# Q-Refine: A Perceptual Quality Refiner for AI-Generated Image

arXiv: https://arxiv.org/abs/2401.01117 · HTML: https://mlsanity.com/paper/2401.01117/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 43 references that are in the map.
- First posted on arXiv 2 Jan 2024.

## Builds on (most cited first, 27 of 27)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis](https://mlsanity.com/paper/2310.00426/) (2023)
- [Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels](https://mlsanity.com/paper/2312.17090/) (2023)
- [Text-to-image Diffusion Models in Generative AI: A Survey](https://mlsanity.com/paper/2303.07909/) (2023)
- [DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior](https://mlsanity.com/paper/2308.15070/) (2023)
- [Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision](https://mlsanity.com/paper/2309.14181/) (2023)
- [From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality](https://mlsanity.com/paper/1912.10088/) (2019)
- [Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models](https://mlsanity.com/paper/2311.06783/) (2023)
- [AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2306.04717/) (2023)
- [Residual Feature Distillation Network for Lightweight Image Super-Resolution](https://mlsanity.com/paper/2009.11551/) (2020)
- [Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training](https://mlsanity.com/paper/2004.01178/) (2020)
- [Text-Guided Synthesis of Artistic Images with Retrieval-Augmented Diffusion Models](https://mlsanity.com/paper/2207.13038/) (2022)
- [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)
- [A Perceptual Quality Assessment Exploration for AIGC Images](https://mlsanity.com/paper/2303.12618/) (2023)
- [Adversarial Text-to-Image Synthesis: A Review](https://mlsanity.com/paper/2101.09983/) (2021)
- [Best Prompts for Text-to-Image Models and How to Find Them](https://mlsanity.com/paper/2209.11711/) (2022)
- [Advancing Zero-Shot Digital Human Quality Assessment through Text-Prompted Evaluation](https://mlsanity.com/paper/2307.02808/) (2023)
- [VDPVE: VQA Dataset for Perceptual Video Enhancement](https://mlsanity.com/paper/2303.09290/) (2023)
- [Q-Boost: On Visual Quality Assessment Ability of Low-level Multi-Modality Foundation Models](https://mlsanity.com/paper/2312.15300/) (2023)
- [GMS-3DQA: Projection-based Grid Mini-patch Sampling for 3D Model Quality Assessment](https://mlsanity.com/paper/2306.05658/) (2023)
- [StableVQA: A Deep No-Reference Quality Assessment Model for Video Stability](https://mlsanity.com/paper/2308.04904/) (2023)
- [A real-time blind quality-of-experience assessment metric for HTTP adaptive streaming](https://mlsanity.com/paper/2303.09818/) (2023)
- [XGC-VQA: A unified video quality assessment model for User, Professionally, and Occupationally-Generated Content](https://mlsanity.com/paper/2303.13859/) (2023)

## Built on by (most cited first, 21 of 21)

- [A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends](https://mlsanity.com/paper/2410.15067/) (2024)
- [Subjective-Aligned Dataset and Metric for Text-to-Video Quality Assessment](https://mlsanity.com/paper/2403.11956/) (2024)
- [AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2404.03407/) (2024)
- [MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Model](https://mlsanity.com/paper/2402.16749/) (2024)
- [R-Bench: Are your Large Multimodal Model Robust to Real-world Corruptions?](https://mlsanity.com/paper/2410.05474/) (2024)
- [Quality Assessment in the Era of Large Models: A Survey](https://mlsanity.com/paper/2409.00031/) (2024)
- [Large Multi-modality Model Assisted AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2404.17762/) (2024)
- [NTIRE 2024 Quality Assessment of AI-Generated Content Challenge](https://mlsanity.com/paper/2404.16687/) (2024)
- [Image Quality Assessment: From Human to Machine Preference](https://mlsanity.com/paper/2503.10078/) (2025)
- [G-Refine: A General Quality Refiner for Text-to-Image Generation](https://mlsanity.com/paper/2404.18343/) (2024)
- [MoEdit: On Learning Quantity Perception for Multi-object Image Editing](https://mlsanity.com/paper/2503.10112/) (2025)
- [Light-VQA+: A Video Quality Assessment Model for Exposure Correction with Vision-Language Guidance](https://mlsanity.com/paper/2405.03333/) (2024)
- [CMC-Bench: Towards a New Paradigm of Visual Signal Compression](https://mlsanity.com/paper/2406.09356/) (2024)
- [Q-Save: Towards Scoring and Attribution for Generated Video Evaluation](https://mlsanity.com/paper/2511.18825/) (2025)
- [Q-Sched: Pushing the Boundaries of Few-Step Diffusion Models with Quantization-Aware Scheduling](https://mlsanity.com/paper/2509.01624/) (2025)
- [LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents](https://mlsanity.com/paper/2412.04090/) (2024)
- [D-Judge: How Far Are We? Assessing the Discrepancies Between AI-synthesized and Natural Images through Multimodal Guidance](https://mlsanity.com/paper/2412.17632/) (2024)
- [RePack then Refine: Efficient Diffusion Transformer with Vision Foundation Model](https://mlsanity.com/paper/2512.12083/) (2025)
- [rPPG-VQA: A Video Quality Assessment Framework for Unsupervised rPPG Training](https://mlsanity.com/paper/2604.11156/) (2026)
- [IQA-Adapter: Exploring Knowledge Transfer from Image Quality Assessment to Diffusion-based Generative Models](https://mlsanity.com/paper/2412.01794/) (2024)
- [SynerDiff: Synergetic Continuous Batching for Fast and Parallel Diffusion Model Inference](https://mlsanity.com/paper/2605.08835/) (2026)

## Abstract (arXiv)

With the rapid evolution of the Text-to-Image (T2I) model in recent years, their unsatisfactory generation result has become a challenge. However, uniformly refining AI-Generated Images (AIGIs) of different qualities not only limited optimization capabilities for low-quality AIGIs but also brought negative optimization to high-quality AIGIs. To address this issue, a quality-award refiner named Q-Refine is proposed. Based on the preference of the Human Visual System (HVS), Q-Refine uses the Image Quality Assessment (IQA) metric to guide the refining process for the first time, and modify images of different qualities through three adaptive pipelines. Experimental shows that for mainstream T2I models, Q-Refine can perform effective optimization to AIGIs of different qualities. It can be a general refiner to optimize AIGIs from both fidelity and aesthetic quality levels, thus expanding the application of the T2I generation models.