A stochastic model of block segmentation based on the quadtree and the bayes code for it

研究成果: Conference contribution

抄録

In this paper, we propose a novel stochastic model based on the quadtree, so that our model effectively represents the variable block size segmentation of images. Then, we construct the Bayes code for the proposed stochastic model. In general, the computational cost to calculate the posterior distribution required in the Bayes code increases exponentially with respect to the data size. However, we introduce an efficient algorithm to calculate it in the polynomial order of the data size without loss of the optimality. Some experiments are performed to confirm the flexibility of the proposed stochastic model and the efficiency of the introduced algorithm.

本文言語English
ホスト出版物のタイトルProceedings - DCC 2020
ホスト出版物のサブタイトルData Compression Conference
編集者Ali Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer
出版社Institute of Electrical and Electronics Engineers Inc.
ページ293-302
ページ数10
ISBN(電子版)9781728164571
DOI
出版ステータスPublished - 2020 3
イベント2020 Data Compression Conference, DCC 2020 - Snowbird, United States
継続期間: 2020 3 242020 3 27

出版物シリーズ

名前Data Compression Conference Proceedings
2020-March
ISSN(印刷版)1068-0314

Conference

Conference2020 Data Compression Conference, DCC 2020
CountryUnited States
CitySnowbird
Period20/3/2420/3/27

ASJC Scopus subject areas

  • Computer Networks and Communications

フィンガープリント 「A stochastic model of block segmentation based on the quadtree and the bayes code for it」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル