Adaptive Long-Term Reference Selection for Efficient Scalable Surveillance Video Coding

Abstract

The exponential growth of video surveillance has been asking for a more powerful video coding solution, which is characterized by not only the high compression efficiency but also the adaptive video streaming capability. The surveillance video content, however, usually contains a large number of background areas and having high temporal correlation between frames. In this context, we propose a novel adaptive long-term reference mechanism for scalable surveillance video coding, which provides the quality and temporal scalabilities while achieving the high compression performance. The proposed long - term reference is mainly selected based on the content analysis of video sequence. The long-term reference selection solution is integrated into the most recent Scalable High Efficiency Video Coding (SHVC) standard. Experiments conducted for a rich set of surveillance videos show that the proposed scalable video coding solution can achieve around 5.38% bitrate saving when compared to the traditional SHVC video coding benchmark.