  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.
