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.

翻译：随着现代显微镜和生物成像技术的快速发展，前所未有的海量成像数据被生成、存储、分析，甚至通过网络共享。这些数据的规模对当前数据基础设施构成严峻挑战。减少数据规模的常用方法之一是图像压缩。本研究分析了经典和基于深度学习的图像压缩方法，及其对基于深度学习的图像处理模型的影响。以基于深度学习的无标记预测模型（即从明场图像预测荧光图像）为例进行对比分析。有效的图像压缩方法能够在保留必要信息的同时显著减小数据规模，从而降低数据管理基础设施的负担，并允许通过网络快速传输以实现数据共享或云计算。为实现理想的图像压缩，我们比较了多种经典有损图像压缩技术与若干基于人工智能的压缩模型，这些模型由CompressAI工具箱提供并使用Python训练。我们在压缩比、多种图像相似度指标以及最重要的——无标记模型对压缩图像的预测精度方面，对这些不同压缩技术进行了比较。我们发现，基于人工智能的压缩技术显著优于经典方法，并且在二维情况下对下游无标记任务的影响极小。最后，我们希望本研究能够揭示基于深度学习的图像压缩潜力以及图像压缩对下游基于深度学习的图像分析模型的影响。