Autoregressive Image Generative Models with Normal and t-distributed Noise and the Bayes Codes for Them

研究成果: Conference contribution

抄録

In this paper, we propose an autoregressive stochastic generative model for images. This modelshould be one of the most basic models for the new type of lossless image compression which explicitly assume the stochastic generative model. We can easily expand it and theoretically interpret theimplicitly assumed stochastic generative models in the various previous predictive coding methods as the expanded versions of our model. Moreover, we can utilize the achievements in the related fields where the linear regression analysis and its expansion are studied to construct the Bayes codes for these generative models. As an example, we expand our generative model from the one with normalnoise to the one with the t-distributed noise. Then, we construct the sub-optimal Bayes codes for this generative model by utilizing the variational Bayesian method.

本文言語English
ホスト出版物のタイトルProceedings of 2020 International Symposium on Information Theory and its Applications, ISITA 2020
出版社Institute of Electrical and Electronics Engineers Inc.
ページ81-85
ページ数5
ISBN(電子版)9784885523304
出版ステータスPublished - 2020 10 24
イベント16th International Symposium on Information Theory and its Applications, ISITA 2020 - Virtual, Kapolei, United States
継続期間: 2020 10 242020 10 27

出版物シリーズ

名前Proceedings of 2020 International Symposium on Information Theory and its Applications, ISITA 2020

Conference

Conference16th International Symposium on Information Theory and its Applications, ISITA 2020
CountryUnited States
CityVirtual, Kapolei
Period20/10/2420/10/27

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems
  • Software
  • Theoretical Computer Science

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