This paper focuses on sharpness mismatch between stereoscopic views.An automatic metric is proposed to assess sharpness mismatch artifacts.The proposed metric outperforms the sota metrics on sharpness mismatch artifacts.A subjective test was carried out based on natural stereo 3D videos. This paper analyzes sharpness mismatch between stereoscopic views. Sharpness mismatch is a special binocular mismatch and can occur through e.g. focus mismatch between stereoscopic cameras, errors in post-processing or asymmetric coding for low-bandwidth transmission, where one view is subsampled or transmitted at a much lower rate. Although blurred edges in one view can be suppressed by the corresponding sharper edges in the other view according to the binocular suppression phenomenon, sharpness mismatch can still be perceived and cause eye strain for viewers. Subjective studies were carried out with a test video dataset, in which the stereoscopic views are asymmetrically blurred by Gaussian low-pass filters since defocus-based effects of lens aberrations can be modeled as Gaussian blur. Also, an efficient novel automatic no-reference approach to measure the probability of sharpness mismatch is presented in this paper. The sharpness mismatch score is estimated by measuring width deviations of edge pairs in each "edge-significant" depth plane based on depth edges in both views. The probability of sharpness mismatch (PSM) is then calculated considering the perceptibility of edge width deviations considering absolute depth at which the edges occur. This PSM metric is evaluated using the test video dataset and blurriness dataset of LIVE 3D Phase II database. The experimental results show that the proposed metric outperforms the state-of-the-art stereo 3D quality metrics on analyzing sharpness mismatch between stereoscopic views.
['Mohan Liu', 'Karsten Muller', 'Alexander Raake']
Efficient no-reference metric for sharpness mismatch artifact between stereoscopic views