The proliferation of Artificial Intelligence-Generated Images (AGIs) has greatly expanded the Image Naturalness Assessment (INA) problem. Different from early definitions that mainly focus on tone-mapped images with limited distortions (e.g., exposure, contrast, and color reproduction), INA on AI-generated images is especially challenging as it has more diverse contents and could be affected by factors from multiple perspectives, including low-level technical distortions and high-level rationality distortions. In this paper, we take the first step to benchmark and assess the visual naturalness of AI-generated images. First, we construct the AI-Generated Image Naturalness (AGIN) database by conducting a large-scale subjective study to collect human opinions on the overall naturalness as well as perceptions from technical and rationality perspectives. AGIN verifies that naturalness is universally and disparately affected by both technical and rationality distortions. Second, we propose the Joint Objective Image Naturalness evaluaTor (JOINT), to automatically learn the naturalness of AGIs that aligns human ratings. Specifically, JOINT imitates human reasoning in naturalness evaluation by jointly learning both technical and rationality perspectives. Experimental results show our proposed JOINT significantly surpasses baselines for providing more subjectively consistent results on naturalness assessment. Our database and code will be released in https://github.com/zijianchen98/AGIN.

翻译：人工智能生成图像（AGI）的激增极大地扩展了图像自然性评估（INA）问题。与早期主要关注存在有限失真（如曝光、对比度和色彩还原）的色调映射图像的定义不同，AI生成图像的INA尤其具有挑战性，因为其内容更加多样，且可能受到来自多个层面的因素影响，包括低层次技术失真和高层次合理性失真。本文率先对AI生成图像的视觉自然性进行基准测试与评估。首先，我们通过开展大规模主观研究，从整体自然性以及技术和合理性两个感知维度收集人类意见，构建了AI生成图像自然性（AGIN）数据库。AGIN验证了自然性普遍且差异性地受到技术和合理性失真的影响。其次，我们提出联合客观图像自然性评估器（JOINT），以自动学习与人类评分一致的AGI自然性。具体而言，JOINT通过联合学习技术和合理性两个维度，模拟人类在自然性评估中的推理过程。实验结果表明，我们提出的JOINT在自然性评估上显著超越基线方法，能够提供更具主观一致性的结果。我们的数据库和代码将在https://github.com/zijianchen98/AGIN 发布。