An Image Compression Method for Video Surveillance System in Underground Mines Based on Residual Networks and Discrete Wavelet Transform

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.

Deep Learning Applications with Practical Measured Results in Electronics Industries

“…) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were given in these articles, and the results indicated that the performance of the improved deep learning methods could be higher than the performance of conventional machine learning methods [43][44][45][46][47][48][49][50][51][52][53][54][55][56].…”

“…In the experiments, this study collected the images from the COCO 2014 dataset and the images of underground mines for training and testing. The results show that the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) of the proposed method were higher than the PSNR and the SSIM of other methods (e.g., denoising-based approximate message passing (D-AMP), ReconNet and total variation augmented Lagrangian alternating direction algorithm (TVAL3)) [46].…”

“…Four papers on environmental information analyses and predictions are as follows: (1) "A Data-Driven Short-Term Forecasting Model for Offshore Wind Speed Prediction Based on Computational Intelligence" by Panapakidis et al [43]; (2) "Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting" by Wan et al [44]; (3) "Modeling and Analysis of Adaptive Temperature Compensation for Humidity Sensors" by Xu et al [45]; (4) "An Image Compression Method for Video Surveillance System in Underground Mines Based on Residual Networks and Discrete Wavelet Transform" by Zhang et al [46].…”

A Novel Image Processing Approach to Enhancement and Compression of X-ray Images

“…In [ 13 ], the concept of ‘Wavelet-based compression of images’ was used in grayscale images with various techniques, such as SPIHT, EZW, and SOFM. In [ 14 ], the authors deal with a specific type of compression by utilizing wavelet transforms. Wavelets were employed as simple patterns/coefficients, reproducing the initial pattern when multiplied and combined.…”

DetReco: Object-Text Detection and Recognition Based on Deep Neural Network

“…e feature extraction network is a fully convolutional network. It is mainly composed of 3 × 3 and 1 × 1 convolution kernels and a large number of shortcut links with residual units [35][36][37]. e structure of the feature extraction network is shown in Figure 2.…”