Multisource surveillance video data coding with hierarchical knowledge library

The rapidly increasing surveillance video data has challenged the existing video coding standards. Even though knowledge based video coding scheme has been proposed to remove redundancy of moving objects across multiple videos and achieved great coding efficiency improvement, it still has difficulti...

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Bibliographic Details
Published in:Multimedia tools and applications Vol. 78; no. 11; pp. 14705 - 14731
Main Authors: Chen, Yu, Hu, Ruimin, Xiao, Jing, Xu, Liang, Wang, Zhongyuan
Format: Journal Article
Language:English
Published: New York Springer US 01.06.2019
Springer Nature B.V
Subjects:
Coding
Coding standards
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Knowledge
Multimedia Information Systems
Redundancy
Special Purpose and Application-Based Systems
Surveillance
Video data
Redundancy removal
Hybrid prediction
Surveillance video data
Visual changes
Hierarchical knowledge extraction
ISSN:1380-7501, 1573-7721
Online Access:Get full text
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Summary:The rapidly increasing surveillance video data has challenged the existing video coding standards. Even though knowledge based video coding scheme has been proposed to remove redundancy of moving objects across multiple videos and achieved great coding efficiency improvement, it still has difficulties to cope with complicated visual changes of objects resulting from various factors. In this paper, a novel hierarchical knowledge extraction method is proposed. Common knowledge on three coarse-to-fine levels, namely category level, object level and video level, are extracted from history data to model the initial appearance, stable changes and temporal changes respectively for better object representation and redundancy removal. In addition, we apply the extracted hierarchical knowledge to surveillance video coding tasks and establish a hybrid prediction based coding framework. On the one hand, hierarchical knowledge is projected to the image plane to generate reference for I frames to achieve better prediction performance. On the other hand, we develop a transform based prediction for P/B frames to reduce the computational complexity while improve the coding efficiency. Experimental results demonstrate the effectiveness of our proposed method.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-018-6825-4