First Gradually, Then Suddenly: Understanding the Impact of Image Compression on Object Detection Using Deep Learning.

ABSTRACT: Video surveillance systems process high volumes of image data. To enable long-term retention of recorded images and because of the data transfer limitations in geographically distributed systems, lossy compression is commonly applied to images prior to processing, but this causes a deterioration in image quality due to the removal of potentially important image details. In this paper, we investigate the impact of image compression on the performance of object detection methods based on convolutional neural networks. We focus on Joint Photographic Expert Group (JPEG) compression and thoroughly analyze a range of the performance metrics. Our experimental study, performed over a widely used object detection benchmark, assessed the robustness of nine popular object-detection deep models agains

SUBMITTER: Gandor T 

PROVIDER: S-EPMC8840351 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature