Abstract
In the wild, wireless multimedia sensor network (WMSN) communication has limited bandwidth and the transmission of wildlife monitoring images always suffers signal interference, which is time-consuming, or sometimes even causes failure. Generally, only part of each wildlife image is valuable, therefore, if we could transmit the images according to the importance of the content, the above issues can be avoided. Inspired by the progressive transmission strategy, we propose a hierarchical coding progressive transmission method in this paper, which can transmit the saliency object region (i.e. the animal) and its background with different coding strategies and priorities. Specifically, we firstly construct a convolution neural network via the MobileNet model for the detection of the saliency object region and obtaining the mask on wildlife. Then, according to the importance of wavelet coefficients, set partitioned in hierarchical tree (SPIHT) lossless coding is utilized to transmit the saliency image which ensures the transmission accuracy of the wildlife region. After that, the background region left over is transmitted via the Embedded Zerotree Wavelets (EZW) lossy coding strategy, to improve the transmission efficiency. To verify the efficiency of our algorithm, a demonstration of the transmission of field-captured wildlife images is presented. Further, comparison of results with existing EZW and discrete cosine transform (DCT) algorithms shows that the proposed algorithm improves the peak signal to noise ratio (PSNR) and structural similarity index (SSIM) by 21.11%, 14.72% and 9.47%, 6.25%, respectively.
Highlights
Wildlife monitoring is crucial for the balance and stability of the whole ecosystem [1,2]
Both peak signal to noise ratio (PSNR) and structural similarity index (SSIM) [29] are utilized as objective criteria to evaluate the quality of image reconstruction
PSNR is the ratio of the signal maximum possible power to the destructive noise power based on the mean square error (MSE) [30], which affects representation accuracy
Summary
Wildlife monitoring is crucial for the balance and stability of the whole ecosystem [1,2]. Images and videos of wildlife are the main materials that can be collected in the monitoring process Processing those materials in real time and effectively is a challenge. Due to the limitations of WMSNs with low processing capability, power consumption restrictions and narrow transmission bandwidth, the wildlife monitoring images collected encompass high resolution and large information data characteristics, which poses a challenge for the WMSN transmission process [7]. For transmitting task through resource-constrained WMSN, image compression coding is utilized to reduce the transmission workload. In this field, image compression algorithms such as discrete cosine transform (DCT) [8], singular value decomposition (SVD) [9], and Sensors 2019, 19, 946; doi:10.3390/s19040946 www.mdpi.com/journal/sensors.
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