A Multilayer Improved RBM Network Based Image Compression Method in Wireless Sensor Networks
The processing capacity and power of nodes in a Wireless Sensor Network (WSN) are limited. And most image compression algorithms in WSN are subject to random image content changes or have low image qualities after the images are decoded. Therefore, an image compression method based on multilayer Res...
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| Vydáno v: | International journal of distributed sensor networks Ročník 2016; číslo 3; s. 1851829 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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London, England
Hindawi Publishing Corporation
01.01.2016
SAGE Publications John Wiley & Sons, Inc Wiley |
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| ISSN: | 1550-1329, 1550-1477, 1550-1477 |
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| Abstract | The processing capacity and power of nodes in a Wireless Sensor Network (WSN) are limited. And most image compression algorithms in WSN are subject to random image content changes or have low image qualities after the images are decoded. Therefore, an image compression method based on multilayer Restricted Boltzmann Machine (RBM) network is proposed in this paper. The alternative iteration algorithm is also applied in RBM to optimize the training process. The proposed image compression method is compared with a region of interest (ROI) compression method in simulations. Under the same compression ratio, the qualities of reconstructed images are better than that of ROI. When the number of hidden units in top RBM layer is 8, the peak signal-to-noise ratio (PSNR) of the multilayer RBM network compression method is 74.2141, and it is much higher than that of ROI which is 60.2093. The multilayer RBM based image compression method has better compression performance and can effectively reduce the energy consumption during image transmission in WSN. |
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| AbstractList | The processing capacity and power of nodes in a Wireless Sensor Network (WSN) are limited. And most image compression algorithms in WSN are subject to random image content changes or have low image qualities after the images are decoded. Therefore, an image compression method based on multilayer Restricted Boltzmann Machine (RBM) network is proposed in this paper. The alternative iteration algorithm is also applied in RBM to optimize the training process. The proposed image compression method is compared with a region of interest (ROI) compression method in simulations. Under the same compression ratio, the qualities of reconstructed images are better than that of ROI. When the number of hidden units in top RBM layer is 8, the peak signal-to-noise ratio (PSNR) of the multilayer RBM network compression method is 74.2141, and it is much higher than that of ROI which is 60.2093. The multilayer RBM based image compression method has better compression performance and can effectively reduce the energy consumption during image transmission in WSN. |
| Author | Wang, Shu Chen, Xingguo Yang, Yanying Cheng, Chunling |
| Author_xml | – sequence: 1 givenname: Chunling surname: Cheng fullname: Cheng, Chunling organization: College of Computer Nanjing University of Posts and Telecommunications Nanjing 213001 China njupt.edu.cn – sequence: 2 givenname: Shu surname: Wang fullname: Wang, Shu organization: College of Computer Nanjing University of Posts and Telecommunications Nanjing 213001 China njupt.edu.cn – sequence: 3 givenname: Xingguo surname: Chen fullname: Chen, Xingguo organization: College of Computer Nanjing University of Posts and Telecommunications Nanjing 213001 China njupt.edu.cn – sequence: 4 givenname: Yanying surname: Yang fullname: Yang, Yanying organization: Department of Information and Technology Nanjing College of Forestry Police Nanjing 210023 China |
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| Cites_doi | 10.1109/IFITA.2009.403 10.1109/IJCNN.2010.5596837 10.1109/5.726791 10.1360/jos180669 10.1016/j.patcog.2013.05.025 10.1162/089976602760128018 10.1016/j.neucom.2014.02.024 10.3724/SP.J.1146.2009.00704 10.1109/ijcnn.2014.6889634 10.1007/978-3-642-15825-4_26 |
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| Copyright | Copyright © 2016 Chunling Cheng et al. 2016 Chunling Cheng et al. Copyright © 2016 Chunling Cheng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
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| DOI | 10.1155/2016/1851829 |
