Efficient Video Compressing Technology For Video Surveillance

Along with the development of the video process and video coding technology, video surveillance systems have been used widely. The requirement for the quality of the picture and the performance of compressing is endless, so that efficient video coding technology becomes the hot research topic. However, the existed video coding technology doesn't consider the characteristics of video surveillance enough. Therefore, there is more space to improve the performance of video coding for video surveillance.Firstly, this paper studies the characteristics and the demands of surveillance video, and analyzes the disadvantage of existing coding techniques for surveillance, then proposes some video coding schemes, mainly includes the following aspects:In surveillance applications, there are often large static areas, which are not the focus. It is a waste of bit stream to code these static areas. In order to compress these areas largely and improve the quality of the image in the motion areas, this paper proposes an algorithm combined with the motion detection technique, which divides the picture into static layer picture and motion layer picture. For the static layer picture, the algorithm adopts a larger GOP (group of the pictures), and adds a layer skip mode, which improve coding efficiency greatly. For the motion layer picture, the algorithm adopts inter-layer prediction to improve the quality of the picture. The experiment shows the algorithm is very efficient.There is a lot of noise in surveillance video, especially in low light. It's difficult to improve coding efficiency for this kind of video. The paper proposes an algorithm, which combines video filter and adaptive synthesized reference (SR) picture. Video filter can achieve a better compression performance by reducing the noise. However, it will blur the texture details and the motion details at the same time. This is disadvantage in the surveillance applications. On the other hand, the SR picture is a new reference picture which is filtered simply. The SR picture can provide better prediction to improve the quality of the coding picture. Based on this, an adaptive SR picture is proposed. The experiment shows the adaptive SR picture can improve the performance of the video coding further. However this method just filters the reference picture but not the picture to be coded. The paper takes video filter and adaptive SR picture into consideration, combines both of them to suppress the video noise. At the same time, the proposed scheme improves the coding efficiency and preserves the picture details.