Panoramic video and stereoscopic panoramic video are essential carriers of virtual reality content, so it is very crucial to establish their quality assessment models for the standardization of virtual reality industry. However, it is very challenging to evaluate the quality of the panoramic video at present. One reason is that the spatial information of the panoramic video is warped due to the projection process, and the conventional video quality assessment (VQA) method is difficult to deal with this problem. Another reason is that the traditional VQA method is problematic to capture the complex global time information in the panoramic video. In response to the above questions, this paper presents an end-to-end neural network model to evaluate the quality of panoramic video and stereoscopic panoramic video. Compared to other panoramic video quality assessment methods, our proposed method combines spherical convolutional neural networks (CNN) and non-local neural networks, which can effectively extract complex spatiotemporal information of the panoramic video. We evaluate the method in two databases, VRQ-TJU and VR-VQA48. Experiments show the effectiveness of different modules in our method, and our method outperforms state-of-the-art other related methods.

中文翻译：

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基于非局部球形CNN的全景视频质量评估

全景视频和立体全景视频是虚拟现实内容的重要载体，因此建立它们的质量评估模型对于虚拟现实行业的标准化至关重要。但是，目前评估全景视频的质量非常具有挑战性。原因之一是全景视频的空间信息由于投影过程而变形，并且传统的视频质量评估（VQA）方法难以解决该问题。另一个原因是，传统的VQA方法在捕获全景视频中的复杂全球时间信息方面存在问题。针对上述问题，本文提出了一种端到端神经网络模型来评估全景视频和立体全景视频的质量。与其他全景视频质量评估方法相比，我们提出的方法结合了球形卷积神经网络（CNN）和非局部神经网络，可以有效地提取全景视频的时空信息。我们在两个数据库VRQ-TJU和VR-VQA48中评估该方法。实验证明了我们方法中不同模块的有效性，并且我们的方法优于最新的其他相关方法。