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.
Highlights
Wireless Sensor Network (WSN) emerge as a new research hot spot in recent years
The image compression algorithms in WSNs are subject to the random changes in image contents
The experiment consists of three parts: the performance analysis of Restricted Boltzmann Machine (RBM); the analysis of the compression performance of the proposed image compression method and the evaluation of reconstructed image quality; the analysis of energy consumption in WSNs when multilayer RBM network image compression method is used
Summary
WSNs emerge as a new research hot spot in recent years. In a WSN, the resources in each sensor node are limited. It is a huge challenge to reduce energy consumption and extend the lifetime of a sensor node. The energy cost of transmitting image in WSN remains to be a main factor that affects the lifetime of a sensor node. To reduce the bandwidth and energy consumption in image transmission, it is necessary to propose a more effective image compression method. It is unrealistic to describe various images in real world with only one kind of image model. To address this issue, the neural network is adopted in WSNs to compress images. The training complexity of RBM has a great effect on the energy consumption of image compression coding
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