Image Quality Assessment using Deep Features for Object Detection

Applications such as video surveillance and self-driving cars produce large amounts of video data. Computer vision algorithms such as object detection have found a natural place in these scenarios. The reliability of these algorithms is usually benchmarked using curated datasets. However, one of the core challenges of working with computer vision data is variability. Compression is one such parameter that introduces artifacts (variability) in the data and can negatively affect performance. In this paper, we study the effect of compression on CNN-based object detectors and propose a new full-reference image quality metric based on Discrete Cosine Transform (DCT) to quantify the quality of an image for CNN-based object detectors. We compare this metric with commonly used image quality metrics, and the results show that the proposed metric correlates better with object detection performance. Furthermore, we train a regression model to estimate the quality of images for object detection.