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A Pattern-Based Artificial Bee Colony Algorithm for Motion Estimation in Video Compression Techniques

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Abstract

Block matching (BM) motion estimation plays an inevitable role in video coding applications. BM approaches are used for data compression. The compression is achieved by removing the temporal redundancy in the video sequences. In the BM process, each video frame is subdivided into macroblocks. Each macroblock in the current frame is compared with the previous frame. The main objective is to minimize sum absolute difference. In this work, some modifications have been performed on conventional artificial bee colony algorithm to improve the conventional BM systems. An initial pattern is used in the proposed algorithm to reduce the computational cost. The computational cost is represented in terms of search points and convergence time. Experimental results results show the improvement for the proposed approach over other block matching algorithms in terms of the performance measures.

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References

  1. J. Cai, W.D. Pan, On fast and accurate block based motion estimation algorithms using particle swarm optimization. Inf. Sci. 197, 53–64 (2012)

    Article  Google Scholar 

  2. H. M. Chen, et al., A modified directional gradient descent searches for fast block motion estimation, in IEEE Conference on Computer, Consumer and Control (IEEE, 2012), pp. 188–191

  3. W. Chen, R.P. Mied, Optical flow estimation for motion compensated compression. Image Vis. Comput. 31, 275–289 (2011)

    Article  Google Scholar 

  4. E. Cuevas et al., Block matching algorithm for motion estimation based on artificial bee colony. Appl. Soft Comput. 13, 3047–3059 (2013a)

    Article  Google Scholar 

  5. E. Cuevas et al., Block matching algorithm based on differential evolution for motion estimation. Eng. Appl. Artif. Intell. 26, 488–498 (2013b)

    Article  Google Scholar 

  6. F. Dufaux, F. Moscheni, Motion estimation techniques for digital TV: a review and a new contribution, in Proceedings of IEEE (IEEE, 1995), pp. 858–879

  7. J.H. Holland, Adaptation in Natural and Artificial Systems (University of Michigan Press, Ann Arbor, 1975)

    Google Scholar 

  8. J.R. Jain, A.K. Jain, Displacement measurement and its application in inter-frame image coding. IEEE Trans. Commun. 29, 1799–1808 (1981)

    Article  Google Scholar 

  9. D. Karaboga, An Idea Based on Honey Bee Swarm for Numerical Optimization: Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department (2005)

  10. D. Karaboga, B. Basturk, A powerful and efficient algorithm for numerical function optimization—artificial bee colony (ABC) algorithm. J. Global Optim. 39, 459–471 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  11. S. Kockanat, N. Karaboga, A novel 2D-ABC adaptive filter algorithm: a comparative study. Digit. Signal Proc. 40, 140–153 (2015)

    Article  MathSciNet  Google Scholar 

  12. T. Koga, et al. Motion compensated interframe coding for video conferencing. in Proceedings of NTC81, C9.6.1-9.6.5 New Orleans, LA (1981)

  13. J. Lu, M.L. Liou, A simple and efficient search algorithm for block-matching motion estimation. IEEE Trans. Circuits Syst. Video Technol. 7, 429–433 (1997)

    Article  Google Scholar 

  14. S.I.A. Pandian, G.J. Bala, J. Anitha, A pattern based PSO approach for block matching in motion estimation. Eng. Appl. Artif. Intell. 26, 1811–1817 (2013)

    Article  Google Scholar 

  15. L.M. Po, W.C. Ma, A novel four-step search algorithm for fast block motion estimation. IEEE Trans. Circuits Syst. Video Technol. 6, 313–317 (1996)

    Article  Google Scholar 

  16. L. Renxiang, B. Zeng, L. Ming, A new three-step search algorithm for block motion estimation. IEEE Trans. Circuits Syst. Video Technol. 4, 438–442 (1994)

    Article  Google Scholar 

  17. A. Saha, J. Mukherjee, S. Sural, A neighborhood elimination approach for block matching in motion estimation. Sig. Process. Image Commun. 26, 438–454 (2011)

    Article  Google Scholar 

  18. Y. Song, T. Ikenaga, S. Goto, Lossy strict multilevel successive elimination algorithm for fast motion estimation. IEICE Trans. Fundam. E90(4), 764–770 (2007)

    Article  Google Scholar 

  19. M. Tok, A. Glantz, A. Krutz, T. Sikora, Monte-Carlo-based parametric motion estimation using a hybrid model approach. IEEE Trans. Circuits Syst. Video Technol. 23, 607–620 (2013)

    Article  Google Scholar 

  20. R. Vamshikrishna, P. Muralidhar, R. Rama, Adaptive diamond search step search motion estimation algorithm for H.264/AVC. in IEEE Conference on Advanced Communication Control and Computing Technologies (IEEE, 2014), pp. 1389–1392

  21. R. Verma, M. Y. Dabbagh, Binary pattern based edge detection for motion estimation in H.264/AVC, in IEEE Canadian Conference of Electrical and Computer Engineering (IEEE, 2013)

  22. C. Yan et al., A highly parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Signal Process. Lett. 21, 573–576 (2014)

    Article  Google Scholar 

  23. C. Yan et al., Efficient parallel framework for HEVC motion estimation on many-core processors. IEEE Trans. Circuits Syst. Video Technol. 24, 2077–2089 (2014)

    Article  Google Scholar 

  24. N. Yao, M.K. Kuang, Adaptive rood pattern search for fast block-matching motion estimation. IEEE Trans. Image Process. 11, 1442–1448 (2002)

    Article  Google Scholar 

  25. F. Zhong, H. Li, S. Zhong, A modified ABC algorithm based on improved-global-best-guided approach and adaptive-limit strategy for global optimization. Appl. Soft Comput. 46, 469–486 (2016)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Karunya University, Coimbatore, India

    D. Jude Hemanth & J. Anitha

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  1. D. Jude Hemanth
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  2. J. Anitha
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Correspondence to D. Jude Hemanth.

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Hemanth, D.J., Anitha, J. A Pattern-Based Artificial Bee Colony Algorithm for Motion Estimation in Video Compression Techniques. Circuits Syst Signal Process 37, 1609–1624 (2018). https://doi.org/10.1007/s00034-017-0613-7

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  • DOI: https://doi.org/10.1007/s00034-017-0613-7

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