Context Region Identification Based Quality Assessment of 3D Synthesized Views

Perceptual quality assessment of 3D synthesized views is an open research problem in computer vision. Researchers across the globe have developed several algorithms to identify distortions. At the same time, the existing algorithms cannot quantify the context in which these distortions affect the overall perceptual quality. According to the recently proposed 3D view synthesis algorithm, the choice of context region for the disocclusion plays a vital role in predicting the quality of 3D views. The context region taken from the background of a view produces a perceptually better quality of 3D synthesized views than when the context region is taken from the foreground. With this view, the proposed algorithm aims to identify the context region and incorporate this information for the perceptual quality assessment of 3D synthesized views. We observed that the depth energy maps of the 3D synthesized views vary significantly with the change in the context region and subsequently can identify the context region. Hence, in this work, we propose a new and efficient quality assessment algorithm based upon the variation in the depth of 3D synthesized and reference views, giving two-fold advantages: 1. It can predict the quality based on whether the context region is foreground or not. 2. It is also able to suggest the possible location of distortions. We have proposed two new algorithms for both situations when the context region is foreground or not. The overall predicted score is the direct multiplication of the quality score estimated when the context region is foreground or not. When applied to the established benchmark dataset, the proposed technique performs satisfactorily with the PLCC of 0.7707 and 0.7572 of SRCC. Also, the proposed algorithm can work as a plug-in to improve the performance of the existing algorithms.