\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.

翻译：人工智能生成内容（AIGC）随着深度学习在内容创作中效率的不断提升而受到广泛关注。AIGC借助人工智能技术创建，包含多种内容形式，其中AI生成图像（AGIs）对社会产生了重大影响，并已应用于娱乐、教育、社交媒体等多个领域。然而，受硬件条件和技术水平限制，AIGC图像的质量参差不齐，在实际应用前需要进行优化和筛选。因此，亟需开发客观模型以评估AGIs的质量。遗憾的是，目前尚无专门针对AGIs感知质量评估的研究。为此，本文首先探讨了AGI质量评估的关键维度，包括技术问题、AI伪影、非自然性、差异性和美学表现。随后，我们建立了首个感知AGI质量评估数据库AGIQA-1K，包含由扩散模型生成的1080幅AGIs。在此基础上，我们组织了结构化的主观实验以收集AGIs的质量标签。最后，通过基准实验评估了现有图像质量评估（IQA）模型的性能。