Using distortion and asymmetry determination for blind stereoscopic image quality assessment strategy

Predicting the perceived quality of stereoscopic 3D images is a challenging task, especially when the stereo-pair is asymmetrically distorted. Despite the considerable efforts to fix this issue, there is no commonly accepted metric. Most of the attempts consisted in developing full reference quality metrics, while very few efforts have been dedicated to blind/no-reference (NR) quality assessment of stereoscopic images. In this paper, we propose a blind/NR quality assessment strategy for stereoscopic images based on the identification of the distortion type in order to select the most efficient impairment measure in addition to the determination of whether a stereo-pair is symmetrically or asymmetrically distorted to account for the binocular fusion properties. Finally, the last step combines the two key information derived from previous steps and estimates the 3D image quality appropriately using different binocular combination strategies. Experimental results on four publicly available 3D image quality assessment databases showed that the proposed strategy reaches significant prediction consistency and accuracy when compared to state-of-the-art metrics.