A hybrid motion estimation approach based on normalized cross correlation for video compression

Wei-Hau Pan, Shou-Der Wei, Shang-Hong Lai

Abstract: In this paper we propose a new hybrid approach for block based motion estimation (ME) by adaptively using the normalized cross correlation (NCC) and sum of absolute differences (SAD) measures. We use the SAD value and gradient sum as the criterion to determine which similarity measure to be used for motion estimation. In general, using the NCC as the similarity measure in the motion estimation leads to more uniform residuals than those of using the SAD. This leads to larger DC terms and smaller AC terms, which yields less information loss after DCT quantization. However, NCC is not suitable for homogeneous regions since the best match may have a high NCC value but with large average gray level difference. Thus, we propose to alternatively use the SAD and NCC as the ME criterion for homogeneous and inhomogeneous blocks. Experimental results show the proposed hybrid motion estimation algorithms can provide superior PSNR and SSIM values than the traditional SAD-based ME method.