# Spatial Attention-based Non-reference Perceptual Quality Prediction Network for Omnidirectional Images

arXiv: https://arxiv.org/abs/2103.06116 · HTML: https://mlsanity.com/paper/2103.06116/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 36 references that are in the map.
- First posted on arXiv 10 Mar 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution](https://mlsanity.com/paper/1704.03915/) (2017)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs](https://mlsanity.com/paper/1904.06505/) (2019)
- [A Haar Wavelet-Based Perceptual Similarity Index for Image Quality Assessment](https://mlsanity.com/paper/1607.06140/) (2016)
- [MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video](https://mlsanity.com/paper/1902.09707/) (2019)
- [Wavelet Domain Style Transfer for an Effective Perception-distortion Tradeoff in Single Image Super-Resolution](https://mlsanity.com/paper/1910.04074/) (2019)
- [Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images](https://mlsanity.com/paper/2006.16581/) (2020)
- [Multi-level Wavelet-based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video](https://mlsanity.com/paper/2008.00499/) (2020)
- [Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model](https://mlsanity.com/paper/1807.10990/) (2018)
- [Adaptive 360 VR Video Streaming: Divide and Conquer!](https://mlsanity.com/paper/1609.08729/) (2016)

## Built on by (most cited first, 12 of 12)

- [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://mlsanity.com/paper/2502.10444/) (2025)
- [Assessor360: Multi-sequence Network for Blind Omnidirectional Image Quality Assessment](https://mlsanity.com/paper/2305.10983/) (2023)
- [Perceptual Quality Assessment of 360^circ Images Based on Generative Scanpath Representation](https://mlsanity.com/paper/2309.03472/) (2023)
- [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)
- [Blind VQA on 360deg Video via Progressively Learning from Pixels, Frames and Video](https://mlsanity.com/paper/2111.09503/) (2021)
- [Max360IQ: Blind Omnidirectional Image Quality Assessment with Multi-axis Attention](https://mlsanity.com/paper/2502.19046/) (2025)
- [Computational Analysis of Degradation Modeling in Blind Panoramic Image Quality Assessment](https://mlsanity.com/paper/2503.03255/) (2025)
- [Embedding-Driven Data Distillation for 360-Degree IQA With Residual-Aware Refinement](https://mlsanity.com/paper/2412.12667/) (2024)
- [Research on Audio-Visual Quality Assessment Dataset and Method for User-Generated Omnidirectional Video](https://mlsanity.com/paper/2506.10331/) (2025)
- [Hierarchical Graph Attention Network for No-Reference Omnidirectional Image Quality Assessment](https://mlsanity.com/paper/2508.09843/) (2025)
- [Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Unified and Generalized Approach](https://mlsanity.com/paper/2604.23953/) (2026)

## Abstract (arXiv)

Due to the strong correlation between visual attention and perceptual quality, many methods attempt to use human saliency information for image quality assessment. Although this mechanism can get good performance, the networks require human saliency labels, which is not easily accessible for omnidirectional images (ODI). To alleviate this issue, we propose a spatial attention-based perceptual quality prediction network for non-reference quality assessment on ODIs (SAP-net). To drive our SAP-net, we establish a large-scale IQA dataset of ODIs (IQA-ODI), which is composed of subjective scores of 200 subjects on 1,080 ODIs. In IQA-ODI, there are 120 high quality ODIs as reference, and 960 ODIs with impairments in both JPEG compression and map projection. Without any human saliency labels, our network can adaptively estimate human perceptual quality on impaired ODIs through a self-attention manner, which significantly promotes the prediction performance of quality scores. Moreover, our method greatly reduces the computational complexity in quality assessment task on ODIs. Extensive experiments validate that our network outperforms 9 state-of-the-art methods for quality assessment on ODIs. The dataset and code have been available on url https://github.com/yanglixiaoshen/SAP-Net.