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| Title | An Efficient Bayes Coding Algorithm for the Source Based on Context Tree Models that Vary from Section to Section (in Japanese) |
|---|---|
| Authors | Koshi Shimada 、Shota Saito 、Toshiyasu Matsushima |
| Released Year | 2020 |
| Format | Conference |
| Category | Source coding |
| Jounal Name | |
| Jounal Page | vol.120, no.410, IT2020-115, pp.19-24 |
| Published Year | 2021 |
| Published Month | 3 |
| Abstract （English） |
In this paper, we present an efficient coding algorithm for a non-stationary source based on context tree models that very from section to section. The context tree model is the extension of Markov models, and it represents a wide source model class that each symbol's generation depends on past sequences. The source model we present is an extension of context tree models. The calculation of the Bayes codes for it needs weighted mean over all context tree models and all changing patterns of the models; hence, we provide an approach of reducing the amount of the calculation. |
| Note （English） |
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Involved Papers
- Asymptotic property of universal lossless coding for independent piecewise identically distributed sources
- Bayes Universal Source Coding Scheme for Correlated Sources
- An Efficient Bayes Coding Algorithm using a New Unlimited Depth Context Tree
- A study of interactive source coding of correlated sources (in Japanese)
- Asymptotic Property on Nonstationary Sources with Piecewise Constant Parameters (in Japanese)
- Asymptotic Property of Universal Lossless Coding for Independent Piecewise Identically Distributed Sources
