A Novel Video Stabilization Model With Motion Morphological Component Priors

Abstract

Video stabilization is the process of improving the video quality by removing annoying fluctuant motion caused by camera jittering. A key issue of a successful solution is the temporal adaptability to motion and the overall robustness with respect to different motion types. However, most previous methods usually produce non-motion adaptive stabilized videos. In other words, under-smoothing in slow motion segments and over-smoothing in rapid motion segments will be produced for complex shaky videos. To overcome these drawbacks, we propose a novel video stabilization approach using a motion morphological component (MMC) decomposition. Specifically, the observed motion is decomposed into three MMCs: low-frequency smoothed (LFS) motion, high-frequency compensatory (HFC) motion, and shaky motion. LFS motion helps to largely stabilize videos, and HFC motion helps to recover missing motion to deal with over-smoothing. Subsequently, we present an MMC-based model to retrieve the desired smoothed motion, in which weighted nuclear norm and autoregression priors are used for LFS motion, while a sparsity prior is adopted for HFC motion. In addition, we design an adaptive weight setting scheme to detect rapid motions and to calculate the optimal weights. Finally, we develop a stabilization algorithm under the Alternating Direction Method of Multipliers (ADMM) framework. Experimental results demonstrate that our method can achieve high-quality results compared with that of other state-of-the-art stabilization methods in terms of robustness and efficiency, both quantitatively and qualitatively.