With the fast development of modern microscopes and bioimaging techniques, an
unprecedentedly large amount of imaging data are being generated, stored,
analyzed, and even shared through networks. The size of the data poses great
challenges for current data infrastructure. One common way to reduce the data
size is by image compression. This present study analyzes classic and deep
learning based image compression methods, and their impact on deep learning
based image processing models. Deep learning based label-free prediction models
(i.e., predicting fluorescent images from bright field images) are used as an
example application for comparison and analysis. Effective image compression
methods could help reduce the data size significantly without losing necessary
information, and therefore reduce the burden on data management infrastructure
and permit fast transmission through the network for data sharing or cloud
computing. To compress images in such a wanted way, multiple classical lossy
image compression techniques are compared to several AI-based compression
models provided by and trained with the CompressAI toolbox using python. These
different compression techniques are compared in compression ratio, multiple
image similarity measures and, most importantly, the prediction accuracy from
label-free models on compressed images. We found that AI-based compression
techniques largely outperform the classic ones and will minimally affect the
downstream label-free task in 2D cases. In the end, we hope the present study
could shed light on the potential of deep learning based image compression and
the impact of image compression on downstream deep learning based image
analysis models.

利用经典和深度学习的图像压缩方法对基于深度学习的图像处理模型进行分析并比较，表明深度学习的压缩技术在 2D 情况下在保持预测准确性的同时显著优于传统方法，并对图像压缩对下游深度学习图像分析模型的影响进行评估。