Image Retargeting for Small Display Devices

In this paper, we propose a novel image importance model for image retargeting. The most widely used image importance model in existing image retargeting methods is L-1-norm or L-2-norm of gradient magnitude. It works well under non-complex environment. However, the gradient magnitude based image importance model often leads to severe visual distortions when the scene is cluttered or the background is complex. In contrast to the most previous approaches, we focus on the excellence of gradient domain statistics (GDS) for more effective image retargeting rather than the gradient magnitude itself. In our work, the image retargeting is developed in the sense of human visual perception. We assume that the human visual perception is highly adaptive and sensitive to structural information in an image rather than non-structural information. We do not model the image structure explicitly since there are diverse aspects of image structure. Instead, our method obtains the structural information in an image by exploiting the gradient domain statistics in an implicit manner. Experimental results show that the proposed method is more effective than the previous image retargeting methods.