Gradient domain statistical image-importance model for content-aware image resizing

Abstract

We propose a novel image-importance model for content-aware image resizing. In contrast to the previous gradient magnitude-based approaches, we focus on the excellence of gradient domain statistics. The proposed scheme originates from a well-known property of the human visual system that the human visual perception is highly adaptive and sensitive to structural information in images rather than nonstructural information. We do not model the image structure explicitly, because there are diverse aspects of image structure and they cannot be easily modeled from cluttered natural images. Instead, our method obtains the structural information in an image by exploiting the gradient domain statistics in an implicit manner. Extensive tests on a variety of cluttered natural images show that the proposed method is more effective than the previous content-aware image-resizing methods and it is very robust to images with a cluttered background, unlike the previous schemes.