Background-foreground division based search for motion estimation in surveillance video coding

Basically, motion search is very time-consuming in the process of video coding. For surveillance videos, however, there exist a large amount of static background regions whose motion vectors actually are equal to zero. By utilizing the background and foreground information of coding units, this paper proposes a background-foreground division based search algorithm (BFDS) to accelerate the motion search in surveillance video coding. The basic idea of BFDS is to classify a predicting unit (PU) into a background predicting unit (BPU) or a foreground predicting unit (FPU) and then adopt different search strategies respectively for BPUs and FPUs. That is, a zero motion vector biased search strategy is applied in BPUs to reduce the search complexity on a large scale while a precise global search strategy is applied in FPUs to get higher coding performance. Compared with the current TZ search algorithm used in HEVC, the proposed BFDS algorithm can reduce the number of search points by 57.73% while remaining the coding performance almost unchanged.