# Perceptual Quality Assessment of Omnidirectional Images

arXiv: https://arxiv.org/abs/2207.02674 · HTML: https://mlsanity.com/paper/2207.02674/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 28 references that are in the map.
- First posted on arXiv 6 Jul 2022.

## Builds on (most cited first, 1 of 1)

- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)

## Built on by (most cited first, 30 of 34)

- [AIGCIQA2023: A Large-scale Image Quality Assessment Database for AI Generated Images: from the Perspectives of Quality, Authenticity and Correspondence](https://mlsanity.com/paper/2307.00211/) (2023)
- [MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2209.00244/) (2022)
- [A Survey of Representation Learning, Optimization Strategies, and Applications for Omnidirectional Vision](https://mlsanity.com/paper/2502.10444/) (2025)
- [GMS-3DQA: Projection-based Grid Mini-patch Sampling for 3D Model Quality Assessment](https://mlsanity.com/paper/2306.05658/) (2023)
- [Assessor360: Multi-sequence Network for Blind Omnidirectional Image Quality Assessment](https://mlsanity.com/paper/2305.10983/) (2023)
- [AIGCOIQA2024: Perceptual Quality Assessment of AI Generated Omnidirectional Images](https://mlsanity.com/paper/2404.01024/) (2024)
- [PKU-I2IQA: An Image-to-Image Quality Assessment Database for AI Generated Images](https://mlsanity.com/paper/2311.15556/) (2023)
- [Perceptual Quality Assessment of Omnidirectional Audio-visual Signals](https://mlsanity.com/paper/2307.10813/) (2023)
- [Audio-visual Saliency for Omnidirectional Videos](https://mlsanity.com/paper/2311.05190/) (2023)
- [ESIQA: Perceptual Quality Assessment of Vision-Pro-based Egocentric Spatial Images](https://mlsanity.com/paper/2407.21363/) (2024)
- [View-aware Salient Object Detection for 360deg Omnidirectional Image](https://mlsanity.com/paper/2209.13222/) (2022)
- [ST360IQ: No-Reference Omnidirectional Image Quality Assessment with Spherical Vision Transformers](https://mlsanity.com/paper/2303.06907/) (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)
- [Perceptual Visual Quality Assessment: Principles, Methods, and Future Directions](https://mlsanity.com/paper/2503.00625/) (2025)
- [How Does Audio Influence Visual Attention in Omnidirectional Videos? Database and Model](https://mlsanity.com/paper/2408.05411/) (2024)
- [Learned Scanpaths Aid Blind Panoramic Video Quality Assessment](https://mlsanity.com/paper/2404.00252/) (2024)
- [RealOSR: Latent Guidance Boosts Diffusion-based Real-world Omnidirectional Image Super-Resolutions](https://mlsanity.com/paper/2412.09646/) (2024)
- [Multitask Auxiliary Network for Perceptual Quality Assessment of Non-Uniformly Distorted Omnidirectional Images](https://mlsanity.com/paper/2501.11512/) (2025)
- [Quality-guided Skin Tone Enhancement for Portrait Photography](https://mlsanity.com/paper/2406.15848/) (2024)
- [Perceptual Depth Quality Assessment of Stereoscopic Omnidirectional Images](https://mlsanity.com/paper/2408.10134/) (2024)
- [Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Flexible and Effective Paradigm](https://mlsanity.com/paper/2503.06129/) (2025)
- [Quality Assessment and Distortion-aware Saliency Prediction for AI-Generated Omnidirectional Images](https://mlsanity.com/paper/2506.21925/) (2025)
- [RL-ScanIQA: Reinforcement-Learned Scanpaths for Blind 360degImage Quality Assessment](https://mlsanity.com/paper/2603.14297/) (2026)
- [Blind Image Quality Assessment: A Brief Survey](https://mlsanity.com/paper/2312.16551/) (2023)
- [Computational Analysis of Degradation Modeling in Blind Panoramic Image Quality Assessment](https://mlsanity.com/paper/2503.03255/) (2025)
- [Learning with Noisy Low-Cost MOS for Image Quality Assessment via Dual-Bias Calibration](https://mlsanity.com/paper/2311.15846/) (2023)
- [Beyond MOS: Subjective Image Quality Score Preprocessing Method Based on Perceptual Similarity](https://mlsanity.com/paper/2404.19666/) (2024)
- [Pathformer3D: A 3D Scanpath Transformer for 360deg Images](https://mlsanity.com/paper/2407.10563/) (2024)

## Abstract (arXiv)

Omnidirectional images and videos can provide immersive experience of real-world scenes in Virtual Reality (VR) environment. We present a perceptual omnidirectional image quality assessment (IQA) study in this paper since it is extremely important to provide a good quality of experience under the VR environment. We first establish an omnidirectional IQA (OIQA) database, which includes 16 source images and 320 distorted images degraded by 4 commonly encountered distortion types, namely JPEG compression, JPEG2000 compression, Gaussian blur and Gaussian noise. Then a subjective quality evaluation study is conducted on the OIQA database in the VR environment. Considering that humans can only see a part of the scene at one movement in the VR environment, visual attention becomes extremely important. Thus we also track head and eye movement data during the quality rating experiments. The original and distorted omnidirectional images, subjective quality ratings, and the head and eye movement data together constitute the OIQA database. State-of-the-art full-reference (FR) IQA measures are tested on the OIQA database, and some new observations different from traditional IQA are made.