No-reference stereo image quality assessment by learning gradient dictionary-based color visual characteristics

In this paper, we propose a no-reference (NR) stereo image quality assessment metric by learning gradient dictionary-based color visual characteristics. To be specific, firstly, since human eyes are highly sensitive to the structure of images, the gradient magnitude (GM) and gradient orientation (GO) are extracted from left and right views of stereo image, meanwhile, the difference map is obtained. Considering the influence of color distortion, images are decomposed into RGB channels to be processed respectively, and we get the local gradient of the color image by adding up the RGB gradient vectors. Constructively, the gradient dictionary is generated, which is different from traditional image dictionary. All quality-aware features are extracted by joint sparse representation. Afterwards, to avoid over-fitting, the principal component analysis (PCA) is applied to optimize the quality-aware features. Finally, all features are fed into the trained support vector regression (SVR) model to predict the objective score. The experimental results show that the proposed metric always achieves high consistency with human subjective assessment for both symmetric and asymmetric distortions.