Share This Article:

Video Compression USING a New Active Mesh Based Motion Compensation Algorithm in Wavelet Sub-Bands

Abstract Full-Text HTML Download Download as PDF (Size:3906KB) PP. 368-376
DOI: 10.4236/jsip.2012.33048    4,037 Downloads   6,588 Views   Citations

ABSTRACT

In this paper, a new mesh based algorithm is applied for motion estimation and compensation in the wavelet domain. The first major contribution of this work is the introduction of a new active mesh based method for motion estimation and compensation. The proposed algorithm is based on the mesh energy minimization with novel sets of energy functions. The proposed energy functions have appropriate features, which improve the accuracy of motion estimation and compensation algorithm. We employ the proposed motion estimation algorithm in two different manners for video compression. In the first approach, the proposed algorithm is employed for motion estimation of consecutive frames. In the second approach, the algorithm is applied for motion estimation and compensation in the wavelet sub-bands. The experimental results reveal that the incorporation of active mesh based motion-compensated temporal filtering into wavelet sub-bands significantly improves the distortion performance rate of the video compression. We also use a new wavelet coder for the coding of the 3D volume of coefficients based on the retained energy criteria. This coder gives the maximum retained energy in all sub-bands. The proposed algorithm was tested with some video sequences and the results showed that the use of the proposed active mesh method for motion compensation and its implementation in sub-bands yields significant improvement in PSNR performance.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

M. Bisjerdi and A. Behrad, "Video Compression USING a New Active Mesh Based Motion Compensation Algorithm in Wavelet Sub-Bands," Journal of Signal and Information Processing, Vol. 3 No. 3, 2012, pp. 368-376. doi: 10.4236/jsip.2012.33048.

References

[1] ITU-T Recommendation T.800. JPEG2000 Image Coding System, Part 1, ITU Std., July 2002.
[2] A. Skodras, C. Christopoulis and T. Ebrahimi, “The JPEG2000 Still Image Compression Standard,” IEEE Signal Processing Magazine, Vol. 18, No. 5, 2001, pp. 36-58. doi:10.1109/79.952804
[3] S.-J. Choi and J. W. Woods, “Motion-Compensated 3-D Subband Coding of Video,” IEEE Transactions on Image Processing, Vol. 8, No. 2, 1999, pp. 155-167. doi:10.1109/83.743851
[4] P. Chen and J. W. Woods, “Bidirectional MC-EZBC with Lifting Implementation,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 14, No. 10, 2004, pp. 1183-1194. doi:10.1109/TCSVT.2004.833165
[5] Y. M. Chi and T. D. Tran and R. Etinne-cummings, “Optical Flow Approximation of Sub-Pixel Accurate Block Matching for Video Coding,” Proceedings of IEEE International Conference on Acoustic, Speech and Signal Processing, Honolulu, 15-20 April 2007, pp. 1017-1020.
[6] D. C. Liu and S. Cheng, “A Brief Introduction of Feature Matching,” Proceedings of 2008 IEEE Region 5 Conference, Kansas, 17-20 April 2008, pp. 1-4.
[7] M. Eckert, D. Ruiz, J. I. Ronda and N. Garcia, “Evaluation of DWT and DCT for Irregular Mesh-Based Motion Compensation in Predictive Video Coding,” In: K. N. Ngan, T. Sikora and M.-T. Sun, Eds., Visual Communications and Image Processing, Proceedings of SPIE 4067, 2000, pp. 447-456.
[8] B. Song, A. Roy-Chowdhury and E. Tuncel, “A Multi- Terminal Model-Based Video Compression Algorithm,” Proceedings of IEEE International Conference on Image Processing, Atlanta, 8-11 October 2006, pp. 265-268.
[9] B. Lucas and T. Kanade, “An Iterative Image Registration Technique with an Application to Stereovision,” Proceeding of DARPA Image Understanding Workshop, 1981, pp. 121-130.
[10] J.-Y. Bouguet, “Pyramidal Implementation of Lucas Kanade Feature Tracker Description of the Algorithm,” Intel Corporation, Microprocessor Research Labs, OpenCV Documentation, May 2001.
[11] N. Bo?inovi? and J. Konrad, “Mesh-Based Motion Models for Wavelet Video Coding,” Proceedings of IEEE International Conference on Acoustics Speech Signal Processing, Vol. 3, 17-21 May 2004, pp. 141-144.
[12] J. Shi and C. Tomasi, “Good Features to Track,” IEEE International Conference on Computer Vision and Pattern Recognition, Seattle, 21-23 June1994, pp. 593-600.
[13] J. R. Shewchuk, “Delaunay Refinement Algorithms for Triangular Mesh Generation,” Computational Geometry, Vol. 22, No. 1-3, 2002, pp. 21-74.
[14] A. Said and W. A. Pearlman, “A New, Fast, and Efficient Image Codec Based on Set Partitioning in Hierarchical Trees,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 6, No. 3, 1996, pp. 243-250. doi:10.1109/76.499834
[15] S. Li and W. Li, “Shape-Adaptive Discrete Wavelet Transforms for Arbitrarily Shaped Visual Object Coding,” IEEE Transactions on Circuits and Systems for Video Coding, Vol. 10, No. 5, 2000, pp. 725-743.
[16] G. Minami, Z. Xiong, A. Wang and S. Mehrota, “3-D Wavelet Coding of Video with Arbitrary Regions of Support,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 11, No. 9, 2001, pp. 1063-1068. doi:10.1109/76.946523
[17] K. M. Lam and H. Yan, “Locating Head Boundary by Snakes,” International Symposium on Speech, Image Processing and Neural Networks, Vol. 1, 13-16 April 1994, pp. 17-20.
[18] Y. Wang, S. Cui, and J. E. Fowler, “3D Video Coding Using Redundant-Wavelet Multihypothesis and Motion- Compensated Temporal Filtering,” Proceedings of the IEEE International Conference on Image Processing, Barcelona, 14-17 September 2003, pp. 755-758.

  
comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.