Low Complexity Weighted Two-Bit Transforms Based Multiple Candidate Motion Estimation

Abstract

A low complexity weighted two-bit transforms (2BT) based multiple candidate motion estimation algorithm is proposed in this paper. By exploiting almost the identical operations in two different matching error criteria, we can efficiently determine two best motion vectors according to the respective matching criteria with negligible complexity increase. And with additional local square search, the proposed algorithm can enhance the overall motion estimation accuracy substantially. Experimental results show that the proposed algorithm achieves peak-to-peak signal-to-noise ratio (PSNR) gains about 0.55dB on average compared with the conventional 2BT-based motion estimation.