Computational evaluation demonstrates controllable high-ratio compression across diverse microscopy datasets, indicating scalable storage solutions for biological research.
The rapid pace of innovation in biological microscopy has produced increasingly large images, putting pressure on data storage and impeding efficient data sharing, management and visualization. This trend necessitates new, efficient compression solutions, as traditional coder-decoder methods often struggle with the diversity of bioimages, leading to suboptimal results. Here we show an adaptive compression workflow based on implicit neural representation that addresses these challenges. Our approach enables application-specific compression, supports images of varying dimensionality and allows arbitrary pixel-wise decompression. On a wide range of real-world microscopy images, we demonstrate that our workflow achieves high, controllable compression ratios while preserving the critical details necessary for downstream scientific analysis.
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Dai et al. (2025) studied this question.
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