A Novel Video Stabilization Model with Motion Morphological Component Priors
- Authors
- Wu, H.[Wu, H.]; Xiao, L.[Xiao, L.]; Sun, L.[Sun, L.]; Jeon, B.[Jeon, B.]
- Issue Date
- Nov-2021
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Adaptive weight; Cameras; Hybrid fiber coaxial cables; motion morphological component decomposition; Motion segmentation; rapid motion; Smoothing methods; Three-dimensional displays; Trajectory; video stabilization; Visualization
- Citation
- IEEE Transactions on Multimedia, v.25, pp.1 - 1
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE Transactions on Multimedia
- Volume
- 25
- Start Page
- 1
- End Page
- 1
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/92675
- DOI
- 10.1109/TMM.2021.3126934
- ISSN
- 1520-9210
- 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. IEEE
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Collections - Information and Communication Engineering > School of Electronic and Electrical Engineering > 1. Journal Articles
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