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

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

4 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - DCC 2020
Subtitle of host publicationData Compression Conference
EditorsAli Bilgin, Michael W. Marcellin, Joan Serra-Sagrista, James A. Storer
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781728164571
Publication statusPublished - 2020 Mar
Event2020 Data Compression Conference, DCC 2020 - Snowbird, United States
Duration: 2020 Mar 242020 Mar 27

Publication series

NameData Compression Conference Proceedings
ISSN (Print)1068-0314


Conference2020 Data Compression Conference, DCC 2020
Country/TerritoryUnited States


  • Bayes code
  • Lossless image compression
  • Quadtree
  • Stochastic model

ASJC Scopus subject areas

  • Computer Networks and Communications


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