Level-set-based motion estimation algorithm for multiple reference frame motion estimation

Abstract

Motion estimation (ME) has a variety of applications in image processing, pattern recognition, target tracking, and video compression. In modern video compression standards such as H.264/AVC and HEVC, multiple reference frame ME (MRFME) is adopted to reduce the temporal redundancy between successive frames in a video sequence. In MRFME, the motion search process is conducted using additional reference frames, thereby obtaining better prediction signal as compared to single reference frame ME (SRFME). However, its high computational complexity makes it difficult to be utilized in real-world applications. In order to reduce the computational complexity of MRFME, this paper proposes a level-set-based ME algorithm (LSME) without any penalty in the rate-distortion (RD) performance. First, the proposed algorithm partitions the motion search space into multiple level sets based on a rate constraint. The proposed algorithm then controls the ME process on the basis of the predetermined level sets. Experimental results show that the proposed algorithm reduces the ME time by up to 83.46% as compared to the conventional full search (FS) algorithm.