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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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
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Abstract	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.
AbstractList	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.
Author	Xu, Liang Xiao, Jing Hu, Ruimin Chen, Yu Wang, Zhongyuan
Author_xml	– sequence: 1 givenname: Yu surname: Chen fullname: Chen, Yu organization: National Engineering Research Center for Multimedia and Software, Wuhan University – sequence: 2 givenname: Ruimin surname: Hu fullname: Hu, Ruimin email: hrm@whu.edu.cn organization: National Engineering Research Center for Multimedia and Software, Wuhan University, Hubei Key Laboratory of Multimedia and Network Communication Engineering, Wuhan University – sequence: 3 givenname: Jing surname: Xiao fullname: Xiao, Jing organization: National Engineering Research Center for Multimedia and Software, Wuhan University – sequence: 4 givenname: Liang surname: Xu fullname: Xu, Liang organization: National Engineering Research Center for Multimedia and Software, Wuhan University – sequence: 5 givenname: Zhongyuan surname: Wang fullname: Wang, Zhongyuan organization: National Engineering Research Center for Multimedia and Software, Wuhan University
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Cites_doi	10.1109/CVPR.2011.5995616 10.1145/2601097.2601206 10.1109/TPAMI.2004.1262177 10.1016/j.neucom.2015.08.096 10.1109/TMM.2017.2766043 10.1109/TMM.2016.2645398 10.1109/ICIP.2017.8296285 10.1109/TCSVT.2010.2041820 10.1109/TIP.2016.2551366 10.1023/A:1007924428535 10.1109/JPROC.2010.2098830 10.1109/ISM.2016.0065 10.1109/TIP.2014.2352036 10.1109/TCSVT.2015.2389511 10.1007/978-3-319-48890-5_28 10.1109/TBC.2017.2781118 10.1109/TCSVT.2012.2221191 10.1109/TMM.2016.2581590 10.1109/TIP.2018.2867740 10.1145/2502081.2502145 10.1609/aaai.v30i1.9969 10.1109/TMM.2011.2180705 10.1609/aaai.v30i1.10155 10.1109/TMM.2013.2239629 10.1007/978-3-642-33718-5_60 10.1109/TPAMI.2003.1195991 10.1109/TCSVT.2003.815165 10.1007/978-3-319-10602-1_54 10.1145/2393347.2396294 10.1145/1101149.1101289 10.1109/TCSVT.2015.2477935 10.1109/ICME.2013.6607570 10.1109/ICALIP.2012.6376591 10.1109/TMM.2018.2867742 10.1109/TBDATA.2017.2715815
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Calculation of average PSNR differences between RD-curves. VCEG-M33 – reference: Guo, X., Li, S., & Cao, X. (2013). Motion matters: A novel framework for compressing surveillance videos. In Proceedings of the 21st ACM international conference on Multimedia, ACM, pp. 549-552 – reference: Ma, C., Liu, D., Peng, X., & Wu, F. (2017). Surveillance video coding with vehicle library. In Image Processing (ICIP), 2017 IEEE International Conference on, IEEE, pp. 270-274 – reference: YueHSunXYangJWuFCloud-based image coding for mobile devices—Toward thousands to one compressionIEEE Trans Multimedia201315484585710.1109/TMM.2013.2239629 – reference: Liu, Y., Nie, L., Han, L., Zhang, L., & Rosenblum, D. S. (2015). Action2Activity: Recognizing Complex Activities from Sensor Data. In IJCAI, pp. 1617-1623 – reference: Hakeem, A., Shafique, K., & Shah, M. (2005). An object-based video coding framework for video sequences obtained from static cameras. 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Snippet	The rapidly increasing surveillance video data has challenged the existing video coding standards. Even though knowledge based video coding scheme has been...
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SubjectTerms	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
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Title	Multisource surveillance video data coding with hierarchical knowledge library
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