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Computer Science > Computer Vision and Pattern Recognition

arXiv:2312.05476 (cs)
[Submitted on 9 Dec 2023 (v1), last revised 4 Mar 2024 (this version, v3)]

Title:Exploring the Naturalness of AI-Generated Images

Authors:Zijian Chen, Wei Sun, Haoning Wu, Zicheng Zhang, Jun Jia, Zhongpeng Ji, Fengyu Sun, Shangling Jui, Xiongkuo Min, Guangtao Zhai, Wenjun Zhang
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Abstract: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 technical and rationality distortions. Second, we propose the Joint Objective Image Naturalness evaluaTor (JOINT), to automatically predict the naturalness of AGIs that aligns human ratings. Specifically, JOINT imitates human reasoning in naturalness evaluation by jointly learning both technical and rationality features. We demonstrate that JOINT significantly outperforms baselines for providing more subjectively consistent results on naturalness assessment.
Comments: 33 pages
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2312.05476 [cs.CV]
  (or arXiv:2312.05476v3 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2312.05476

Submission history

From: Zijian Chen [view email]
[v1] Sat, 9 Dec 2023 06:08:09 UTC (19,841 KB)
[v2] Thu, 14 Dec 2023 15:46:05 UTC (19,841 KB)
[v3] Mon, 4 Mar 2024 13:30:44 UTC (22,851 KB)
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