Omnidirectional image quality assessment (OIQA) aims to predict the perceptual quality of omnidirectional images that cover the whole 180$\times$360$^{\circ}$ viewing range of the visual environment. Here we propose a blind/no-reference OIQA method named S$^2$ that bridges the gap between low-level statistics and high-level semantics of omnidirectional images. Specifically, statistic and semantic features are extracted in separate paths from multiple local viewports and the hallucinated global omnidirectional image, respectively. A quality regression along with a weighting process is then followed that maps the extracted quality-aware features to a perceptual quality prediction. Experimental results demonstrate that the proposed S$^2$ method offers highly competitive performance against state-of-the-art methods.

翻译：全方位图像质量评估（OIQA）旨在预测覆盖视觉环境180°×360°完整视角范围的全方位图像的感知质量。本文提出一种名为S²的无参考/盲测OIQA方法，该方法弥合了全方位图像底层统计特征与高层语义特征之间的鸿沟。具体而言，分别从多个局部视口和基于补全技术的全局全方位图像中提取统计特征与语义特征，随后通过质量回归与加权过程，将提取的质量感知特征映射为感知质量预测值。实验结果表明，与现有最先进方法相比，所提出的S²方法展现出极具竞争力的性能。