Efficient Scale-Space Spatiotemporal Saliency Tracking for Distortion-Free Video Retargeting

Video retargeting aims at transforming an existing video in order to display it appropriately on a target device, often in a lower resolution, such as a mobile phone. To preserve a viewer’s experience, it is desired to keep the important regions in their original aspect ratio, i.e., to maintain them distortionfree. Most previous methods are susceptible to geometric distortions due to the anisotropic manipulation of image pixels. In this paper, we propose a novel approach to distortion-free video retargeting by scale-space spatiotemporal saliency tracking. An optimal source cropping window with the target aspect ratio is smoothly tracked over time, and then isotropically resized to the retargeted display. The problem is cast as the task of finding the most spatiotemporally salient cropping window with minimal information loss due to resizing. We conduct the spatiotemporal saliency analysis in scale-space to better account for the effect of resizing. By leveraging integral images, we develop an efficient coarse-to-fine solution that combines exhaustive coarse and gradient-based fine search, which we term scale-space spatiotemporal saliency tracking. Experiments on real-world videos and our user study demonstrate the efficacy of the proposed approach.