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Depth-image-based rendering (DIBR), as the most popular view synthesis method, is commonly used in the application of multi-view and free-viewpoint videos. However, the quality evaluation of DIBR-synthesised videos remains largely unexplored, which may hinder the development of more advanced view synthesis technology. With this motivation, this Letter presents a new quality metric for DIBR-synthesised videos. Specifically, the disoccluded regions are first detected based on an adaptive threshold to quantify geometric distortions. An energy-based sequence mapping strategy is proposed to portray spatiotemporal inconsistency by calculating first-order and second-order similarities in the gradient magnitude domain and the Laplace-of-Gaussian domain, respectively. Finally, the overall quality score is generated by pooling the scores of geometric distortion and spatiotemporal inconsistency. Experimental results demonstrate that the proposed metric outperforms the state-of-the-art metrics dedicated to DIBR-synthesised images and videos. [ABSTRACT FROM AUTHOR]

A quality prediction method for stereoscopic images is proposed based on joint structure–texture sparse coding. The goal is to predict the perceptual quality of a stereoscopic image by solving the joint structure–texture sparse coding problem. First, structure and texture dictionaries from a training database are learnt. Then, the quality score for a testing stereoscopic image is predicted by computing left and right sparse feature similarity indexes, respectively, and combining them together. Experimental results on two 3D image‐quality assessment databases demonstrate that the proposed method can achieve high consistent alignment with subjective assessment. [ABSTRACT FROM AUTHOR]