论文标题
盲目的全向图像质量评估与面向视口的图形卷积网络
Blind Omnidirectional Image Quality Assessment with Viewport Oriented Graph Convolutional Networks
论文作者
论文摘要
由于虚拟现实应用的快速增长，全向图像的质量评估变得越来越紧迫。与传统的2D图像和视频不同，全向内容可以为消费者提供自由变化的视口和更大的视野，覆盖$ 360^{\ circ} \ times180^{\ circ} $球形表面，这使得对填充图像的目标质量评估变得更具挑战性。在本文中，以人类视觉系统（HVS）的特征和全向内容的观看过程的激励，我们提出了一个新颖的面向视口的图形卷积网络（VGCN），以进行盲目的全向图像质量评估（IQA）。通常，观察者在浏览球形风景时通过并汇总了不同的视口信息后，倾向于给出360度图像的主观额定值。因此，为了在全向图像中建模视口的相互依赖性，我们构建了空间视口图。具体而言，首先将图形节点定义为具有较高概率的选定视口，这是受HVS的启发，即人类对结构信息更敏感。然后，这些节点通过空间关系连接，以捕获它们之间的相互作用。最后，通过图形卷积网络对所提出的图进行推理。此外，我们同时使用整个全向图像获得全球质量，而无需观看取样，以根据观看体验来提高性能。实验结果表明，我们提出的模型在两个公共全向IQA数据库上优于最先进的全参考和无参考的IQA指标。
Quality assessment of omnidirectional images has become increasingly urgent due to the rapid growth of virtual reality applications. Different from traditional 2D images and videos, omnidirectional contents can provide consumers with freely changeable viewports and a larger field of view covering the $360^{\circ}\times180^{\circ}$ spherical surface, which makes the objective quality assessment of omnidirectional images more challenging. In this paper, motivated by the characteristics of the human vision system (HVS) and the viewing process of omnidirectional contents, we propose a novel Viewport oriented Graph Convolution Network (VGCN) for blind omnidirectional image quality assessment (IQA). Generally, observers tend to give the subjective rating of a 360-degree image after passing and aggregating different viewports information when browsing the spherical scenery. Therefore, in order to model the mutual dependency of viewports in the omnidirectional image, we build a spatial viewport graph. Specifically, the graph nodes are first defined with selected viewports with higher probabilities to be seen, which is inspired by the HVS that human beings are more sensitive to structural information. Then, these nodes are connected by spatial relations to capture interactions among them. Finally, reasoning on the proposed graph is performed via graph convolutional networks. Moreover, we simultaneously obtain global quality using the entire omnidirectional image without viewport sampling to boost the performance according to the viewing experience. Experimental results demonstrate that our proposed model outperforms state-of-the-art full-reference and no-reference IQA metrics on two public omnidirectional IQA databases.
