Fast Motion Estimation Algorithm Based on Complex Wavelet Transform
Rent the article at a discountRent now
* Final gross prices may vary according to local VAT.Get Access
In this paper, we introduce an algorithm for motion estimation. It combines complex wavelet decomposition and a fast motion estimation method based on affine model. The principle of wavelet transform is to decompose hierarchically the input image into a series of successively lower resolution reference images and detail images which contain the information needed to be reconstructed back to the next higher resolution level. The motion estimation determines the velocity field between two successive images. This phase can be extracted from this measure descriptive information of the sequence. Motion Estimation (ME) is an important part of any video compression system, since it can achieve significant compression by exploiting the temporal redundancy existing in a video sequence. This paper described a method from calculating the optical flow of an image sequence based on complex wavelet transform. It consists to project the optical flow vectors on a basis of complex-valued wavelets. Thus, we add an additional assumption on the shape of the velocity field that we want to find, which is the affinity of the optical flow. The two-dimensional affine motion model is used to formulate the optical flow problem by coarse resolution simultaneously coarse-and-fine, beside the traditional approach by coarse-to-fine, to avoid the error propagation during the decomposition of coarse level to fine level. This method opens the way for a quick and low-cost computing optical flow.
- Barron, J., Fleet, D., Beauchemin, S. (1994). “Performance of optical flow techniques”. International Journal of Computer Vision.
- Weber, J., & Malik, J. (1995). Robust computation of optical flow in multi-scale differential framework. International Journal of Computer Vision, 14(1), 67–81. CrossRef
- Lee, C., & Lu, M. (1997). An efficient VLSI architecture for full-search block matching algorithms. Journal of Signal Processing Systems - JSPS, 15(3), 275–282.
- Mokraoui, A., Muñoz-Jiménèz, V., & Astruc, J. P. (2010). Motion estimation algorithms using the deformation of planar hierarchical mesh grid for videoconferencing applications at low bit-rate transmission. Journal of Signal Processing Systems for Signal, Image and Video Technology, 61, 1939–8018 (Print).
- Lee, S. (2010). “Fast motion estimation based on search range adjustment and matching point decimation” Department of Imaging Engineering, Chung-Ang University, Seoul, COREE, REPUBLIQUE DE.
- Ahmadi, A., Azadfar, M.M. (2008) Implementation of fast motion estimation algorithms comparison with full search method in H.264. IJCSNS International Journal of Computer Science and Network Security, 8(No.3), March 2008.
- Liu, H., Chellappa, R., Rosenfeld, A. (2003) ‘Fast two-frame multiscale dense optical flow estimation using discrete wavelet filters’, Center for Automation Research, University of Maryland, College Park, Maryland 20770, Journal of the Optical Society.
- Magarey, J., & Kingsbury, N.G. (1998) Motion estimation using complex-valued wavelet transform. IEEE Transactions Signal Process, 46(4).
- Sebe, N., Lamba, C., & Lew, M. S. (2002). An overcomplete discrete wavelet transform for video compression. International Conference on Multimedia and Expo, 1, 641–644. CrossRef
- Fernandes, F. C. A., van Spaendonck, R. L. C., & Burrus, C. S. (2003). A new framework for complex wavelet transforms. IEEE Transactions on Signal Processing, 2, 51(no:7), 1825–1837. CrossRef
- Fast Motion Estimation Algorithm Based on Complex Wavelet Transform
Journal of Signal Processing Systems
Volume 72, Issue 2 , pp 99-105
- Cover Date
- Print ISSN
- Online ISSN
- Springer US
- Additional Links
- Motion estimation
- Complex wavelet
- Fast two frame algorithm
- Coarse and fine model
- Industry Sectors