LFC-UNet: learned lossless medical image fast compression with U-Net.

ABSTRACT: In the field of medicine, the rapid advancement of medical technology has significantly increased the speed of medical image generation, compelling us to seek efficient methods for image compression. Neural networks, owing to their outstanding image estimation capabilities, have provided new avenues for lossless compression. In recent years, learning-based lossless image compression methods, combining neural network predictions with residuals, have achieved performance comparable to traditional non-learning algorithms. However, existing methods have not taken into account that residuals often concentrate excessively, hindering the neural network's ability to learn accurate residual probability estimation. To address this issue, this study employs a weighted cross-entropy method to handle t

SUBMITTER: Liao H 

PROVIDER: S-EPMC10909184 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature