Weighted Mean Deviation Similarity Index for Objective Omnidirectional Video Quality Assessment. Objective quality assessment for omnidirectional videos is essential for the watching experience and optimization processes of Virtual Reality (VR) technologies. Currently, the most used objective video quality assessment (VQA) metrics for omnidirectional videos are based on PSNR. Because PSNR usually shows weak consistency with human perception, we adapt the mean deviation similarity index (MDSI) for omnidirectional VQA, and present a weighted mean deviation similarity index (WSMDSI) for objective omnidirectional VQA. Because an omnidirectional video usually has a large frame resolution and a large number of frames, we also investigate the influence of temporal sampling on VQA. The proposed index WSMDSI is evaluated on an omnidirectional VQA database. Experimental results show that, compared with the stateoftheart methods based on PSNR, WSMDSI increases the performance by 23.5% and 23.25% on the indicators of SROCC and PLCC, respectively. We also found that a 14 reduction in temporal resolution does not significantly affect the performance of the objective VQA for omnidirectional video.

Quality Assessment for Omnidirectional Video with Consideration of Temporal Distortion Variations Omnidirectional video, also known as 360degree video, offers an immersive visual experience by providing viewers with an ability to look in all directions within a scene. The quality assessment for omnidirectional video is still a quite difficult task compared to 2D video. As the temporal changes of spatial distortions can considerably influence human visual perception, this paper proposes a full reference objective video quality assessment metric by considering both the spatial characteristics of omnidirectional video and the temporal variation of distortions across frames. Firstly, we construct a spatioxe2x80x93temporal quality assessment unit to evaluate the average distortion in temporal dimension at eye fixation level. The smoothed distortion value is then consolidated by the characteristics of temporal variations. Afterwards, a global quality score of the whole video sequence is produced by pooling. Finally, our experimental results show that our proposed VQA method improves the prediction performance of existing VQA methods for omnidirectional video.