mlsanityData to 14 Oct 2026

Max360IQ: Blind Omnidirectional Image Quality Assessment with Multi-axis Attention

arXiv 2502.19046 ↗First posted 26 Feb 2025eess.IV cs.CVcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 17 of its 48 references that are in the map. First posted on arXiv 26 Feb 2025.

Builds on

17 of 48 references.

References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  3. 2017Fine-tuning CNN Image Retrieval with No Human Annotationcited by 499
  4. 2022MaxViT: Multi-Axis Vision Transformercited by 476
  5. 2019Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Networkcited by 302
  6. 2017RankIQA: Learning from Rankings for No-reference Image Quality Assessmentcited by 151
  7. 2020MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessmentcited by 144
  8. 2019dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairscited by 73
  9. 2020Norm-in-Norm Loss with Faster Convergence and Better Performance for Image Quality Assessmentcited by 54
  10. 2020Blind Omnidirectional Image Quality Assessment with Viewport Oriented Graph Convolutional Networkscited by 31
  11. 2020Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessmentcited by 24
  12. 2023Assessor360: Multi-sequence Network for Blind Omnidirectional Image Quality Assessmentcited by 17
5 more
  1. 2021Spatial Attention-based Non-reference Perceptual Quality Prediction Network for Omnidirectional Imagescited by 12
  2. 2021Adaptive Hypergraph Convolutional Network for No-Reference 360-degree Image Quality Assessmentcited by 10
  3. 2018VR IQA NET: Deep Virtual Reality Image Quality Assessment using Adversarial Learningcited by 9
  4. 2021SurroundNet: Towards Effective Low-Light Image Enhancementcited by 7
  5. 2023ST360IQ: No-Reference Omnidirectional Image Quality Assessment with Spherical Vision Transformerscited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Unified and Generalized Approachcited by 0
  2. 2026Scientific Image Quality Assessment via Multi-modal Retrieval-Augmented Generationcited by 0