# Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Flexible and Effective Paradigm

arXiv: https://arxiv.org/abs/2503.06129 · HTML: https://mlsanity.com/paper/2503.06129/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 52 references that are in the map.
- First posted on arXiv 8 Mar 2025.

## Builds on (most cited first, 15 of 15)

- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [Deformable ConvNets v2: More Deformable, Better Results](https://mlsanity.com/paper/1811.11168/) (2018)
- [InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions](https://mlsanity.com/paper/2211.05778/) (2022)
- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective](https://mlsanity.com/paper/2303.14968/) (2023)
- [Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessment](https://mlsanity.com/paper/2008.03889/) (2020)
- [Perceptual Quality Assessment of Omnidirectional Images](https://mlsanity.com/paper/2207.02674/) (2022)
- [Blind Omnidirectional Image Quality Assessment with Viewport Oriented Graph Convolutional Networks](https://mlsanity.com/paper/2002.09140/) (2020)
- [VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learning](https://mlsanity.com/paper/1804.03943/) (2018)
- [Pseudocylindrical Convolutions for Learned Omnidirectional Image Compression](https://mlsanity.com/paper/2112.13227/) (2021)
- [Quality Assessment of Stereoscopic 360-degree Images from Multi-viewports](https://mlsanity.com/paper/1908.10079/) (2019)
- [Subjective and Objective Quality Assessment of Non-Uniformly Distorted Omnidirectional Images](https://mlsanity.com/paper/2501.11511/) (2025)
- [Omnidirectional Image Quality Captioning: A Large-scale Database and A New Model](https://mlsanity.com/paper/2502.15271/) (2025)

## Built on by (most cited first, 2 of 2)

- [RL-ScanIQA: Reinforcement-Learned Scanpaths for Blind 360degImage Quality Assessment](https://mlsanity.com/paper/2603.14297/) (2026)
- [Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Unified and Generalized Approach](https://mlsanity.com/paper/2604.23953/) (2026)

## Abstract (arXiv)

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.