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3D video is expected to provide an enhanced user experience by using the impression of depth to bring greater realism to the user. Quality assessment plays an important role in the design and optimization of 3D video processing systems. In this paper, a new 3D image quality model that is specifically tailored for mobile 3D video is proposed. The model adopts three quality components, called the cyclopean view, binocular rivalry, and the scene geometry, in which the quality must be quantified. The cyclopean view formation process is simulated and its quality is evaluated using the three proposed approaches. Binocular rivalry is quantified over the distorted stereo pairs, and the scene quality is quantified over the disparity map. Based on the model, the 3D image quality can then be assessed using state-of-the-art 2D quality measures selected appropriately through a machine learning approach. To make the metric simple, fast, and efficient, final selection of the quality features is accomplished by also considering the computational complexity and the CPU running time. The metric is compared with several currently available 2D and 3D metrics. Experimental results show that the compound metric gives a significantly high correlation with the mean opinion scores that were collected through large-scale subjective tests run on mobile 3D video content.

Quantifying the importance of cyclopean view and binocular rivalry-related features for objective quality assessment of mobile 3D video