Theoretical Improvement of the Image Compression Method Based on Wavelet Transform

A digital watermarking image compression method based on symmetrical encryption algorithm is proposed in this study. First, the original image and scrambled watermarking image are processed by wavelet transform, and then the watermarking image processed by the Arnold replacement method is transformed into a meaningless image in the time domain to achieve the effect of encryption. Watermarking is generated by embedding the watermarking image into the important coefficients of the wavelet transform. As an inverse process of watermarking embedding, watermarking extraction needs to be reconstructed by the wavelet transform. Finally, the watermarking is extracted from the inverse scrambled watermarking image, and a new symmetrically encrypted digital watermarking image is obtained. The compression method compresses the embedded digital watermarking image, so that the volume of the compressed watermarking image is greatly reduced when the visual difference is very small. The experimental results show that the watermarking image encrypted by this method not only has good transparency, but also has strong anti-brightness/contrast attack, anti-shearing, and anti-noise performance. When the volume of the compressed image is greatly reduced, the root mean square error and visual difference measurement of the watermarking image are very small.

Download Full-text

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

Electronics ◽

10.3390/electronics8121559 ◽

2019 ◽

Vol 8 (12) ◽

pp. 1559 ◽

Cited By ~ 3

Author(s):

Fan Zhang ◽

Zhichao Xu ◽

Wei Chen ◽

Zizhe Zhang ◽

Hao Zhong ◽

...

Keyword(s):

Wavelet Transform ◽

Image Compression ◽

Discrete Wavelet Transform ◽

Video Surveillance ◽

Structural Similarity ◽

Underground Mines ◽

Noise Robustness ◽

Discrete Wavelet ◽

Surveillance Systems ◽

Compression Method

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.

Download Full-text