  \underline{AI} \underline{G}enerated \underline{C}ontent (\textbf{AIGC}) has
gained widespread attention with the increasing efficiency of deep learning in
content creation. AIGC, created with the assistance of artificial intelligence
technology, includes various forms of content, among which the AI-generated
images (AGIs) have brought significant impact to society and have been applied
to various fields such as entertainment, education, social media, etc. However,
due to hardware limitations and technical proficiency, the quality of AIGC
images (AGIs) varies, necessitating refinement and filtering before practical
use. Consequently, there is an urgent need for developing objective models to
assess the quality of AGIs. Unfortunately, no research has been carried out to
investigate the perceptual quality assessment for AGIs specifically. Therefore,
in this paper, we first discuss the major evaluation aspects such as technical
issues, AI artifacts, unnaturalness, discrepancy, and aesthetics for AGI
quality assessment. Then we present the first perceptual AGI quality assessment
database, AGIQA-1K, which consists of 1,080 AGIs generated from diffusion
models. A well-organized subjective experiment is followed to collect the
quality labels of the AGIs. Finally, we conduct a benchmark experiment to
evaluate the performance of current image quality assessment (IQA) models.
