Omnidirectional images, aka 360 images, can deliver immersive and interactive visual experiences. As their popularity has increased dramatically in recent years, evaluating the quality of 360 images has become a problem of interest since it provides insights for capturing, transmitting, and consuming this new media. However, directly adapting quality assessment methods proposed for standard natural images for omnidirectional data poses certain challenges. These models need to deal with very high-resolution data and implicit distortions due to the spherical form of the images. In this study, we present a method for no-reference 360 image quality assessment. Our proposed ST360IQ model extracts tangent viewports from the salient parts of the input omnidirectional image and employs a vision-transformers based module processing saliency selective patches/tokens that estimates a quality score from each viewport. Then, it aggregates these scores to give a final quality score. Our experiments on two benchmark datasets, namely OIQA and CVIQ datasets, demonstrate that as compared to the state-of-the-art, our approach predicts the quality of an omnidirectional image correlated with the human-perceived image quality. The code has been available on https://github.com/Nafiseh-Tofighi/ST360IQ

翻译：全景图像（即360度图像）能提供沉浸式交互视觉体验。随着近年来其普及度急剧提升，评估360度图像质量已成为备受关注的问题，因为这能为采集、传输和消费这种新型媒体提供重要洞见。然而，直接将面向标准自然图像设计的质量评估方法应用于全景数据面临诸多挑战：这类模型需要处理极高分辨率的数据，且因图像呈球面形态而产生隐式失真。本研究提出一种无参考360度图像质量评估方法。我们提出的ST360IQ模型从输入全景图像的显著性区域中提取切线视口，并采用基于视觉变换器的模块处理显著性选择性的图像块/标记，进而从每个视口估算质量分数，最终聚合这些分数得出整体质量分数。在两个基准数据集（OIQA和CVIQ数据集）上的实验表明，与现有最优方法相比，我们的方法预测的全景图像质量与人类感知图像质量具有更高相关性。相关代码已开源至 https://github.com/Nafiseh-Tofighi/ST360IQ