The rapid pace of innovation in biological microscopy imaging has led to large images, putting pressure on data storage and impeding efficient sharing, management, and visualization. This necessitates the development of efficient compression solutions. Traditional CODEC methods struggle to adapt to the diverse bioimaging data and often suffer from sub-optimal compression. In this study, we propose an adaptive compression workflow based on Implicit Neural Representation (INR). This approach permits application-specific compression objectives, capable of compressing images of any shape and arbitrary pixel-wise decompression. We demonstrated on a wide range of microscopy images from real applications that our workflow not only achieved high, controllable compression ratios (e.g., 512x) but also preserved detailed information critical for downstream analysis.

翻译：生物显微成像技术的快速发展导致图像数据量急剧增加，给数据存储带来压力，并阻碍了高效共享、管理和可视化。这迫切需要开发高效的压缩解决方案。传统的编解码器方法难以适应多样化的生物成像数据，且往往存在压缩效果欠佳的问题。在本研究中，我们提出了一种基于隐式神经表示的自适应压缩工作流程。该方法支持针对特定应用的压缩目标，能够压缩任意形状的图像并实现逐像素的任意解压缩。我们在真实应用场景中的多种显微图像上验证表明，该工作流程不仅能实现高且可控的压缩比（例如512倍），还能保留对下游分析至关重要的细节信息。