AU-IQA: A Benchmark Dataset for Perceptual Quality Assessment of AI-Enhanced User-Generated Content
- URL: http://arxiv.org/abs/2508.05016v1
- Date: Thu, 07 Aug 2025 03:55:11 GMT
- Title: AU-IQA: A Benchmark Dataset for Perceptual Quality Assessment of AI-Enhanced User-Generated Content
- Authors: Shushi Wang, Chunyi Li, Zicheng Zhang, Han Zhou, Wei Dong, Jun Chen, Guangtao Zhai, Xiaohong Liu,
- Abstract summary: AI-based image enhancement techniques have been widely adopted in various visual applications, significantly improving the perceptual quality of user-generated content (UGC)<n>The lack of specialized quality assessment models has become a significant limiting factor in this field, limiting user experience and hindering the advancement of enhancement methods.<n>We construct AU-IQA, a benchmark dataset comprising 4,800 AI-UGC images produced by three representative enhancement types.<n>On this dataset, we evaluate a range of existing quality assessment models, including traditional IQA methods and large multimodal models.
- Score: 43.82962694838953
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: AI-based image enhancement techniques have been widely adopted in various visual applications, significantly improving the perceptual quality of user-generated content (UGC). However, the lack of specialized quality assessment models has become a significant limiting factor in this field, limiting user experience and hindering the advancement of enhancement methods. While perceptual quality assessment methods have shown strong performance on UGC and AIGC individually, their effectiveness on AI-enhanced UGC (AI-UGC) which blends features from both, remains largely unexplored. To address this gap, we construct AU-IQA, a benchmark dataset comprising 4,800 AI-UGC images produced by three representative enhancement types which include super-resolution, low-light enhancement, and denoising. On this dataset, we further evaluate a range of existing quality assessment models, including traditional IQA methods and large multimodal models. Finally, we provide a comprehensive analysis of how well current approaches perform in assessing the perceptual quality of AI-UGC. The access link to the AU-IQA is https://github.com/WNNGGU/AU-IQA-Dataset.
Related papers
- TRIQA: Image Quality Assessment by Contrastive Pretraining on Ordered Distortion Triplets [31.2422359004089]
No-Reference (NR) IQA remains particularly challenging due to the absence of a reference image.<n>We propose a novel approach that constructs a custom dataset using a limited number of reference content images.<n>We train a quality-aware model using contrastive triplet-based learning, enabling efficient training with fewer samples.
arXiv Detail & Related papers (2025-07-16T23:43:12Z) - Quality Assessment for AI Generated Images with Instruction Tuning [58.41087653543607]
We first establish a novel Image Quality Assessment (IQA) database for AIGIs, termed AIGCIQA2023+.<n>This paper presents a MINT-IQA model to evaluate and explain human preferences for AIGIs from Multi-perspectives with INstruction Tuning.
arXiv Detail & Related papers (2024-05-12T17:45:11Z) - PKU-AIGIQA-4K: A Perceptual Quality Assessment Database for Both Text-to-Image and Image-to-Image AI-Generated Images [1.5265677582796984]
We establish a large scale perceptual quality assessment database for both text-to-image and image-to-image AIGIs, named PKU-AIGIQA-4K.
We propose three image quality assessment (IQA) methods based on pre-trained models that include a no-reference method NR-AIGCIQA, a full-reference method FR-AIGCIQA, and a partial-reference method PR-AIGCIQA.
arXiv Detail & Related papers (2024-04-29T03:57:43Z) - Large Multi-modality Model Assisted AI-Generated Image Quality Assessment [53.182136445844904]
We introduce a large Multi-modality model Assisted AI-Generated Image Quality Assessment (MA-AGIQA) model.
It uses semantically informed guidance to sense semantic information and extract semantic vectors through carefully designed text prompts.
It achieves state-of-the-art performance, and demonstrates its superior generalization capabilities on assessing the quality of AI-generated images.
arXiv Detail & Related papers (2024-04-27T02:40:36Z) - Multi-Modal Prompt Learning on Blind Image Quality Assessment [65.0676908930946]
Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly.
Traditional methods, hindered by a lack of sufficiently annotated data, have employed the CLIP image-text pretraining model as their backbone to gain semantic awareness.
Recent approaches have attempted to address this mismatch using prompt technology, but these solutions have shortcomings.
This paper introduces an innovative multi-modal prompt-based methodology for IQA.
arXiv Detail & Related papers (2024-04-23T11:45:32Z) - AIGCOIQA2024: Perceptual Quality Assessment of AI Generated Omnidirectional Images [70.42666704072964]
We establish a large-scale AI generated omnidirectional image IQA database named AIGCOIQA2024.
A subjective IQA experiment is conducted to assess human visual preferences from three perspectives.
We conduct a benchmark experiment to evaluate the performance of state-of-the-art IQA models on our database.
arXiv Detail & Related papers (2024-04-01T10:08:23Z) - AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment [62.8834581626703]
We build the most comprehensive subjective quality database AGIQA-3K so far.
We conduct a benchmark experiment on this database to evaluate the consistency between the current Image Quality Assessment (IQA) model and human perception.
We believe that the fine-grained subjective scores in AGIQA-3K will inspire subsequent AGI quality models to fit human subjective perception mechanisms.
arXiv Detail & Related papers (2023-06-07T18:28:21Z) - A Perceptual Quality Assessment Exploration for AIGC Images [39.72512063793346]
In this paper, we discuss the major evaluation aspects such as technical issues, AI artifacts, unnaturalness, discrepancy, and aesthetics for AGI quality assessment.
We present the first perceptual AGI quality assessment database, AGIQA-1K, which consists of 1,080 AGIs generated from diffusion models.
arXiv Detail & Related papers (2023-03-22T14:59:49Z) - Generating Adversarial Examples with an Optimized Quality [12.747258403133035]
Deep learning models are vulnerable to Adversarial Examples (AEs),carefully crafted samples to deceive those models.
Recent studies have introduced new adversarial attack methods, but none provided guaranteed quality for the crafted examples.
In this paper, we incorporateImage Quality Assessment (IQA) metrics into the design and generation process of AEs.
arXiv Detail & Related papers (2020-06-30T23:05:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.