提出无需视口的全景图像质量评估新方法，计算轻量且可通用到2D图像。
Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Flexible and Effective Paradigm
- 通过自适应等腰采样从全景图提取图像块序列，不依赖视口生成。
- 在四个数据库上表现优于主流模型，复杂度低且支持非均匀失真评估。
- 可无缝迁移至2D图像质量评估，适合跨域应用研究者。
现有盲式全景图像质量评估（BOIQA）模型通常依赖视口生成或图像格式转换，存在计算开销大、扩展性差的问题。本文提出一种无需视口的灵活高效范式，可轻松适配2D图像质量评估。所提模型包含：自适应先验-赤道采样模块，以分辨率无关方式从等距投影（ERP）图像中提取图像块序列；渐进式无形变特征融合模块，能无损捕捉局部失真；以及局部到全局质量聚合模块，自适应映射局部感知至整体质量。在四个全景图像质量评估数据库（含均匀与非均匀失真）上的实验表明，该模型在性能上达到先进水平，同时保持低复杂度，并验证了其向2D-IQA的适配能力。
原文摘要 · Abstract (English)
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.
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