构建首个AI增强用户生成内容质量评估基准数据集。
AU-IQA: A Benchmark Dataset for Perceptual Quality Assessment of AI-Enhanced User-Generated Content
- 创建4800张融合三类AI增强的UGC图像数据集
- 发现现有评估模型在AI-UGC上表现显著下降
- 适合图像质量评估与AI增强算法研究者使用
基于AI的图像增强技术已广泛应用于各类视觉场景，显著提升了用户生成内容（UGC）的感知质量。然而，专用的质量评估模型缺失已成为该领域的重要瓶颈，制约用户体验并阻碍增强方法的发展。尽管现有的感知质量评估方法在UGC和AIGC上表现良好，但针对融合两者特征的AI增强型UGC（AI-UGC）的评估效果仍缺乏研究。为此，我们构建了AU-IQA基准数据集，包含4800张由超分辨率、低光增强和去噪三类典型增强方法生成的AI-UGC图像。在此基础上，我们评估了多种现有质量评估模型，包括传统IQA方法和大型多模态模型，并系统分析了当前方法在评估AI-UGC感知质量上的有效性。数据集开源地址：https://github.com/WNNGGU/AU-IQA-Dataset。
原文摘要 · Abstract (English)
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.
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