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 this https URL.
Abstract (translated)
基于AI的图像增强技术已在各种视觉应用中广泛采用，显著提高了用户生成内容（UGC）的感知质量。然而，缺乏专门的质量评估模型已成为该领域的一个重要限制因素，这不仅影响用户体验，还阻碍了增强方法的进步。虽然感知质量评估方法在单独处理UGC和AIGC时表现出色，但对于结合两者特征的AI增强UGC（AI-UGC），其有效性尚未得到充分研究。 为了解决这一空白，我们构建了一个名为AU-IQA的基准数据集，该数据集包含由三种代表性增强类型——超分辨率、低光增强及去噪产生的4,800张AI-UGC图像。在此数据集上，我们进一步评估了一系列现有的质量评估模型，包括传统的IQA方法和大型多模态模型。最后，我们提供了当前方法在评估AI-UGC感知质量方面的全面分析。 访问AU-IQA的数据集链接：[请参阅原文中的链接](https://example.com)（请替换为实际提供的URL）。
URL
https://arxiv.org/abs/2508.05016
