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Omnidirectional video enables spherical stimuli with the $360 \times 180^\circ$ viewing range. Meanwhile, only the viewport region of omnidirectionalvideo can be seen by the observer through head movement (HM), and an evensmaller region within the viewport can be clearly perceived through eyemovement (EM). Thus, the subjective quality of omnidirectional video may becorrelated with HM and EM of human behavior. To fill in the gap betweensubjective quality and human behavior, this paper proposes a large-scale visualquality assessment (VQA) dataset of omnidirectional video, called VQA-OV, whichcollects 60 reference sequences and 540 impaired sequences. Our VQA-OV datasetprovides not only the subjective quality scores of sequences but also the HMand EM data of subjects. By mining our dataset, we find that the subjectivequality of omnidirectional video is indeed related to HM and EM. Hence, wedevelop a deep learning model, which embeds HM and EM, for objective VQA onomnidirectional video. Experimental results show that our model significantlyimproves the state-of-the-art performance of VQA on omnidirectional video.

Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model