Low complexity truncated Gray-coded bit plane matching based multiple candidate motion estimation

In this paper, we propose a low complexity truncated Gray-coded (TGC) bit plane matching (BPM) based multiple candidate motion estimation algorithm. We can efficiently determine two best motion vectors according to the respective matching criteria and can enhance the overall motion estimation accuracy by exploiting almost the identical operations in two different matching error criteria. Experimental results show that the proposed algorithm achieves peak-to-peak signal-to-noise ratio (PSNR) gains about 0.63dB on average compared with the conventional 2BT-based motion estimation with negligible complexity increase.