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.

中文翻译：

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多功能 360 度视频质量评估


我们提出了一种用于 360 度（全向）视频视觉质量评估的新方法。该方法基于计算从 360 度视频中提取的视口上的多个时空客观质量特征。学习了一种新模型，可以将这些特征正确地组合成与主观质量得分密切匹配的指标。所提出方法的主要动机是：1）在视口上计算的质量度量比在投影域上计算的度量更好地捕捉用户体验； 2）视口的使用可以轻松支持当前360度视频系统中使用的不同投影方法； 3）没有一个单独的客观图像质量度量对于所有类型的视觉失真总是表现最好，而它们的学习组合能够适应不同的条件。基于最大的可用 360 度视频质量数据集和跨数据集验证的实验结果表明，所提出的指标优于最先进的 360 度和 2D 视频质量指标。