3-D Compression-Oriented Image Content Correlation Model for Wireless Visual Sensor Networks

Wireless visual sensor networks (WVSNs) comprise a large number of camera-equipped visual sensors, which are often deployed densely to gain enhanced observations in field of interest. Therefore, there exists abundant image redundancy caused by the image content correlation (ICC) between cameras with the overlapped field of views (FoVs). Image compression is an important method to remove the image redundancy. Aiming at the problem of image compression, this paper designs a 3-D compression-oriented ICC (3D-COICC) model for WVSNs to quantitatively describe the ICC characteristics. First, the 3-D sensing model is developed by providing an improved method for the calculation of FoVs. After that, the 3D-COICC model is proposed by comparing the difference between foreshortening effects for one observed object, and then an algorithm of 3D-ICC is provided to estimate the ICC value. Furthermore, a scheme of correlation-based compression (CBC) is proposed to remove the redundancy of content between transmitted images, and thus the communication data are compressed. Where, an algorithm of image block restoration is provided to realize the transformation between two image blocks with the same pre-image. Evaluation results demonstrate that the developed 3-D sensing model can accurately depict the FoV of camera, and the 3D-COICC model outperforms the state of the art in accuracy. Further simulations show that the CBC scheme based on the 3D-COICC model is an effective method to remove the image content redundancy and improve the compression ratio.

[1]  Xi Zhang,et al.  3D percolation theory-based exposure-path prevention for optimal power-coverage tradeoff in clustered wireless camera sensor networks , 2014, 2014 IEEE Global Communications Conference.

[2]  Ian F. Akyildiz,et al.  Wireless multimedia sensor networks: A survey , 2007, IEEE Wireless Communications.

[3]  Hesham A. Ali,et al.  Image compression algorithms in wireless multimedia sensor networks: A survey , 2015 .

[4]  Gamantyo Hendrantoro,et al.  Energy Efficiency of Image Compression for Virtual View Image over Wireless Visual Sensor Network , 2015, J. Networks.

[5]  Jian Guo,et al.  A Camera Nodes Correlation Model Based on 3D Sensing in Wireless Multimedia Sensor Networks , 2012, Int. J. Distributed Sens. Networks.

[6]  Ian F. Akyildiz,et al.  Visual correlation-based image gathering for wireless multimedia sensor networks , 2011, 2011 Proceedings IEEE INFOCOM.

[7]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[8]  Lu Zhang,et al.  Coverage-Enhancing Algorithm Based on Overlap-Sense Ratio in Wireless Multimedia Sensor Networks , 2013, IEEE Sensors Journal.

[9]  Yu-Chee Tseng,et al.  The coverage problem in three-dimensional wireless sensor networks , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

[10]  Yunhuai Liu,et al.  Surface Coverage in Wireless Sensor Networks , 2009, IEEE INFOCOM 2009.

[11]  Umi Kalthum Ngah,et al.  Efficient Hardware-Based Image Compression Schemes for Wireless Sensor Networks: A Survey , 2014, Wireless Personal Communications.

[12]  Li-Minn Ang,et al.  Survey of image compression algorithms in wireless sensor networks , 2008, 2008 International Symposium on Information Technology.

[13]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[14]  José M. Barceló-Ordinas,et al.  A Method for Clustering and Cooperation in Wireless Multimedia Sensor Networks , 2010, Sensors.

[15]  Wendi B. Heinzelman,et al.  A Survey of Visual Sensor Networks , 2009, Adv. Multim..

[16]  Enzo Baccarelli,et al.  Green multimedia wireless sensor networks: distributed intelligent data fusion, in-network processing, and optimized resource management , 2014, IEEE Wireless Communications.

[17]  Moad Yassin Mowafi,et al.  A novel approach for extracting spatial correlation of visual information in heterogeneous wireless multimedia sensor networks , 2014, Comput. Networks.

[18]  Ian F. Akyildiz,et al.  A Spatial Correlation Model for Visual Information in Wireless Multimedia Sensor Networks , 2009, IEEE Transactions on Multimedia.

[19]  Chin-Feng Lai,et al.  A green data transmission mechanism for wireless multimedia sensor networks using information fusion , 2014, IEEE Wireless Communications.

[20]  Jan Flusser,et al.  Image registration methods: a survey , 2003, Image Vis. Comput..

[21]  Anlong Ming,et al.  A Coverage-Enhancing Method for 3D Directional Sensor Networks , 2009, IEEE INFOCOM 2009.

[22]  Hong-Hsu Yen,et al.  Novel Visual Sensor Coverage and Deployment in Time Aware PTZ Wireless Visual Sensor Networks , 2017, Sensors.

[23]  Naoki Wakamiya,et al.  Challenging issues in visual sensor networks , 2009, IEEE Wireless Communications.

[24]  Hamid Sharif,et al.  A Survey of Energy-Efficient Compression and Communication Techniques for Multimedia in Resource Constrained Systems , 2013, IEEE Communications Surveys & Tutorials.

[25]  Jean Ponce,et al.  Computer Vision: A Modern Approach , 2002 .

[26]  Ian F. Akyildiz,et al.  Wireless Multimedia Sensor Networks: Applications and Testbeds , 2008, Proceedings of the IEEE.

[27]  Tossaporn Srisooksai,et al.  Practical data compression in wireless sensor networks: A survey , 2012, J. Netw. Comput. Appl..

[28]  Ian F. Akyildiz,et al.  A Spatial Correlation-Based Image Compression Framework for Wireless Multimedia Sensor Networks , 2011, IEEE Transactions on Multimedia.