  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
