# HarmonyIQA: Pioneering Benchmark and Model for Image Harmonization Quality Assessment

arXiv: https://arxiv.org/abs/2501.01116 · HTML: https://mlsanity.com/paper/2501.01116/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 62 references that are in the map.
- First posted on arXiv 2 Jan 2025.

## Builds on (most cited first, 30 of 32)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks](https://mlsanity.com/paper/2312.14238/) (2023)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models](https://mlsanity.com/paper/2401.06066/) (2024)
- [mPLUG-Owl2: Revolutionizing Multi-modal Large Language Model with Modality Collaboration](https://mlsanity.com/paper/2311.04257/) (2023)
- [MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2204.08958/) (2022)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment](https://mlsanity.com/paper/2308.03060/) (2023)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [DoveNet: Deep Image Harmonization via Domain Verification](https://mlsanity.com/paper/1911.13239/) (2019)
- [ARNIQA: Learning Distortion Manifold for Image Quality Assessment](https://mlsanity.com/paper/2310.14918/) (2023)
- [Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network](https://mlsanity.com/paper/2204.10485/) (2022)
- [High-Resolution Image Harmonization via Collaborative Dual Transformations](https://mlsanity.com/paper/2109.06671/) (2021)
- [Shift-tolerant Perceptual Similarity Metric](https://mlsanity.com/paper/2207.13686/) (2022)
- [Perceptual Video Quality Assessment: A Survey](https://mlsanity.com/paper/2402.03413/) (2024)
- [Confusing Image Quality Assessment: Towards Better Augmented Reality Experience](https://mlsanity.com/paper/2204.04900/) (2022)
- [FineVQ: Fine-Grained User Generated Content Video Quality Assessment](https://mlsanity.com/paper/2412.19238/) (2024)
- [Large Multi-modality Model Assisted AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2404.17762/) (2024)
- [AIGV-Assessor: Benchmarking and Evaluating the Perceptual Quality of Text-to-Video Generation with LMM](https://mlsanity.com/paper/2411.17221/) (2024)
- [AIGCOIQA2024: Perceptual Quality Assessment of AI Generated Omnidirectional Images](https://mlsanity.com/paper/2404.01024/) (2024)
- [Quality Assessment for AI Generated Images with Instruction Tuning](https://mlsanity.com/paper/2405.07346/) (2024)
- [Painterly Image Harmonization using Diffusion Model](https://mlsanity.com/paper/2308.02228/) (2023)
- [Deep Image Harmonization in Dual Color Spaces](https://mlsanity.com/paper/2308.02813/) (2023)
- [Deep Image-based Illumination Harmonization](https://mlsanity.com/paper/2108.00150/) (2021)
- [Painterly Image Harmonization in Dual Domains](https://mlsanity.com/paper/2212.08846/) (2022)

## Built on by (most cited first, 7 of 7)

- [LOVE: Benchmarking and Evaluating Text-to-Video Generation and Video-to-Text Interpretation](https://mlsanity.com/paper/2505.12098/) (2025)
- [DFBench: Benchmarking Deepfake Image Detection Capability of Large Multimodal Models](https://mlsanity.com/paper/2506.03007/) (2025)
- [ManipShield: A Unified Framework for Image Manipulation Detection, Localization and Explanation](https://mlsanity.com/paper/2511.14259/) (2025)
- [EditHF-1M: A Million-Scale Rich Human Preference Feedback for Image Editing](https://mlsanity.com/paper/2603.14916/) (2026)
- [EditRefiner: A Human-Aligned Agentic Framework for Image Editing Refinement](https://mlsanity.com/paper/2605.07457/) (2026)
- [ITIScore: An Image-to-Text-to-Image Rating Framework for the Image Captioning Ability of MLLMs](https://mlsanity.com/paper/2604.03765/) (2026)
- [Harnessing Vision-Language Models for Perceptual Quality Assessment and Autonomous Content Adjustment in Augmented Reality](https://mlsanity.com/paper/2610.00677/) (2026)

## Abstract (arXiv)

Image composition involves extracting a foreground object from one image and pasting it into another image through Image harmonization algorithms (IHAs), which aim to adjust the appearance of the foreground object to better match the background. Existing image quality assessment (IQA) methods may fail to align with human visual preference on image harmonization due to the insensitivity to minor color or light inconsistency. To address the issue and facilitate the advancement of IHAs, we introduce the first Image Quality Assessment Database for image Harmony evaluation (HarmonyIQAD), which consists of 1,350 harmonized images generated by 9 different IHAs, and the corresponding human visual preference scores. Based on this database, we propose a Harmony Image Quality Assessment (HarmonyIQA), to predict human visual preference for harmonized images. Extensive experiments show that HarmonyIQA achieves state-of-the-art performance on human visual preference evaluation for harmonized images, and also achieves competing results on traditional IQA tasks. Furthermore, cross-dataset evaluation also shows that HarmonyIQA exhibits better generalization ability than self-supervised learning-based IQA methods. Both HarmonyIQAD and HarmonyIQA will be made publicly available upon paper publication.