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Updated: Aug 30, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
Published on: July 5, 2024
Stochastic Model of Block Segmentation Based on Improper Quadtree and Optimal Code under the Bayes Criterion.
Yuta Nakahara1, Toshiyasu Matsushima2
1Center for Data Science, Waseda University, 1-6-1 Nisniwaseda, Shinjuku-ku, Tokyo 169-8050, Japan.
This study introduces a novel stochastic model for lossless image compression using improper quadtrees. Our method achieves theoretical limits with efficient polynomial-time computation, improving upon existing techniques.
Area of Science:
- Computer Science
- Information Theory
- Image Processing
Background:
- Previous lossless image compression research primarily focused on preprocessing.
- Stochastic generative models directly on pixel values offer a path to theoretical limits.
Purpose of the Study:
- To propose a novel stochastic generative model for lossless image compression.
- To achieve the theoretical compression limit for the proposed model.
- To develop an efficient algorithm for optimal coding.
Main Methods:
- Developed a stochastic model based on improper quadtrees.
- Theoretically derived the Bayes-optimal code for the model.
- Proposed a polynomial-time algorithm for optimal coding by assuming a novel prior distribution.
Main Results:
- The proposed improper quadtree model enables achieving theoretical compression limits.
- The developed algorithm computes Bayes-optimal codes in polynomial time, overcoming exponential complexity.
- The novel prior distribution is key to the algorithm's efficiency and optimality.
Conclusions:
- The improper quadtree model is a promising approach for advanced lossless image compression.
- Efficient computation of Bayes-optimal codes is achievable, advancing the field.
- This work sets a new benchmark for lossless image compression algorithms.
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