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Motion estimation is a major problem for video-coding applications. Among several other motion estimation approaches, block matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity at their software and hardware implementation. The BM approach assumes that the pixel movement inside a given region of the current frame (Macro-Block, MB) can be modeled as a pixel translation from its corresponding region in the previous frame. In this procedure, the motion vector is obtained by minimizing the sum of absolute differences (SAD) from the current frame’s MB over a determined search window from the previous frame. Unfortunately, the SAD evaluation is computationally expensive and represents the most consuming operation in the BM process. The simplest available BM method is the full search algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of SAD values for all elements of the search window. However, several fast BM algorithms have been lately presented to reduce the number of SAD operations by calculating only a fixed subset of search locations at the price of poor accuracy. In this chapter, a new algorithm based on Differential Evolution (DE) is presented to reduce the number of search locations in the BM process. In order to avoid the computing of several search locations, the algorithm estimates the SAD (fitness) values for some locations by considering SAD values from previously calculated neighboring positions. Since the presented algorithm does not consider any fixed search pattern or other different assumption, a high probability for finding the true minimum (accurate motion vector) is expected. In comparison to other fast BM algorithms, the presented method deploys more accurate motion vectors yet delivering competitive time rates.
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Dimitrios Tzovaras, Ioannis Kompatsiaris, Michael G. Strintzis. 3D object articulation and motion estimation in model-based stereoscopic videoconference image sequence analysis and coding. Signal Processing: Image Communication, 14(10), 1999, 817–840.
Barron, J.L., Fleet, D.J., Beauchemin, S.S., 1994. Performance of optical flow techniques. Int. J. Comput. Vision 12 (1), 43–77.
J. Skowronski. Pel recursive motion estimation and compensation in subbands. Signal Processing: Image Communication 14, (1999), 389–396.
MPEG1, Information Technology Coding of Moving Pictures and Associated Audio for Digital Storage Media At Up To About 1.5 mbit/s—Part 2: Video, JTC1/SC29/WG11, ISO/IEC11172-2 (MPEG-1 Video),1993.
MPEG2, Generic Coding of Moving Pictures and Associated Audio Information—Part 2: Video, ITU-T and ISO/IECJTC1, ITURec. H.262—ISO/IEC 13818-2(MPEG-2Video), 1994.
MPEG4, Information Technology Coding of Audio Visual Objects Part 2: Visual, JTC1/SC29/WG11, ISO/IEC14469-2(MPEG-4Visual), 2000.
H261,Video Codec for Audio visual Services at px64 kbit/s, ITU-T SG15, ITU-TRec.H.261, seconded, 1993.
I.-T.R., H.263, Video Coding for Low Bit Rate Communication, ITU-T SG16, ITU-TRec.H.263, thirded, 2000.
J. R. Jain and A. K. Jain, Displacement measurement and its application in interframe image coding, IEEE Trans. Commun., vol. COM-29, pp. 1799–1808, Dec. 1981.
H.-M. Jong, L.-G. Chen, and T.-D. Chiueh, “Accuracy improvement and cost reduction of 3-step search block matching algorithm for video coding,” IEEE Trans. Circuits Syst. Video Technol., vol. 4, pp. 88–90, Feb. 1994.
Renxiang Li, Bing Zeng, and Ming L. Liou, “A New Three-Step Search Algorithm for Block Motion Estimation”, IEEE Trans. Circuits And Systems For Video Technology, vol 4., no. 4, pp. 438–442, August 1994.
Jianhua Lu, and Ming L. Liou, “A Simple and Efficient Search Algorithm for Block-Matching Motion Estimation”, IEEE Trans. Circuits And Systems For Video Technology, vol 7, no. 2, pp. 429–433, April 1997.
Lai-Man Po, and Wing-Chung Ma, “A Novel Four-Step Search Algorithm for Fast Block Motion Estimation”, IEEE Trans. Circuits And Systems For Video Technology, vol 6, no. 3, pp. 313–317, June 1996.
Shan Zhu, and Kai-Kuang Ma, “ A New Diamond Search Algorithm for Fast Block-Matching Motion Estimation”, IEEE Trans. Image Processing, vol 9, no. 2, pp. 287–290, February 2000.
Yao Nie, and Kai-Kuang Ma, Adaptive Rood Pattern Search for Fast Block-Matching Motion Estimation, IEEE Trans. Image Processing, vol 11, no. 12, pp. 1442–1448, December 2002.
Yi-Ching L., Jim L., Zuu-Chang H. Fast block matching using prediction and rejection criteria. Signal Processing, 89, (2009), pp 1115–1120.
Liu, L., Feig, E. A block-based gradient descent search algorithm for block motion estimation in video coding, IEEE Trans. Circuits Syst. Video Technol., 6(4),(1996),419–422.
Saha, A., Mukherjee, J., Sural, S. A neighborhood elimination approach for block matching in motion estimation, Signal Process Image Commun, (2011), 26, 8–9, 2011, 438–454.
K.H.K. Chow, M.L. Liou, Generic motion search algorithm for video compression, IEEE Trans. Circuits Syst. Video Technol. 3, (1993), 440–445.
