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LC-TMNet: learned lossless medical image compression with tunable multi-scale network.

Hengrui Liao¹, Yue Li¹

¹School of Computer, University of South China, HengYang, HuNan, China.

Peerj. Computer Science

|February 3, 2025

Summary

This study introduces a new lossless image compression method using flexible tree-structured segmentation and attention mechanisms for improved probabilistic estimation in medical imaging. The approach enhances accuracy in complex regions and offers variable-speed compression options.

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Area of Science:

Medical Imaging
Computer Science
Data Compression

Background:

High-quality medical images are vital for accurate diagnosis, necessitating lossless compression to maintain image integrity.
Neural networks integrated with entropy encoders offer advanced lossless compression, outperforming traditional methods.
Existing neural network compression methods struggle with probabilistic estimation in complex or edge regions, limiting performance.

Purpose of the Study:

To develop a novel lossless image compression method that overcomes limitations in probabilistic estimation for complex image regions.
To improve the accuracy and efficiency of neural network-based lossless image compression for medical applications.

Main Methods:

A flexible tree-structured image segmentation mechanism was employed to leverage relationships between subimages.

Keywords:

Lossless compression Medical image Neural networks

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An attention mechanism was integrated into the UNet network architecture to enhance probabilistic estimation accuracy.

Variable-speed compression was implemented, offering distinct fast and slow modes.

Main Results:

The proposed method demonstrates improved probabilistic estimation, particularly in complex textured regions.
Variable-speed compression achieved state-of-the-art compression speed in fast mode.
The slow mode achieved state-of-the-art compression performance.

Conclusions:

The novel method effectively enhances lossless image compression accuracy and efficiency for medical imaging.
Flexible tree-structured segmentation and attention mechanisms are key to improving neural network-based compression.
The variable-speed compression offers practical adaptability for different clinical or research needs.