Novel Gaussian Mixture-based Video Coding for Fixed Background Video Streaming

Abstract

In recent years, tremendous advances have been made in Artificial Intelligence (AI) algorithms in the field of image processing. Despite these advances, video compression using AI algorithms has always faced major challenges. These challenges often lie in two areas of higher processing load in comparison with traditional video compression methods, as well as lower visual quality in video content. Careful study and solution of these two challenges is the main motivation of this article that by focusing on them, we have introduced a new video compression based on AI. Since the challenge of processing load is often present in online systems, we have examined our AI video encoder in video streaming applications. One of the most popular applications of video streaming is traffic cameras and video surveillance in road environments which here we called it CCTVs. Our idea in this type of system goes back to fixed background images, where always occupied the bandwidth not efficiently, and the streaming video is related to duplicate background images. Our AI-based video encoder detects fixed background and caches it at the client-side by the background subtraction method. By separating the background image from the moving objects, it is only enough to send the moving objects to the destination, which can save a lot of network bandwidth. Our experimental results show that, in exchange for an acceptable reduction in visual quality assessment, the video compression processing load will be drastically reduced.