No reference quality assessment for stereoscopic images by statistical features
Yuming Fang, Jiebin Yan, Jiheng Wang · 2017
In this paper, we propose a novel no reference (NR) quality assessment metric for stereoscopic images by statistical features. First, we calculate the luminance map through the local normalization, which is further used to extract the statistic luminance features. Second, we predict the disparity map of the stereoscopic image, which is further combined with the corresponding left and right views to extract the statistical structure and depth features for the stereoscopic image. The support vector regression (SVR) is employed as the mapping function from the quality-aware features to subjective quality scores. Experimental results on four publicly available large-scale stereoscopic image databases show that the proposed metric can obtain high-accuracy performance and is competitive with the state-of-the-art methods designed for visual quality prediction of stereoscopic images.