Content-aware Image Retargeting Based on Visual Effect Assessment

Content-aware image retargeting has drawn much attention in image and vision research in recent years. However, existing methods are very difficult to ensure that the result images from retargeting achieve good visual effect on the whole, since these methods mainly focus on spatial image information. In this paper, we propose a new approach on content-aware image retargeting based on visual effect assessment. We establish an evaluation mechanism of the visual effects of retargeted images which is based on a priori statistical knowledge through studying the user's evaluation, and build the computable model of visual effect assessment of retargeted image with the help of mathematical description from Dynamic Bayesian Networks. After finishing contentaware processing and construct a three-level model of visual saliency contents for the original image, we retarget the original image into the target image by virtue of deforming image, and integrate computable model of visual effect assessment into retargeting process, so as to guide the retargeting. Finally, by steadily adjusting the size of the intermediate results from deforming image, we make this size be eventually equal to the size of the target image of retargeting, under the constraint that the result image should acquire good visual effect through optimizing the parameters of visual effect assessment. Real data have been used to test the proposed approach and very good results have been achieved, validating it.