanced Imaging Technologies 2020 (IWAIT 2020), 2020, Yogyakarta, Indonesia

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.

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Yuta Nakahara and Toshiyasu Matsushima "Bayes code for two-dimensional auto-regressive hidden Markov model and its application to lossless image compression", Proc. SPIE 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020, 115151T (1 June 2020); https://doi.org/10.1117/12.2566943

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