Semantic redundancy-aware implicit neural compression for multidimensional biomedical image data.

ABSTRACT: The surge in advanced imaging techniques has generated vast biomedical image data with diverse dimensions in space, time and spectrum, posing big challenges to conventional compression techniques in image storage, transmission, and sharing. Here, we propose an intelligent image compression approach with the first-proved semantic redundancy of biomedical data in the implicit neural function domain. This Semantic redundancy based Implicit Neural Compression guided with Saliency map (SINCS) can notably improve the compression efficiency for arbitrary-dimensional image data in terms of compression ratio and fidelity. Moreover, with weight transfer and residual entropy coding strategies, it shows improved compression speed while maintaining high quality. SINCS yields high quality compression wi

SUBMITTER: Ma Y 

PROVIDER: S-EPMC11371832 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature