To render omnidirectional (or 360°) visual content, a projection that maps the pixels from a portion of the viewing sphere to a 2D plane must be employed; this projection creates the viewport image shown to the user and thus has an important role on the quality of experience, for this type of content. However, a sphere to planar projection always introduces geometrical distortions on the rendered image, such as stretching and/or bending of some image regions and structures, which may impact negatively the quality offered to the users. In this article, a content-aware objective quality metric, that predicts the perceived viewport quality, is proposed for the general perspective projection (GPP) rendering of 360° images, which includes the popular rectilinear and stereographic projections. The proposed metric relies on two set of features that characterize the bending of straight lines and stretching of image regions. The extracted features, and the corresponding viewport subjective quality CMOS (comparative mean opinion scores) are then used to build a quality prediction model, based on Support Vector Regression (SVR). The experimental results show that the proposed metric is able to predict the viewport CMOS with a Pearson correlation coefficient close to 0.8. Furthermore, it is shown that the proposed metric may be used to accurately find the GPP projection center that minimizes the perceived geometric distortions, according to the viewport content.

中文翻译：

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360° 图像视口渲染中感知几何失真的客观评估

要呈现全向（或 360°）视觉内容，必须采用将像素从视球的一部分映射到 2D 平面的投影；此投影创建向用户显示的视口图像，因此对于此类内容的体验质量具有重要作用。然而，球面到平面投影总是在渲染图像上引入几何失真，例如某些图像区域和结构的拉伸和/或弯曲，这可能会对提供给用户的质量产生负面影响。在本文中，针对 360° 图像的一般透视投影 (GPP) 渲染提出了一种内容感知客观质量度量，可预测感知视口质量，其中包括流行的直线和立体投影。所提出的度量依赖于表征直线弯曲和图像区域拉伸的两组特征。然后使用提取的特征和相应的视口主观质量 CMOS（比较平均意见分数）来构建基于支持向量回归 (SVR) 的质量预测模型。实验结果表明，所提出的度量能够以接近 0.8 的 Pearson 相关系数预测视口 CMOS。此外，表明根据视口内容，所提出的度量可用于准确地找到最小化感知几何失真的 GPP 投影中心。然后使用相应的视口主观质量 CMOS（比较平均意见分数）来构建基于支持向量回归 (SVR) 的质量预测模型。实验结果表明，所提出的度量能够以接近 0.8 的 Pearson 相关系数预测视口 CMOS。此外，表明根据视口内容，所提出的度量可用于准确地找到最小化感知几何失真的 GPP 投影中心。然后使用相应的视口主观质量 CMOS（比较平均意见分数）来构建基于支持向量回归 (SVR) 的质量预测模型。实验结果表明，所提出的度量能够以接近 0.8 的 Pearson 相关系数预测视口 CMOS。此外，表明根据视口内容，所提出的度量可用于准确地找到最小化感知几何失真的 GPP 投影中心。