ST360IQ: No-Reference Omnidirectional Image Quality Assessment with Spherical Vision Transformers
This addresses the challenge of evaluating 360 image quality for applications like capturing and transmission, though it is an incremental improvement over existing methods.
The paper tackles the problem of no-reference quality assessment for omnidirectional (360) images by proposing the ST360IQ model, which extracts tangent viewports from salient parts and uses vision transformers to estimate quality scores, achieving results correlated with human-perceived quality on benchmark datasets OIQA and CVIQ.
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