Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Flexible and Effective Paradigm
This addresses the need for scalable and efficient quality assessment in omnidirectional images, which is incremental as it builds on existing BOIQA methods by simplifying the approach.
The paper tackles the problem of blind omnidirectional image quality assessment (BOIQA) by proposing a viewport-unaware paradigm that avoids computationally expensive viewport generation, achieving competitive performance across four databases with low complexity and adaptability to 2D image quality assessment.
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.