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:直方向图像, aka 360 图像, 能够提供隐性、 互动的视觉体验。 近些年来, 其受欢迎度急剧提高, 评估360 图像的质量成为一个令人感兴趣的问题, 因为它为捕捉、 传送和消耗新媒体提供了洞察力。 但是, 直接调整为全方向数据的标准自然图像提议的质量评估方法, 带来了某些挑战。 这些模型需要处理非常高分辨率的数据和由于图像的球状形式而隐含的扭曲。 在这次研究中, 我们展示了一个不参照360 图像质量评估的方法。 我们提议的ST360 Q 模型从输入的全方向图像的突出部分中提取亮色的视图, 并使用基于模块处理突出度选择性补丁/ 的视觉转换器, 来估计每个视图站的质量得分 。 然后, 这些评分加起来来给最终的质量评分。 我们在两个基准数据集, 即 OIQA 和 CVIQ 数据集上进行的实验显示, 与状态- art 相比, 我们的方法从输入了输入的全方向图像的质量 Q- Qirealfi/ comimalimal 。</s>