A Novel Motion Estimation Method Based on Normalized Cross Correlation for Video Compression

  • Shou-Der Wei
  • Wei-Hau Pan
  • Shang-Hong Lai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4903)


In this paper we propose to use the normalized cross correlation (NCC) as the similarity measure for block-based motion estimation (ME) to replace the sum of absolute difference (SAD) measure. NCC is a more suitable similarity measure than SAD for reducing the temporal redundancy in video comparison since we can obtain flatter residual after motion compensation by using the NCC as the similarity measure in the motion estimation. The flat residual results in large DC term and smaller AC term, which means less information is lost after quantization. Thus, we can obtain better quality in the compressed video. Experimental results show the proposed NCC-based motion estimation algorithm can provide similar PSNR but better SSIM than the traditional full search ME with the SAD measure.


Motion estimation normalized cross correlation SSIM 


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  1. 1.
    Zhu, S., Ma, K.K.: A new diamond search algorithm for fast block-matching motion estimation. IEEE Trans. Image Processing 9(2), 287–290 (2000)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Li, R., Zeng, B., Liou, M.L.: A new three-step search algorithm for block motion estimation. IEEE Trans. Circuits Systems Video Technology 4(4), 438–442 (1994)CrossRefGoogle Scholar
  3. 3.
    Po, L.M., Ma, W.C.: A novel four-step search algorithm for fast block motion estimation. IEEE Trans. Circuits Systems Video Technology 6(3), 313–317 (1996)CrossRefGoogle Scholar
  4. 4.
    Li, W., Salari, E.: Successive elimination algorithm for motion estimation. IEEE Trans. Image Processing 4(1), 105–107 (1995)CrossRefGoogle Scholar
  5. 5.
    Gao, X.Q., Duanmu, C.J., Zou, C.R.: A multilevel successive elimination algorithm for block matching motion estimation. IEEE Trans. Image Processing 9(3), 501–504 (2000)CrossRefGoogle Scholar
  6. 6.
    Lee, C.-H., Chen, L.-H.: A fast motion estimation algorithm based on the block sum pyramid. IEEE Trans. Image Processing 6(11), 1587–1591 (1997)CrossRefGoogle Scholar
  7. 7.
    Lewis, J.P.: Fast template matching. Vision Interface, 120–123 (1995)Google Scholar
  8. 8.
    Mc Donnel, M.: Box-filtering techniques. Computer Graphics and Image Processing 17, 65–70 (1981)CrossRefGoogle Scholar
  9. 9.
    Viola, P., Jones, M.: Robust real-time object detection. In: Proceeding of International Conf. on Computer Vision Workshop Statistical and Computation Theories of Vision (2001)Google Scholar
  10. 10.
    Wang, Z., Bovik, A.C., Lu, L.: Why is image quality assessment so difficult. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, Orlando (May 2002)Google Scholar
  11. 11.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing 13(4) (April 2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Shou-Der Wei
    • 1
  • Wei-Hau Pan
    • 1
  • Shang-Hong Lai
    • 1
  1. 1.Department of Computer Science, National Tsing Hua University, HsinchuTaiwan

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