A new disparity map quality assessment based on structural similarity for remotely sensed image pairs. Issue 7 (2nd July 2020) - Jia, Qingling; Wan, Xue; Hei, Baoqin; Li, Shengyang
ABSTRACT: Disparity map quality assessment is crucial to evaluate the accuracies of stereo matching algorithms. Several widely used measures such as root mean square (RMS), mean absolute error (MAE) and bad matching pixels (BMP) have been proposed to evaluate the similarity between disparity map and ground truth (GT) map. These measures are based on grey-scale errors while ignoring the structural similarity between disparity map and GT. In this paper, we propose a united similarity measure based on sructure similarity and normalized RMS (USNR) for disparity map quality assessment. Our new measure is able to simultaneously evaluate grey-scale similarity and region-aware structural similarity between disparity map and GT map. Experiments have been carried out using stereo pair datasets from various scenes, including street view, planetary, natural landscape and urban, to demonstrate the feasibility, reliability and robustness of our measure compared to state-of-the-art evaluation methods.