ST360IQ: No-Reference Omnidirectional Image Quality Assessment with Spherical Vision Transformers

TL;DR

The paper introduces ST360IQ, a no-reference omnidirectional image quality assessment method using spherical vision transformers that effectively predicts perceived image quality by analyzing salient viewports.

Contribution

It proposes a novel spherical vision transformer-based model for no-reference 360 image quality assessment, addressing high-resolution and spherical distortion challenges.

Findings

Outperforms state-of-the-art methods on benchmark datasets

Correlates well with human perceived image quality

Effective in analyzing salient viewports for quality prediction

Abstract

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