Stereoscopic image quality metric based on local entropy and binocular just noticeable difference
Developing a metric that can reliably predict the perceptual 3D quality as perceived by the end user, is a challenging issue and a necessary tool for the success of 3D multimedia applications. The various attempts at predicting 3D quality of experience as the combination of 2D quality of the left and right images have shown their limitations, and particularly for the case of asymmetric distortions. In this paper we propose a full reference quality assessment metric for stereoscopic images based on the perceptual binocular characteristics. The proposed metric handles effectively the asymmetric distortions of stereoscopic images, by incorporating human visual system (HVS) characteristics. Our approach was motivated by the fact that in case of asymmetric quality, 3D perception mechanisms supports the view providing the most important and contrasted information. To achieve that, weighting factors are defined for the quality of each view according to the local information content. Add to that, to take into account the sensitivity of the HVS, quality score of each region are modulated based on the Binocular Just Noticeable Difference (BJND). Experimental results show that the proposed metric correlates better with human perception than the state-of-the-art metrics.