A reliable image compression algorithm based on block luminance adopting deep learning for video surveillance application

Abstract

Video surveillance systems are essential as other application domain. Handling efficient and reliable for underground projects as well surveillance image is so significant to ensure security and safety. The wireless channels are efficient as data transferring media. On the other hand, the bandwidth may be limited for some environmental conditions. Hence, the image compression algorithm is very important to be conducted and applied to save the transmission bandwidth. This paper presents an image compression algorithm for video surveillance. The method is based on the concept of luminance variation of image. The image compression method is expected to achieve a reasonable compression ratio with acceptable quality. With another meaning, the compressed image size is decreased and consumes a smaller transmission bandwidth via the wireless channel compared with the original image size. The method adopts a deep learning approach to improve the quality with limited bandwidth. The proposed method is abbreviated as DLBL (deep learning block luminance). DLBL implemented and tested on some tested bed images. The performance of the proposed method is compared with some ones considering the same conditions. Some measurable criteria are taken into consideration for performance evaluation. The criteria are the compression ratio (CR), peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM). From the experiments results, the proposed method showed significant and efficient performance compared with some other related ones. This is clear from the values of CR, PSNR and SSIM.