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| References_xml | – reference: Cai-xiang N. Study on Image Data Compression Processing in Wireless Multimedia Sensor Network 2014 Xi'an, China Chang'an University – reference: Ji N. Zhang J. Parallel tempering with equi-energy moves for training of restricted boltzmann machines Proceedings of the International Joint Conference on Neural Networks (IJCNN '14) July 2014 Beijing, China 120 127 10.1109/ijcnn.2014.6889634 2-s2.0-84908472621 – reference: Hu Y. Markov chain Monte Carlo based improvements to the learning algorithm of restricted Boltzmann machines [M.S. thesis] 2012 Shanghai, China Shanghai Jiao Tong University – volume: 18 start-page: 669 issue: 3 year: 2007 end-page: 680 ident: 9 article-title: Based on ring model of wavelet compression algorithm in sensor networks – reference: Ding X. Zhang Y. Liu T. Duan J. 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start-page: 463 issue: 2 year: 2010 end-page: 465 article-title: Adaptive nondestructive data compression system of WSN – volume: 86 start-page: 2278 issue: 11 year: 1998 end-page: 2324 article-title: Gradient-based learning applied to document recognition – volume: 14 start-page: 1771 issue: 8 year: 2002 end-page: 1800 article-title: Training products of experts by minimizing contrastive divergence – volume: 32 start-page: 755 issue: 3 year: 2010 end-page: 758 article-title: Based on a linear model of the space-time data compression algorithm in sensor networks – volume: 18 start-page: 669 issue: 3 year: 2007 end-page: 680 article-title: Based on ring model of wavelet compression algorithm in sensor networks – volume: 34 start-page: 141 issue: 2 year: 2007 end-page: 143 article-title: Facing the wireless sensor network streaming data compression technology – volume: 25 start-page: 1676 issue: 11 year: 2005 end-page: 1678 article-title: Based on interval wavelet transform in hybrid entropy data compression algorithm in sensor network – volume: 9 start-page: 789 year: 2010 end-page: 795 article-title: On the convergence properties of contrastive divergence – volume: 47 start-page: 25 issue: 1 year: 2014 end-page: 39 article-title: Training restricted Boltzmann machines: an introduction – start-page: 1033 volume-title: Proceedings of the 26th Annual International Conference on Machine Learning (ICML '09) ident: B18-2016-1851829 – volume: 36 start-page: 74 issue: 16 year: 2010 ident: B12-2016-1851829 publication-title: Computer Engineering – ident: B5-2016-1851829 doi: 10.1109/IFITA.2009.403 – ident: B21-2016-1851829 – volume: 34 start-page: 141 issue: 2 year: 2007 ident: B6-2016-1851829 publication-title: Computer Science – volume: 18 start-page: 463 issue: 2 year: 2010 ident: B13-2016-1851829 publication-title: Computer Measurement and Control – ident: B27-2016-1851829 doi: 10.1109/IJCNN.2010.5596837 – start-page: 1064 volume-title: Proceedings of the 25th International Conference on Machine Learning ident: B17-2016-1851829 – ident: B25-2016-1851829 doi: 10.1109/5.726791 – volume: 30 start-page: 48 issue: 3 year: 2008 ident: B10-2016-1851829 publication-title: Journal of Communication – volume-title: Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives year: 2012 ident: B22-2016-1851829 – ident: B9-2016-1851829 doi: 10.1360/jos180669 – volume: 9 start-page: 789 year: 2010 ident: B16-2016-1851829 publication-title: Journal of Machine Learning Research—Proceedings Track – ident: B23-2016-1851829 doi: 10.1016/j.patcog.2013.05.025 – ident: B15-2016-1851829 doi: 10.1162/089976602760128018 – ident: B20-2016-1851829 doi: 10.1016/j.neucom.2014.02.024 – start-page: 73 volume-title: Proceedings of the 28th Annual Conference on Neural Information Processing Systems (NIPS '14) ident: B4-2016-1851829 – ident: B7-2016-1851829 doi: 10.3724/SP.J.1146.2009.00704 – volume: 25 start-page: 1676 issue: 11 year: 2005 ident: B8-2016-1851829 publication-title: Computer Applications – start-page: 2393 volume-title: Proceedings of the 28th Annual Conference on Neural Information Processing Systems (NIPS '14) ident: B1-2016-1851829 – volume: 46 start-page: 2085 issue: 12 year: 2009 ident: B11-2016-1851829 publication-title: Journal of Computer Research and Development – ident: B28-2016-1851829 doi: 10.1109/ijcnn.2014.6889634 – volume-title: Study on Image Data Compression Processing in Wireless Multimedia Sensor Network year: 2014 ident: B14-2016-1851829 – volume: 9 start-page: 145 year: 2010 ident: B26-2016-1851829 publication-title: Journal of Machine Learning Research Workshop & Conference Proceedings – start-page: 2327 volume-title: Proceedings of the 24th International Joint Conference on Artificial Intelligence (ICJAI '15) ident: B2-2016-1851829 – volume-title: Neural Information Processing Systems (NIPS) year: 2010 ident: B19-2016-1851829 – start-page: 1738 volume-title: Proceedings of the 28th AAAI Conference on Artificial Intelligence ident: B3-2016-1851829 – ident: B24-2016-1851829 doi: 10.1007/978-3-642-15825-4_26 |
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| SubjectTerms | Algorithms Image compression Multilayers Networks Remote sensors Signal to noise ratio Wireless networks |
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| Title | A Multilayer Improved RBM Network Based Image Compression Method in Wireless Sensor Networks |
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