  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.
