Max360IQ: Blind Omnidirectional Image Quality Assessment with Multi-axis Attention
Journal:
arXiv
Published Date:
Feb 26, 2025
Abstract
Omnidirectional image, also called 360-degree image, is able to capture the
entire 360-degree scene, thereby providing more realistic immersive feelings
for users than general 2D image and stereoscopic image. Meanwhile, this feature
brings great challenges to measuring the perceptual quality of omnidirectional
images, which is closely related to users' quality of experience, especially
when the omnidirectional images suffer from non-uniform distortion. In this
paper, we propose a novel and effective blind omnidirectional image quality
assessment (BOIQA) model with multi-axis attention (Max360IQ), which can
proficiently measure not only the quality of uniformly distorted
omnidirectional images but also the quality of non-uniformly distorted
omnidirectional images. Specifically, the proposed Max360IQ is mainly composed
of a backbone with stacked multi-axis attention modules for capturing both
global and local spatial interactions of extracted viewports, a multi-scale
feature integration (MSFI) module to fuse multi-scale features and a quality
regression module with deep semantic guidance for predicting the quality of
omnidirectional images. Experimental results demonstrate that the proposed
Max360IQ outperforms the state-of-the-art Assessor360 by 3.6\% in terms of SRCC
on the JUFE database with non-uniform distortion, and gains improvement of
0.4\% and 0.8\% in terms of SRCC on the OIQA and CVIQ databases, respectively.
The source code is available at https://github.com/WenJuing/Max360IQ.
