Investigating the Performance of Motion Estimation Block-Matching Algorithms on GPU Cards

In the field of video compression, motion estimation (ME) is a process that leads to high computational complexity. Implementation of ME block-matching (BM) algorithms on general purpose Central Processing Unit (CPU), has resulted in poor performance. In this paper we investigate the performance of two BM ME algorithms: Three Step Search (TSS) and Four Step Search (4SS) on Graphics Processing Unit (GPU) NVIDIA Quadro 400 using the Compute Unified Device Architecture (CUDA) platform. Both algorithms perform motion estimation on a block-by-block basis, which is considered the simplest way in terms of hardware and software implementation. The focus is to achieve parallelization of the algorithms for a real time execution. We consider two well-known test sequences: “football” and “mad900”, with different image resolution. The results show that the implementation on a GPU card can improve the performance in terms of execution time, by a factor of 1000.