No-reference stereoscopic image quality evaluator based on human visual characteristics and relative gradient orientation

Abstract

Stereoscopic image quality assessment (SIQA) is of great significance to the development of modern three-dimensional (3D) display technology. In this work, by further mining the relationship between visual features and stereoscopic image quality perception, we build a new no-reference SIQA model, which combines the monocular and binocular features. Statistical quality-aware structural features from relative gradient orientation (RGO) map and texture features from the histogram of the weighted local binary pattern (LBP) in the texture image (TLBP) are not only extracted from both monocular view, but also extracted from binocular views to predict binocular quality perception. Meanwhile, the color statistical features ignored by most models and the binocularity feature is extracted to complement the monocular features and the above binocular features, respectively. Finally, all the extracted features and subjective scores are used to predict the objective quality score through the support vector regression (SVR) model. Experiments on four popular stereoscopic image databases show that the proposed model achieves high consistency with subjective assessment, and the performance of the model is very competitive with the latest models.