No-reference quality assessment of 3D videos based on human visual perception

Broadcasting of high definition stereoscopic 3D videos is growing rapidly because of greater demand in the mass consumer market. In spite of increasing consumer interest, poor quality, crosstalk or side effects and visual quality degradation due to packet loss during transmission has hampered the advancement of 3D visualization. The quality assessment of distorted 3D video is a crucial element in designing and arranging advanced immersive media distribution platforms. A widely accepted no-reference quality metric to measure 3D video considering the human visual system (HVS) is yet to be developed. In this paper we have proposed a quality assessment (QA) criterion that can be measured without the original video. At first, we proposed a disparity index, that is measured by region based similarity matching and then edge magnitude difference is detected for visually significant areas of the image. Finally, an assessment metric is generated to measure the 3D videos focusing on human perception. Experimental analysis with common video datasets and comparison with different algorithms shows the efficiency of the proposed algorithm for 3D stereoscopic videos in terms of perceptual characteristics.