# AIGCIQA2023: A Large-scale Image Quality Assessment Database for AI Generated Images: from the Perspectives of Quality, Authenticity and Correspondence

arXiv: https://arxiv.org/abs/2307.00211 · HTML: https://mlsanity.com/paper/2307.00211/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 35 references that are in the map.
- First posted on arXiv 1 Jul 2023.

## Builds on (most cited first, 22 of 22)

- [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)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Scaling Autoregressive Models for Content-Rich Text-to-Image Generation](https://mlsanity.com/paper/2206.10789/) (2022)
- [ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation](https://mlsanity.com/paper/2304.05977/) (2023)
- [Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation](https://mlsanity.com/paper/2305.01569/) (2023)
- [Aligning Text-to-Image Models using Human Feedback](https://mlsanity.com/paper/2302.12192/) (2023)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [One Transformer Fits All Distributions in Multi-Modal Diffusion at Scale](https://mlsanity.com/paper/2303.06555/) (2023)
- [LAFITE: Towards Language-Free Training for Text-to-Image Generation](https://mlsanity.com/paper/2111.13792/) (2021)
- [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)
- [Saliency in Augmented Reality](https://mlsanity.com/paper/2204.08308/) (2022)
- [Masked Autoencoders as Image Processors](https://mlsanity.com/paper/2303.17316/) (2023)

## Built on by (most cited first, 30 of 61)

- [Teaching Large Language Models to Regress Accurate Image Quality Scores using Score Distribution](https://mlsanity.com/paper/2501.11561/) (2025)
- [AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2404.03407/) (2024)
- [Quality Assessment in the Era of Large Models: A Survey](https://mlsanity.com/paper/2409.00031/) (2024)
- [AIGV-Assessor: Benchmarking and Evaluating the Perceptual Quality of Text-to-Video Generation with LMM](https://mlsanity.com/paper/2411.17221/) (2024)
- [LMM4Edit: Benchmarking and Evaluating Multimodal Image Editing with LMMs](https://mlsanity.com/paper/2507.16193/) (2025)
- [Exploring the Naturalness of AI-Generated Images](https://mlsanity.com/paper/2312.05476/) (2023)
- [Q-Refine: A Perceptual Quality Refiner for AI-Generated Image](https://mlsanity.com/paper/2401.01117/) (2024)
- [NTIRE 2024 Quality Assessment of AI-Generated Content Challenge](https://mlsanity.com/paper/2404.16687/) (2024)
- [AIGCOIQA2024: Perceptual Quality Assessment of AI Generated Omnidirectional Images](https://mlsanity.com/paper/2404.01024/) (2024)
- [Video Quality Assessment: A Comprehensive Survey](https://mlsanity.com/paper/2412.04508/) (2024)
- [PKU-I2IQA: An Image-to-Image Quality Assessment Database for AI Generated Images](https://mlsanity.com/paper/2311.15556/) (2023)
- [PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via Prompts](https://mlsanity.com/paper/2403.04993/) (2024)
- [CLIP-AGIQA: Boosting the Performance of AI-Generated Image Quality Assessment with CLIP](https://mlsanity.com/paper/2408.15098/) (2024)
- [VisualCritic: Making LMMs Perceive Visual Quality Like Humans](https://mlsanity.com/paper/2403.12806/) (2024)
- [Quality Assessment for AI Generated Images with Instruction Tuning](https://mlsanity.com/paper/2405.07346/) (2024)
- [AGHI-QA: A Subjective-Aligned Dataset and Metric for AI-Generated Human Images](https://mlsanity.com/paper/2504.21308/) (2025)
- [Opinion-Unaware Blind Image Quality Assessment using Multi-Scale Deep Feature Statistics](https://mlsanity.com/paper/2405.18790/) (2024)
- [PSCR: Patches Sampling-based Contrastive Regression for AIGC Image Quality Assessment](https://mlsanity.com/paper/2312.05897/) (2023)
- [Adaptive Mixed-Scale Feature Fusion Network for Blind AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2404.15163/) (2024)
- [F-Bench: Rethinking Human Preference Evaluation Metrics for Benchmarking Face Generation, Customization, and Restoration](https://mlsanity.com/paper/2412.13155/) (2024)
- [Vision-Language Consistency Guided Multi-modal Prompt Learning for Blind AI Generated Image Quality Assessment](https://mlsanity.com/paper/2406.16641/) (2024)
- [NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment](https://mlsanity.com/paper/2505.16314/) (2025)
- [Audio-visual Saliency for Omnidirectional Videos](https://mlsanity.com/paper/2311.05190/) (2023)
- [TIER: Text-Image Encoder-based Regression for AIGC Image Quality Assessment](https://mlsanity.com/paper/2401.03854/) (2024)
- [LMME3DHF: Benchmarking and Evaluating Multimodal 3D Human Face Generation with LMMs](https://mlsanity.com/paper/2504.20466/) (2025)
- [DFBench: Benchmarking Deepfake Image Detection Capability of Large Multimodal Models](https://mlsanity.com/paper/2506.03007/) (2025)
- [POET: Supporting Prompting Creativity and Personalization with Automated Expansion of Text-to-Image Generation](https://mlsanity.com/paper/2504.13392/) (2025)
- [Text-Visual Semantic Constrained AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2507.10432/) (2025)
- [AEGIS: Authenticity Evaluation Benchmark for AI-Generated Video Sequences](https://mlsanity.com/paper/2508.10771/) (2025)
- [AI-Generated Image Quality Assessment Based on Task-Specific Prompt and Multi-Granularity Similarity](https://mlsanity.com/paper/2411.16087/) (2024)

## Abstract (arXiv)

In this paper, in order to get a better understanding of the human visual preferences for AIGIs, a large-scale IQA database for AIGC is established, which is named as AIGCIQA2023. We first generate over 2000 images based on 6 state-of-the-art text-to-image generation models using 100 prompts. Based on these images, a well-organized subjective experiment is conducted to assess the human visual preferences for each image from three perspectives including quality, authenticity and correspondence. Finally, based on this large-scale database, we conduct a benchmark experiment to evaluate the performance of several state-of-the-art IQA metrics on our constructed database.