A Multilayer Improved RBM Network Based Image Compression Method in Wireless Sensor Networks | Zendy

Chunling Cheng | Zendy; Shu Wang | Zendy; Xingguo Chen | Zendy; Yanying Yang | Zendy

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A Multilayer Improved RBM Network Based Image Compression Method in Wireless Sensor Networks

Author(s) -

Chunling Cheng,

Shu Wang,

Xingguo Chen,

Yanying Yang

Publication year - 2016

Publication title -

international journal of distributed sensor networks

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.324

H-Index - 53

eISSN - 1550-1477

pISSN - 1550-1329

DOI - 10.1155/2016/1851829

Subject(s) - computer science , image compression , compression (physics) , data compression , region of interest , artificial intelligence , peak signal to noise ratio , compression ratio , data compression ratio , computer vision , image (mathematics) , wireless sensor network , image processing , algorithm , computer network , materials science , internal combustion engine , automotive engineering , engineering , composite material

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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