Publication:
Omnidirectional image quality assessment with local-global vision transformers

Placeholder

Organizational Units

Program

KU Authors

Co-Authors

Elfkir, Mohamed Hedi
Imamoglu, Nevrez
Ozcinar, Cagri
Erdem, Erkut

Advisor

Publication Date

Language

en

Journal Title

Journal ISSN

Volume Title

Abstract

With the rising popularity of omnidirectional images (ODIs) in virtual reality applications, the need for specialized image quality assessment (IQA) methods becomes increasingly critical. Traditional IQA approaches, designed for rectilinear images, often fail to evaluate ODIs accurately due to their 360 -degree scene representation. Addressing this, we introduce the Local - Global Transformer for 360 -degree Image Quality Assessment (LGT360IQ). This novel framework features dual branches tailored to mimic top -down and bottom -up visual attention mechanisms, adapted for the spherical characteristics of ODIs. The local branch processes tangent viewports from salient regions within the equirectangular projection image, extracting detailed features for granular quality assessment. In parallel, the global branch utilizes a task -dependent token sampling strategy for holistic image feature processing and quality score prediction. This integrated approach combines local and global information, offering an effective IQA method for ODIs. Our extensive evaluation across three benchmark ODI datasets, CVIQ, OIQA, and ODI, demonstrates LGT360IQ superior performance and establishes its role in advancing the field of IQA for omnidirectional images.

Description

Source:

Image and Vision Computing

Publisher:

Elsevier

Keywords:

Subject

Computer science, artificial intelligence, Computer science, software engineering, Computer science, theory and methods, Engineering, electrical and electronic, Optics

Citation

Endorsement

Review

Supplemented By

Referenced By

Copy Rights Note

0

Views

0

Downloads

View PlumX Details