A Stochastic Model of Block Segmentation Based on the Quadtree and the Bayes Code for It

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 distri...

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Bibliographic Details
Published inDCC (Los Alamitos, Calif.) pp. 293 - 302
Main Authors Nakahara, Yuta, Matsushima, Toshiyasu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2020
Subjects
Bayes code
Codes
Computational efficiency
Computational modeling
Data compression
Data models
Image segmentation
Lossless image compression
Quadtree
Stochastic model
Stochastic processes
Online AccessGet full text
ISSN2375-0359
DOI10.1109/DCC47342.2020.00037

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Summary: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.
ISSN:2375-0359
DOI:10.1109/DCC47342.2020.00037