Tensor based approach for inpainting of video containing sparse text

Videos received from certain sources may contain irrelevant contents which might reduce the amount of information conveyed by it. This paper proposes an effective tensor based video inpainting approach to improve the quality of these videos by removing and replacing unwanted contents with relevant i...

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Bibliographic Details
Published in:Multimedia tools and applications 2019, Vol.78 (2), p.1805-1829
Main Authors: M, Baburaj, George, Sudhish N.
Format: Article
Language:English
Subjects:
Algorithms
Cameras
Computer Communication Networks
Computer Science
Consistency
Data Structures and Information Theory
Decomposition
Labeling
Methods
Multimedia Information Systems
Principal components analysis
Special Purpose and Application-Based Systems
Video data
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Summary:Videos received from certain sources may contain irrelevant contents which might reduce the amount of information conveyed by it. This paper proposes an effective tensor based video inpainting approach to improve the quality of these videos by removing and replacing unwanted contents with relevant information. The proposed method employs reweighted tensor decomposition technique to identify and discard the inappropriate sparse components of the video data. These sparse components are substituted with proper contents by utilising spatio-temporal consistency through reweighted tensor completion. The replacement is carried out in such a way that the resulting video possesses superior temporal consistency and visual credibility. The proposed method is applied to sparse text removal of videos having dynamic content in various extents and is found that our method outperforms its counterparts.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-018-6251-7