A. Saha, J. Mukherjee, S. Sural. New pixel-decimation patterns for block matching in motion estimation. Signal Processing: Image Communication 23 (2008)725–738.
Y. Song, T. Ikenaga, S. Goto. Lossy Strict Multilevel Successive Elimination Algorithm for Fast Motion Estimation. IEICE Trans. Fundamentals E90(4), 2007, 764–770.
J.H. Holland, Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI, 1975.
J. Kennedy, R.C. Eberhart, Particle swarm optimization, in: Proceedings of the 1995 IEEE International Conference on Neural Networks, vol. 4, 1995, pp. 1942–1948.
Chun-Hung, L., Ja-Ling W. A Lightweight Genetic Block-Matching Algorithm for Video Coding. IEEE Transactions on Circuits and Systems for Video Technology, 8(4), (1998), 386–392.
Wu, A., So, S. VLSI Implementation of Genetic Four-Step Search for Block Matching Algorithm. IEEE Transactions on Consumer Electronics, 49(4), (2003), 1474–1481.
Yuan, X., Shen, X. Block Matching Algorithm Based on Particle Swarm Optimization. International Conference on Embedded Software and Systems (ICESS2008), 2008, Sichuan, China.
Storn R, Price K. Differential evolution – a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Rep. No. TR-95-012, International Computer Science Institute, Berkley (CA). (1995).
Babu B, Munawar S. Differential evolution strategies for optimal design of shell-and-tube heat exchangers. Chem Eng Sci. 62(14):3720–39. (2007).
Mayer D, Kinghorn B, Archer A. Differential evolution – an easy and efficient evolutionary algorithm for model optimization. Agr Syst, 83:315–28. (2005).
Kannan S, Mary Raja Slochanal S, Padhy N. Application and comparison of metaheuristic techniques to generation expansion planning problem. IEEE Trans Power Syst, 20(1):466–75. (2003).
Chiou J, Chang C, Su C. Variable scaling hybrid differential evolution for solving network reconfiguration of distribution systems. IEEE Trans Power Syst, 20(2):668–74. (2005).
Chiou J, Chang C, Su C. Ant direct hybrid differential evolution for solving large capacitor placement problems. IEEE Trans Power Syst, 19(4):1794–800. (2004).
Ursem R, Vadstrup P. Parameter identification of induction motors using differential evolution. In: Proceedings of the 2003 congress on evolutionary computation (CEC’03), vol. 2, Canberra, Australia, p. 790–6. (2003).
Babu B, Angira R, Chakole G, Syed Mubeen J. Optimal design of gas transmission network using differential evolution. In: Proceedings of the second international conference on computational intelligence, robotics, and autonomous systems (CIRAS-2003), Singapore. (2003).
Zelinka, I., Chen, G., Celikovsky, S.: Chaos synthesis by means of evolutionary algorithms. Int. J. Bifurcat Chaos 4, 911–942 (2008).
E. Cuevas, D. Zaldivar, M. Pérez-Cisneros. A novel multi-threshold segmentation approach based on differential evolution optimization. Expert Systems with Applications 37 (2010) 5265–5271.
Jin, Y. Comprehensive survey of fitness approximation in evolutionary computation. Soft Computing, 9, (2005), 3–12.
Yaochu Jin. Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm and Evolutionary Computation, 1, (2011), 61–70.
J. Branke, C. Schmidt. Faster convergence by means of fitness estimation. Soft Computing 9, (2005), 13–20.
Zhou, Z., Ong, Y., Nguyen, M., Lim, D. A Study on Polynomial Regression and Gaussian Process Global Surrogate Model in Hierarchical Surrogate-Assisted Evolutionary Algorithm, IEEE Congress on Evolutionary Computation (ECiDUE’05), Edinburgh, United Kingdom, September 2–5, 2005.
Ratle, A. Kriging as a surrogate fitness landscape in evolutionary optimization. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 15, (2001), 37–49.
Lim, D., Jin, Y., Ong, Y., Sendhoff, B. Generalizing Surrogate-assisted Evolutionary Computation, IEEE Transactions on Evolutionary Computation, 14(3), (2010), 329–355.
Ong, Y., Lum, K., Nair, P. Evolutionary Algorithm with Hermite Radial Basis Function Interpolants for Computationally Expensive Adjoint Solvers, Computational Optimization and Applications, 39(1), (2008), 97–119.
Luoa, C., Shao-Liang, Z., Wanga, C., Jiang, Z. A metamodel-assisted evolutionary algorithm for expensive optimization. Journal of Computational and Applied Mathematics, doi: 10.1016/j.cam.2011.05.047, (2011).
Goldberg, D. E. Genetic algorithms in search, optimization and machine learning. Menlo Park, (1989) CA: Addison-Wesley Professional.
Li, X., Xiao, N., b, Claramunt, C., Lin, H. Initialization strategies to enhancing the performance of genetic algorithms for the p-median problem, Computers & Industrial Engineering, (2011), doi: 10.1016/j.cie.2011.06.015.
Xiao, N. A unified conceptual framework for geographical optimization using evolutionary algorithms. Annals of the Association of American Geographers, 98, (2008), 795–817. doi: 10.1007/s10732-008-9080-4.
- Motion Estimation
- Chapter 5
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