Quality Evaluation of AI-Generated Images: Subjective Study and Objective Methodology

In recent years, AI-Generated Images (AIGIs) have attracted significant attention and shown great potential in various applications, including entertainment, advertisement, education, and product design. Driven by this trend, various Text-to-Image (T2I) models are developed. However, the quality of AIGIs produced by these models varies widely, with many low-quality images failing to meet human aesthetic standards. Consequently, research into both subjective and objective Image Quality Assessment (IQA) methods for AIGIs is crucial. In this paper, we introduce a dataset called AIGI-IQAD, designed to enhance our understanding of human aesthetic preferences for AIGIs. The dataset contains 2,880 AIGIs generated by 8 T2I models using 360 deliberately designed text prompts. Further, we conducted subjective experiments to gather ratings from both aesthetic quality and text-image consistency. Building on this dataset, we propose a model named Question-guided Multimodal Interaction Network (QMI-Net) for evaluating AIGIs. QMI-Net assesses human preferences for AIGIs by focusing on both aesthetic quality and text-image consistency. Specifically, QMI-Net uses a question-answering approach to guide Multimodal Large Language Models (MLLMs) in generating detailed aesthetic and similarity information. The Visual and Aesthetic Feature Fusion Module (VAFFM) then fuses the aesthetic features with the visual features extracted by Contrastive Language-Image Pre-training (CLIP) to obtain more comprehensive aesthetic quality features. Comprehensive experiments demonstrate that state-of-the-art performance is achieved by QMI-Net on our AIGI-IQAD and three other public datasets.The AIGI-IQAD datasets and QMI-Net will be released at https://github.com/ctxya1207/QMI-Net.