# AIGCOIQA2024: Perceptual Quality Assessment of AI Generated Omnidirectional Images

arXiv: https://arxiv.org/abs/2404.01024 · HTML: https://mlsanity.com/paper/2404.01024/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 38 references that are in the map.
- First posted on arXiv 1 Apr 2024.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2204.08958/) (2022)
- [MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion](https://mlsanity.com/paper/2307.01097/) (2023)
- [No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency](https://mlsanity.com/paper/2108.06858/) (2021)
- [SceneScape: Text-Driven Consistent Scene Generation](https://mlsanity.com/paper/2302.01133/) (2023)
- [AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2306.04717/) (2023)
- [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)
- [Perceptual Quality Assessment of Omnidirectional Images](https://mlsanity.com/paper/2207.02674/) (2022)
- [Confusing Image Quality Assessment: Towards Better Augmented Reality Experience](https://mlsanity.com/paper/2204.04900/) (2022)
- [Text2Light: Zero-Shot Text-Driven HDR Panorama Generation](https://mlsanity.com/paper/2209.09898/) (2022)
- [Saliency in Augmented Reality](https://mlsanity.com/paper/2204.08308/) (2022)
- [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)

## Built on by (most cited first, 16 of 16)

- [Quality Assessment in the Era of Large Models: A Survey](https://mlsanity.com/paper/2409.00031/) (2024)
- [Quality Assessment for AI Generated Images with Instruction Tuning](https://mlsanity.com/paper/2405.07346/) (2024)
- [F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration](https://mlsanity.com/paper/2412.13155/) (2024)
- [LOVE: Benchmarking and Evaluating Text-to-Video Generation and Video-to-Text Interpretation](https://mlsanity.com/paper/2505.12098/) (2025)
- [Omni2: Unifying Omnidirectional Image Generation and Editing in an Omni Model](https://mlsanity.com/paper/2504.11379/) (2025)
- [G-Refine: A General Quality Refiner for Text-to-Image Generation](https://mlsanity.com/paper/2404.18343/) (2024)
- [HarmonyIQA: Pioneering Benchmark and Model for Image Harmonization Quality Assessment](https://mlsanity.com/paper/2501.01116/) (2025)
- [LMME3DHF: Benchmarking and Evaluating Multimodal 3D Human Face Generation with LMMs](https://mlsanity.com/paper/2504.20466/) (2025)
- [MVBIND: Self-Supervised Music Recommendation For Videos Via Embedding Space Binding](https://mlsanity.com/paper/2405.09286/) (2024)
- [Quality-guided Skin Tone Enhancement for Portrait Photography](https://mlsanity.com/paper/2406.15848/) (2024)
- [Multi-Dimensional Quality Assessment for Text-to-3D Assets: Dataset and Model](https://mlsanity.com/paper/2502.16915/) (2025)
- [Computational Analysis of Degradation Modeling in Blind Panoramic Image Quality Assessment](https://mlsanity.com/paper/2503.03255/) (2025)
- [PKU-AIGIQA-4K: A Perceptual Quality Assessment Database for Both Text-to-Image and Image-to-Image AI-Generated Images](https://mlsanity.com/paper/2404.18409/) (2024)
- [Immersive Multimedia Communication: State-of-the-Art on eXtended Reality Streaming](https://mlsanity.com/paper/2506.10004/) (2025)
- [Inpainting the Red Planet: Diffusion Models for the Reconstruction of Martian Environments in Virtual Reality](https://mlsanity.com/paper/2510.14765/) (2025)
- [Viewport-Unaware Blind Omnidirectional Image Quality Assessment: A Unified and Generalized Approach](https://mlsanity.com/paper/2604.23953/) (2026)

## Abstract (arXiv)

In recent years, the rapid advancement of Artificial Intelligence Generated Content (AIGC) has attracted widespread attention. Among the AIGC, AI generated omnidirectional images hold significant potential for Virtual Reality (VR) and Augmented Reality (AR) applications, hence omnidirectional AIGC techniques have also been widely studied. AI-generated omnidirectional images exhibit unique distortions compared to natural omnidirectional images, however, there is no dedicated Image Quality Assessment (IQA) criteria for assessing them. This study addresses this gap by establishing a large-scale AI generated omnidirectional image IQA database named AIGCOIQA2024 and constructing a comprehensive benchmark. We first generate 300 omnidirectional images based on 5 AIGC models utilizing 25 text prompts. A subjective IQA experiment is conducted subsequently to assess human visual preferences from three perspectives including quality, comfortability, and correspondence. Finally, we conduct a benchmark experiment to evaluate the performance of state-of-the-art IQA models on our database. The database will be released to facilitate future research.