Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Flexible and Effective Paradigm
Journal:
arXiv
Published Date:
Mar 8, 2025
Abstract
Most of existing blind omnidirectional image quality assessment (BOIQA)
models rely on viewport generation by modeling user viewing behavior or
transforming omnidirectional images (OIs) into varying formats; however, these
methods are either computationally expensive or less scalable. To solve these
issues, in this paper, we present a flexible and effective paradigm, which is
viewport-unaware and can be easily adapted to 2D plane image quality assessment
(2D-IQA). Specifically, the proposed BOIQA model includes an adaptive
prior-equator sampling module for extracting a patch sequence from the
equirectangular projection (ERP) image in a resolution-agnostic manner, a
progressive deformation-unaware feature fusion module which is able to capture
patch-wise quality degradation in a deformation-immune way, and a
local-to-global quality aggregation module to adaptively map local perception
to global quality. Extensive experiments across four OIQA databases (including
uniformly distorted OIs and non-uniformly distorted OIs) demonstrate that the
proposed model achieves competitive performance with low complexity against
other state-of-the-art models, and we also verify its adaptive capacity to
2D-IQA.
