AU-IQA: A Benchmark Dataset for Perceptual Quality Assessment of AI-Enhanced User-Generated Content

TL;DR

This paper introduces AU-IQA, a new benchmark dataset of 4,800 AI-enhanced user-generated images, to evaluate and improve perceptual quality assessment models for AI-UGC, addressing a significant gap in current research.

Contribution

The paper presents AU-IQA, the first dedicated dataset for AI-UGC quality assessment, and evaluates existing models' performance on this new benchmark.

Findings

Traditional IQA models perform poorly on AI-UGC.

Large multimodal models show promising results but need further improvement.

The dataset reveals gaps in current perceptual quality assessment methods.

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.