Block-based Motion Vector Smoothing for Nonrigid Moving Objects

비정형성 등속운동 객체의 움직임 추정을 위한 블록기반 움직임 평활화

  • Sohn, Young-Wook (Dept. of Electrical and Electronic Engineering, Yonsei University) ;
  • Kang, Moon-Gi (Dept. of Electrical and Electronic Engineering, Yonsei University)
  • 손영욱 (연세대학교 전기전자공학과) ;
  • 강문기 (연세대학교 전기전자공학과)
  • Published : 2007.11.25

Abstract

True motion estimation is necessary for deinterlacing, frame-rate conversion, and film judder compensation. There have been several block-based approaches to find true motion vectors by tracing minimum sum-of-absolute-difference (SAD) values by considering spatial and temporal consistency. However, the algorithms cannot find robust motion vectors when the texture of objects is changed. To find the robust motion vectors in the region, a recursive vector selection scheme and an adaptive weighting parameter are proposed. Previous frame vectors are recursively averaged to be utilized for motion error region. The weighting parameter controls fidelity to input vectors and the recursively averaged ones, where the input vectors come from the conventional estimators. If the input vectors are not reliable, then the mean vectors of the previous frame are used for temporal consistency. Experimental results show more robust motion vectors than those of the conventional methods in time-varying texture objects.

블록 기반 프레임 레이트 변환 (frame-rate conversion) 또는 필름 떨림 보상 (film judder compensation)을 수행하기 위해서는 참 움직임 벡터(true motion vector)를 찾아야 한다. 이를 위해서 현재 블록의 공간적 및 시간적 상관성을 최대로 하여 시각적으로 덜 부자연스럽게 느끼도록 하는 방법들이 연구되었다. 그러나 기존의 블록단위 절대값 차이의 합 (SAD)만으로는 비정형성 객체의 움직임 에러를 추정할 수 없었다. 본 논문에서는 비정형성 객체가 등속운동을 하는 경우 재귀적으로 기존의 움직임을 유지하도록 하는 방법을 제안하였다. 현재 블록의 등속움직임 추정값을 재귀평균으로 구하였으며 현재 블록 벡터의 신뢰도를 계산하여 원래의 움직임 벡터와 재귀평균 움직임 벡터중에서 가중치를 두도록 하였다. 실험결과 비정형성 등속운동 객체의 움직임을 블록기반으로 추정함을 확인할 수 있었다.

Keywords

References

  1. G. Haan, P. Biezen, H. Huijgen, and O. A. Ojo, 'True-Motion Estimation with 3-D Recursive Search Block Matching', IEEE Tran. on Circuits Syst. Video Technol. vol. 3, no. 5, 368-379, Oct. 1993 https://doi.org/10.1109/76.246088
  2. J. Tham, S. Ranganath, A. Kassim, 'A novel unrestricted center-biased diamond search algorithm for block motion estimation,' IEEE Trans. Circuits and Systems for Video Tech., vol. 8, no. 4, pp. 369-377, Aug. 1998 https://doi.org/10.1109/76.709403
  3. A. Tourapis, O. Au, and M. Liou, .'An advanced zonal block based algrithm for motion estimation', in Proc. IEEE Int. Conf. Image Processing, vol.2, pp. 610-614, Oct. 1999
  4. A. Tourapis, O. Au, and M. Liou, 'Highly efficient predictive zonal algorithms for fast block-matching motion estimation,' IEEE Trans. Circuits and Systems for Video Tech.,vol. 12, no. 10, pp. 934-947, Oct. 2002 https://doi.org/10.1109/TCSVT.2002.804894
  5. Y. Nie and K. Ma, 'Adaptive rood pattern search for fast block-matching motion estimation,' IEEE Tran. Image Processing, vol. 11, no. 12, pp. 1442-1449, Dec. 2000
  6. Y. Nie and K. Ma, 'Adaptive Irregular Pattern Search width matching prejudgment for fast block-matching motion estimation', IEEE Trans. Circuits and Systems for Video Tech., vol 15, no. 6, pp. 789-794, Jun. 2005 https://doi.org/10.1109/TCSVT.2005.848305
  7. L. Alparone, P. Haavisto, and Y. Neubo, 'Vector median filters,' in Proc. IEEE, vol. 78, no. 4, pp. 678-689, Apr. 1990
  8. L. Alparone, M. Barni, F. Bartolini, and V. Cappellini, 'Adaptively weighted vector-median filters for motion-fields smoothing', in Proc. IEEE Int. Conf. Acoust. Speech Signal Processing, vol. 4, pp. 2267-2270, May, 1996
  9. J. Konrad and E. Dubois, 'Bayesian estimation of motion vector fields,' IEEE Tran. Pattern Anal. and Machine Intell., vol. 14, no. 9, pp. 910-927, Sep. 1992 https://doi.org/10.1109/34.161350