Shaking video synthesis for video stabilization performance assessment

The goal of video stabilization is to remove the unwanted camera motion and obtain stable versions. Theoretically, a good stabilization algorithm should remove the unwanted motion without the loss of image qualities. However, due to the lack of ground-truth video frames, the accurate performance evaluation of different algorithms is hard. Most existing evaluation techniques usually synthesize stable videos from shaking ones, but they are not effective enough. Different from previous methods, in this paper we propose a novel method which synthesize shaking videos from stable frames. Based on the synthetic shaking videos, we perform preliminary video stabilization performance assessment on three stabilization algorithms. Our shaking video synthesis method can not only give a benchmark for full-reference video stabilization performance assessment, but also provide a basis for exploring the theoretical bound of video stabilization which may help to improve existing stabilization algorithms.

[1]  Raanan Fattal,et al.  Video stabilization using epipolar geometry , 2012, TOGS.

[2]  Djemel Ziou,et al.  Image Quality Metrics: PSNR vs. SSIM , 2010, 2010 20th International Conference on Pattern Recognition.

[3]  Peyman Milanfar,et al.  Is Denoising Dead? , 2010, IEEE Transactions on Image Processing.

[4]  Rama Chellappa,et al.  Evaluation of image stabilization algorithms , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[5]  Peyman Milanfar,et al.  Practical Bounds on Image Denoising: From Estimation to Information , 2011, IEEE Transactions on Image Processing.

[6]  M. Rezaei,et al.  Camera Motion Modeling for Video Stabilization Performance Assessment , 2011, 2011 7th Iranian Conference on Machine Vision and Image Processing.

[7]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[8]  Li Song,et al.  Video stabilization with L1–L2 optimization , 2013, 2013 IEEE International Conference on Image Processing.

[9]  Xiaokang Yang,et al.  New bounds on image denoising: Viewpoint of sparse representation and non-local averaging , 2012, 2012 Visual Communications and Image Processing.

[10]  Anat Levin,et al.  Natural image denoising: Optimality and inherent bounds , 2011, CVPR 2011.

[11]  Markus Borschbach,et al.  An approach towards a full-reference-based benchmarking for quality-optimized endoscopic video stabilization systems , 2012, ICVGIP '12.

[12]  Chao Zhang,et al.  Qualitative Assessment of Video Stabilization and Mosaicking Systems , 2008, 2008 IEEE Workshop on Applications of Computer Vision.

[13]  Michael Gleicher,et al.  Subspace video stabilization , 2011, TOGS.

[14]  Irfan A. Essa,et al.  Auto-directed video stabilization with robust L1 optimal camera paths , 2011, CVPR 2011.