Scalable and Coherent Video Resizing with Per-Frame Optimization

Yu-Shuen Wang1,2, Jen-Hung Hsiao2, Olga Sorkine3,4, Tong-Yee Lee2

ACM Transactions on Graphics (Proceedings of SIGGRAPH 2011) Vol. 30, No.4, Aug 2011

1National Chiao Tung University, Taiwan   2National Cheng Kung University, Taiwan   3New York University   4ETH Zurich

We introduce a scalable content-aware video retargeting method. Here, we render pairs of original and deformed motion trajectories in red and blue. Making the relative transformation of such pathlines consistent ensures temporal coherence of the resized video.



The key to high-quality video resizing is preserving the shape and motion of visually salient objects while remaining temporally-coherent. These spatial and temporal requirements are difficult to reconcile, typically leading existing video retargeting methods to sacrifice one of them and causing distortion or waving artifacts. Recent work enforces temporal coherence of content-aware video warping by solving a global optimization problem over the entire video cube. This significantly improves the results but does not scale well with the resolution and length of the input video and quickly becomes intractable. We propose a new method that solves the scalability problem without compromising the resizing quality. Our method factors the problem into spatial and time/motion components: we first resize each frame independently to preserve the shape of salient regions, and then we optimize their motion using a reduced model for each pathline of the optical flow. This factorization decomposes the optimization of the video cube into sets of sub-problems whose size is proportional to a single frame's resolution and which can be solved in parallel. We also show how to incorporate cropping into our optimization, which is useful for scenes with numerous salient objects where warping alone would degenerate to linear scaling. Our results match the quality of state-of-the-art retargeting methods while dramatically reducing the computation time and memory consumption, making content-aware video resizing scalable and practical.



SVR_main.mp4,    SVR_supp.wmv


   author = {Yu-Shuen Wang, Jen-Hung Hsiao, Olga Sorkine and Tong-Yee Lee},
   title = {Scalable and Coherent Video Resizing with Per-Frame Optimization},
   journal = {ACM Trans. \Graph. \(Proceedings of ACM SIGGRAPH)},
   year = {2011},
   volume = {30},
   number = {4},


We thank the anonymous reviewers for their constructive comments. We are also grateful to Annie Ytterberg for narrating the accompanying video and to Christa C. Y. Chen for her help with the video materials and licensing. The usage of the video clips is permitted by ARS Film Production, Blender Foundation and MAMMOTH HD. This work was supported in part by the Landmark Program of the NCKU Top University Project (contract B0008) and by an NYU URCF grant.