Abstract
\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.
Abstract (translated)
随着深度学习在内容创建中的效率提高,AIGC(AI生成内容)已经引起了广泛关注。AIGC是由人工智能技术协助创建的,包括各种形式的内容,其中AI生成的图像(AGI)对社会影响非常大,已经应用于娱乐、教育、社交媒体等领域。然而,由于硬件限制和技术水平,AIGC图像(AGI)的质量 vary,需要在实际应用前进行精化和过滤。因此,迫切需要开发 objective 模型来评估 AGI 的质量。不幸的是,尚未有任何研究专门研究对 AGI 的视觉质量评估。因此,在本文中,我们首先讨论了主要的评估方面,如技术问题、AI 工具包、自然ness、差异和美学,以及对 AGI 质量评估的技术。然后,我们介绍了第一个对 AGI 的视觉质量评估数据库,AGIQA-1K,它由扩散模型生成的 1,080 个 AGI 组成。一个组织良好的主观实验随后用于收集 AGI 的质量标签。最后,我们进行了一个基准实验来评估当前图像质量评估(IQA)模型的性能。
URL
https://arxiv.org/abs/2303.12618
