Deep learning based Image Compression for Microscopy Images: An Empirical Study

TL;DR

This study compares classical and deep learning image compression methods for microscopy images, demonstrating that AI-based techniques significantly reduce data size with minimal impact on downstream image analysis tasks.

Contribution

It provides an empirical comparison of classical and AI-based image compression methods, highlighting the superior performance of deep learning techniques for microscopy image compression.

Findings

AI-based compression outperforms classical methods in compression ratio

Deep learning compression minimally affects label-free prediction accuracy

Effective compression reduces data size and facilitates faster data sharing

Abstract

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.