Publication: ST360IQ: no-reference omnidirectional image quality assessment with spherical vision transformers
Program
KU-Authors
KU Authors
Co-Authors
Hedi Elfkir, Mohamed
İmamoğlu, Nevrez
Özçınar, Çağrı
Erdem, Erkut
Advisor
Publication Date
Language
en
Journal Title
Journal ISSN
Volume Title
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 © 2023 IEEE.
Description
Source:
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publisher:
Institute of Electrical and Electronics Engineers Inc.
Keywords:
Subject
Image quality assessment, Reference image, Quality of service