No-reference stereoscopic images quality assessment method based on monocular superpixel visual features and binocular visual features

Abstract

No-reference quality assessment of images has received considerable attention. However, the accuracy of such assessment remains questionable because of its weak biological basis. In this paper, we propose a novel quality assessment model based on the superpixel index and biological binocular mechanisms. The technical contributions of our model are the introduction of local monocular superpixel features and three global binocular visual features. We utilize monocular superpixel segmentation to extract two types of entropies as the local visual features for accurate quality-aware feature extraction. In addition, natural scene statistics features are extracted from the binocular visual information to complement the local monocular features and quantify the naturalness of the stereoscopic images. Finally, a regression model is learned to evaluate the quality of the stereoscopic images. Experimental results from three popular databases demonstrate that the proposed model has a more reliable performance than earlier models in terms of prediction accuracy and generalizability.