Sparse representation based stereoscopic image quality assessment accounting for perceptual cognitive process

A full reference quality assessment method with fused monocular and binocular features for stereo images

Aiming to automatically monitor and improve stereoscopic image and video processing systems, stereoscopic image quality assessment approaches are becoming more and more important as 3D technology gains popularity. We propose a full-reference stereoscopic image quality assessment method that incorporate monocular and binocular features based on binocular competition and binocular integration. To start, we create a three-channel RGB fused view by fusing Gabor filter bank responses and disparity maps. Then, using the monocular view and the RGB fusion view, respectively, we extract monocular and binocular features. To alter the local features in the binocular features, we simultaneously estimate the saliency of the RGB fusion image. Finally, the monocular and binocular quality scores are calculated based on the monocular and binocular features, and the quality scores of the stereo image prediction are obtained by fusion. Performance testing in the LIVE 3D IQA database Phase I and Phase II. The results of the proposed method are compared with newer methods. The experimental results show good consistency and robustness.