# Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning

arXiv: https://arxiv.org/abs/1804.01681 · HTML: https://mlsanity.com/paper/1804.01681/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2018.

## Built on by (most cited first, 30 of 49)

- [MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2204.08958/) (2022)
- [TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment](https://mlsanity.com/paper/2308.03060/) (2023)
- [No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency](https://mlsanity.com/paper/2108.06858/) (2021)
- [Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment](https://mlsanity.com/paper/2108.07948/) (2021)
- [NTIRE 2022 Challenge on Perceptual Image Quality Assessment](https://mlsanity.com/paper/2206.11695/) (2022)
- [HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling](https://mlsanity.com/paper/2303.02700/) (2023)
- [Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model](https://mlsanity.com/paper/2207.04904/) (2022)
- [Quantifying Visual Image Quality: A Bayesian View](https://mlsanity.com/paper/2102.00195/) (2021)
- [No-Reference Image Quality Assessment by Hallucinating Pristine Features](https://mlsanity.com/paper/2108.04165/) (2021)
- [Video Quality Assessment: A Comprehensive Survey](https://mlsanity.com/paper/2412.04508/) (2024)
- [PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via Prompts](https://mlsanity.com/paper/2403.04993/) (2024)
- [DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wild](https://mlsanity.com/paper/2405.19996/) (2024)
- [Assessing a Single Image in Reference-Guided Image Synthesis](https://mlsanity.com/paper/2112.04163/) (2021)
- [SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction Tuning](https://mlsanity.com/paper/2411.10161/) (2024)
- [Real-World Image Super-Resolution by Exclusionary Dual-Learning](https://mlsanity.com/paper/2206.02609/) (2022)
- [An Overview on Generative AI at Scale with Edge-Cloud Computing](https://mlsanity.com/paper/2306.17170/) (2023)
- [Transformer-based No-Reference Image Quality Assessment via Supervised Contrastive Learning](https://mlsanity.com/paper/2312.06995/) (2023)
- [Advancing the Understanding and Evaluation of AR-Generated Scenes: When Vision-Language Models Shine and Stumble](https://mlsanity.com/paper/2501.13964/) (2025)
- [A survey on IQA](https://mlsanity.com/paper/2109.00347/) (2021)
- [REQA: Coarse-to-fine Assessment of Image Quality to Alleviate the Range Effect](https://mlsanity.com/paper/2209.01760/) (2022)
- [Blind Image Quality Assessment via Transformer Predicted Error Map and Perceptual Quality Token](https://mlsanity.com/paper/2305.09353/) (2023)
- [GMC-IQA: Exploiting Global-correlation and Mean-opinion Consistency for No-reference Image Quality Assessment](https://mlsanity.com/paper/2401.10511/) (2024)
- [AI-Generated Image Quality Assessment Based on Task-Specific Prompt and Multi-Granularity Similarity](https://mlsanity.com/paper/2411.16087/) (2024)
- [Optical Flow for Video Super-Resolution: A Survey](https://mlsanity.com/paper/2203.10462/) (2022)
- [You Only Train Once: A Unified Framework for Both Full-Reference and No-Reference Image Quality Assessment](https://mlsanity.com/paper/2310.09560/) (2023)
- [Stochasticity-aware No-Reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2401.08926/) (2024)
- [PAME: Self-Supervised Masked Autoencoder for No-Reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2403.10061/) (2024)
- [Deep Feature Statistics Mapping for Generalized Screen Content Image Quality Assessment](https://mlsanity.com/paper/2209.05321/) (2022)
- [UniForensics: Face Forgery Detection via General Facial Representation](https://mlsanity.com/paper/2407.19079/) (2024)
- [VTAMIQ: Transformers for Attention Modulated Image Quality Assessment](https://mlsanity.com/paper/2110.01655/) (2021)

## Abstract (arXiv)

No-reference image quality assessment (NR-IQA) is a fundamental yet challenging task in low-level computer vision community. The difficulty is particularly pronounced for the limited information, for which the corresponding reference for comparison is typically absent. Although various feature extraction mechanisms have been leveraged from natural scene statistics to deep neural networks in previous methods, the performance bottleneck still exists. In this work, we propose a hallucination-guided quality regression network to address the issue. We firstly generate a hallucinated reference constrained on the distorted image, to compensate the absence of the true reference. Then, we pair the information of hallucinated reference with the distorted image, and forward them to the regressor to learn the perceptual discrepancy with the guidance of an implicit ranking relationship within the generator, and therefore produce the precise quality prediction. To demonstrate the effectiveness of our approach, comprehensive experiments are evaluated on four popular image quality assessment benchmarks. Our method significantly outperforms all the previous state-of-the-art methods by large margins. The code and model will be publicly available on the project page https://kwanyeelin.github.io/projects/HIQA/HIQA.html.