Exploring the Naturalness of AI-Generated Images

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.Comment: 33 page

Study of naturalness in tone-mapped images

International audienceNowadays, images can be obtained in various ways such as capturing photos in single-exposure mode, applying Multiple Exposure Fusion algorithms to generate an image from multiple shoots of the same scene, mapping High Dynamic Range images to Standard Dynamic Range (SDR) images, converting raw formats to displayable formats, or applying post-processing techniques to enhance image quality, aesthetic quality,.. . When looking at some photos, one might have a feeling of unnaturalness. This paper deals with the problem of developing a model firstly to estimate if an image looks natural or not to humans and the second purpose is to try to understand how the unnaturalness feeling is induced by a photo: Are there specific unnaturalness clues or is unnaturalness a general feeling when looking at a photo? The study focuses on SDR images, especially on tone-mapped images. The first contribution of the paper is the setting of an experiment gathering human naturalness opinions on 1,900 SDR images mainly obtained from tone mapping operators. Based on the collected data, the second contribution of the paper is to study the efficiency of different feature types including handcrafted features and learned features for image naturalness analysis. A binary classification model is then developed based on the determined features to classify if an image looks natural or unnatural