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

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

Abstract

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.

Original languageEnglish
Title of host publicationProceedings of 2020 International Symposium on Information Theory and its Applications, ISITA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages81-85
Number of pages5
ISBN (Electronic)9784885523304
Publication statusPublished - 2020 Oct 24
Event16th International Symposium on Information Theory and its Applications, ISITA 2020 - Virtual, Kapolei, United States
Duration: 2020 Oct 242020 Oct 27

Publication series

NameProceedings 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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