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

• URL: http://arxiv.org/abs/2311.01352v2
• Date: Tue, 16 Jan 2024 11:32:48 GMT
• Title: Deep learning based Image Compression for Microscopy Images: An Empirical Study
• Authors: Yu Zhou, Jan Sollmann, Jianxu Chen
• Abstract summary: This study analyzes classic and deep learning based image compression methods, and their impact on deep learning based image processing models. To compress images in such a wanted way, multiple classical lossy image compression techniques are compared to several AI-based compression models. We found that AI-based compression techniques largely outperform the classic ones and will minimally affect the downstream label-free task in 2D cases.
• Score: 3.915183869199319
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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