ViDA-UGC: Detailed Image Quality Analysis via Visual Distortion Assessment for UGC Images
Wenjie Liao, Jieyu Yuan, Yifang Xu, Chunle Guo, Zilong Zhang, Jihong Li, Jiachen Fu, Haotian Fan, Tao Li, Junhui Cui, Chongyi Li

TL;DR
This paper introduces ViDA-UGC, a large-scale dataset and benchmark for detailed, explainable image quality assessment of user-generated content, leveraging human annotations and GPT-4o for fine-grained distortion analysis.
Contribution
It presents the first comprehensive UGC distortion assessment dataset and benchmark, enabling improved explainable IQA through a novel Chain-of-Thought framework and human-verified annotations.
Findings
ViDA-UGC improves image quality analysis across multiple models.
The Chain-of-Thought framework enhances distortion detection accuracy.
Models trained on ViDA-UGC outperform existing benchmarks.
Abstract
Recent advances in Multimodal Large Language Models (MLLMs) have introduced a paradigm shift for Image Quality Assessment (IQA) from unexplainable image quality scoring to explainable IQA, demonstrating practical applications like quality control and optimization guidance. However, current explainable IQA methods not only inadequately use the same distortion criteria to evaluate both User-Generated Content (UGC) and AI-Generated Content (AIGC) images, but also lack detailed quality analysis for monitoring image quality and guiding image restoration. In this study, we establish the first large-scale Visual Distortion Assessment Instruction Tuning Dataset for UGC images, termed ViDA-UGC, which comprises 11K images with fine-grained quality grounding, detailed quality perception, and reasoning quality description data. This dataset is constructed through a distortion-oriented pipeline,…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Advanced Vision and Imaging · Image Processing Techniques and Applications
