  With the rapid advancements of the text-to-image generative model,
AI-generated images (AGIs) have been widely applied to entertainment,
education, social media, etc. However, considering the large quality variance
among different AGIs, there is an urgent need for quality models that are
consistent with human subjective ratings. To address this issue, we extensively
consider various popular AGI models, generated AGI through different prompts
and model parameters, and collected subjective scores at the perceptual quality
and text-to-image alignment, thus building the most comprehensive AGI
subjective quality database AGIQA-3K so far. Furthermore, we conduct a
benchmark experiment on this database to evaluate the consistency between the
current Image Quality Assessment (IQA) model and human perception, while
proposing StairReward that significantly improves the assessment performance of
subjective text-to-image alignment. We believe that the fine-grained subjective
scores in AGIQA-3K will inspire subsequent AGI quality models to fit human
subjective perception mechanisms at both perception and alignment levels and to
optimize the generation result of future AGI models. The database is released
on https://github.com/lcysyzxdxc/AGIQA-3k-Database.
