Big Data-Driven Fast Reducing the Visual Block Artifacts of DCT Compressed Images for Urban Surveillance Systems

Abstract

The Urban Surveillance Systems generate huge amount of video and image data and impose high pressure onto the recording disks. It is obvious that the research of video is a key point of big data research areas. Since videos are composed of images, the degree and efficiency of image compression are of great importance. Although the DCT based JPEG standard are widely used, it encounters insurmountable problems. For instance, image encoding deficiencies such as block artifacts have to be removed frequently. In this paper, we propose a new, simple but effective method to fast reduce the visual block artifacts of DCT compressed images for urban surveillance systems. The simulation results demonstrate that our proposed method achieves better quality than widely used filters while consuming much less computer CPU resources.