High-efficiency Distributed Image Compression Algorithm Based on Soft Threshold Iteration for Wildlife Images with Wireless Image Sensor Networks.

Wireless image sensor networks (WISNs) are widely applied in wildlife protection as they present a better performance in remote, real-time monitoring. However, traditional WISNs suffer from the limitations of low processing capability, power consumption restrictions, and narrow transmission bandwidth, which leads to a shorter working lifetime of the monitoring system when transmitting the wildlife monitoring image with high resolution. We propose a high-efficiency distributed image compression coding method based on soft threshold iteration and quantitative perception for wildlife monitoring images to rationally assign the electricity resource. Specifically, we first utilize the histogram contrast algorithm to detect the saliency object region from the original samples and use it to generate the mask image of the wildlife region. After the mask image is obtained, the distributed image compression coding method is utilized to transmit the wildlife image, in which the saliency image region is directly transmitted as a cluster head to ensure the transmission efficiency of the wildlife region. Then the background region is assigned to the other four monitoring nodes at the same level for processing and transmission, extending the lifetime of the network. Furthermore, the soft threshold iteration algorithm is utilized to encode the image data; this is suitable for WISNs. The experimental results on our own wildlife dataset show improvements of 7.47 and 9.06% for the peak signal-tonoise ratio and 16.98 and 19.50% for the structural similarity index on the reconstructed image compared with those of the discrete cosine transform and embedded zerotree wavelets algorithms, respectively. Compared with the multihop and single-hop transmission methods, the power consumption is reduced by 29.96 and 40.84%, respectively. The results of this study indicate that the WISN technique can provide feasible solutions for the intelligent monitoring of forest biological resources. [ABSTRACT FROM AUTHOR]