  We propose a new method for the visual quality assessment of 360-degree
(omnidirectional) videos. The proposed method is based on computing multiple
spatio-temporal objective quality features on viewports extracted from
360-degree videos. A new model is learnt to properly combine these features
into a metric that closely matches subjective quality scores. The main
motivations for the proposed approach are that: 1) quality metrics computed on
viewports better captures the user experience than metrics computed on the
projection domain; 2) the use of viewports easily supports different projection
methods being used in current 360-degree video systems; and 3) no individual
objective image quality metric always performs the best for all types of visual
distortions, while a learned combination of them is able to adapt to different
conditions. Experimental results, based on both the largest available
360-degree videos quality dataset and a cross-dataset validation, demonstrate
that the proposed metric outperforms state-of-the-art 360-degree and 2D video
quality metrics.
