Quantifying image naturalness using differential curvelet features

Distinguishing computer-generated (CG) images from natural images is an effortless job for the human eye but not for a machine. Artificial or CG image processing services are growing rapidly due to popular smartphone applications and filters in social media applications. The traditional image quality assessment (IQA) metrics are mostly defined for real-world images in terms of attributes describing noises and distortions. In contrast with traditional natural image content, artificial or CG image content has special characteristics that differentiate them from natural images. This difference opens new opportunities of research towards designing metrics that define 'naturalness' in terms of image attributes. In this paper, we investigate how curvelet features of a natural image can represent naturalness of the image. By training various classifiers using differential curvelet features of artwork-like images, we report a novel approach to estimate image naturalness and its performance for various levels of naturalness. The reported results are promising and show potential of improvement using detailed feature engineering.