On The Design Of A Novel Jpeg Quantization Table For Improved Feature Detection Performance

Keypoint or interest point detection is the first step in many computer vision algorithms. The detection performance of the state-of-the-art detectors is, however, strongly influenced by compression artifacts, especially at low bit rates. In this paper, we design a novel quantization table for the widely-used JPEG compression standard which leads to improved feature detection performance. After analyzing several popular scale-space based detectors, we propose a novel quantization table which is based on the observed impact of scale-space processing on the DCT basis functions. Experimental results show that the novel quantization table outperforms the JPEG default quantization table in terms of feature repeatability, number of correspondences, matching score, and number of correct matches.