Abstract
Video surveillance systems play an important role in underground mines. Providing clear surveillance images is the fundamental basis for safe mining and disaster alarming. It is of significance to investigate image compression methods since the underground wireless channels only allow low transmission bandwidth. In this paper, we propose a new image compression method based on residual networks and discrete wavelet transform (DWT) to solve the image compression problem. The residual networks are used to compose the codec network. Further, we propose a novel loss function named discrete wavelet similarity (DW-SSIM) loss to train the network. Because the information of edges in the image is exposed through DWT coefficients, the proposed network can learn to preserve the edges better. Experiments show that the proposed method has an edge over the methods being compared in regards to the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), particularly at low compression ratios. Tests on noise-contaminated images also demonstrate the noise robustness of the proposed method. Our main contribution is that the proposed method is able to compress images at relatively low compression ratios while still preserving sharp edges, which suits the harsh wireless communication environment in underground mines.
Highlights
Considering the demand of image compression at very low compression ratios in underground mines, in this paper, we propose an image codec network based on convolution neural networks (CNNs) and a new loss function based on discrete wavelet transform
In order to generalize the recovery capability, the network of the proposed method is trained on both images from video images we have collected in underground mines and images from the COCO
We test the proposed method on images of coal cutter and tunnel boring machine (TBM) which are from real underground mines to evaluate the performance in the application-specific environment
Summary
Coal is one of the major resources in China. In the foreseeable future, China will still be the largest consumer and the producer of coal [1]. It is of great importance to research into technologies that contribute to the advancement in intelligent mine monitoring and safe mining practices. One of the key components of intelligent mine monitoring is the video surveillance system since visual information plays a key role in how a human perceives the world. Because digital images usually require large storage, it is natural to think of transmitting images with high bandwidth. Wireless networks are usually chosen as the information channel in mines. The bandwidth can be limited because of relatively limited narrow spaces, harsh environment diffraction, attenuation, and multi-path effect in underground mines. It is necessary to investigate image compression methods in order to save the transmission bandwidth
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
