The study was based on the ImageNet 2012 1000-class dataset, specifically on 10,000 images drawn from the ILSVRC 2012 validation set. One of the methods of quality degradation was lossy image compression, using JPEG and JPEG2000. We show that our methodology can allow practitioners to establish an acceptable compression level for specific use cases hence, it can play a key role in applications that process and store very large image data.ĭodge and Karam investigated the influence of image quality on the performance of image classification, which is similar to object detection but without the localization requirement. Our experimental study, performed over a widely used object detection benchmark, assessed the robustness of nine popular object-detection deep models against varying compression characteristics. We focus on Joint Photographic Expert Group (JPEG) compression and thoroughly analyze a range of the performance metrics. In this paper, we investigate the impact of image compression on the performance of object detection methods based on convolutional neural networks. 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.
Video surveillance systems process high volumes of image data.
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