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Block-matching algorithm based on harmony search optimization for motion estimation

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Abstract

Motion estimation is one of the major problems in developing video coding applications. Among all motion estimation approaches, Block-matching (BM) algorithms are the most popular methods due to their effectiveness and simplicity for both software and hardware implementations. A BM approach assumes that the movement of pixels within a defined region of the current frame can be modeled as a translation of pixels contained in the previous frame. In this procedure, the motion vector is obtained by minimizing a certain matching metric that is produced for the current frame over a determined search window from the previous frame. Unfortunately, the evaluation of such matching measurement is computationally expensive and represents the most consuming operation in the BM process. Therefore, BM motion estimation can be viewed as an optimization problem whose goal is to find the best-matching block within a search space. The simplest available BM method is the Full Search Algorithm (FSA) which finds the most accurate motion vector through an exhaustive computation of all the elements of the search space. Recently, several fast BM algorithms have been proposed to reduce the search positions by calculating only a fixed subset of motion vectors despite lowering its accuracy. On the other hand, the Harmony Search (HS) algorithm is a population-based optimization method that is inspired by the music improvisation process in which a musician searches for harmony and continues to polish the pitches to obtain a better harmony. In this paper, a new BM algorithm that combines HS with a fitness approximation model is proposed. The approach uses motion vectors belonging to the search window as potential solutions. A fitness function evaluates the matching quality of each motion vector candidate. In order to save computational time, the approach incorporates a fitness calculation strategy to decide which motion vectors can be only estimated or actually evaluated. Guided by the values of such fitness calculation strategy, the set of motion vectors is evolved through HS operators until the best possible motion vector is identified. The proposed method has been compared to other BM algorithms in terms of velocity and coding quality. Experimental results demonstrate that the proposed algorithm exhibits the best balance between coding efficiency and computational complexity.

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References

  1. Cirrincione G, Cirrincione M (2003) A novel self-organizing neural network for motion segmentation. Appl Intell 18(1):27–35

    Article  MATH  Google Scholar 

  2. Risinger L, Kaikhah K (2008) Motion detection and object tracking with discrete leaky integrate-and-fire neurons. Appl Intell 29(3):248–262

    Article  Google Scholar 

  3. Bohlooli A, Jamshidi K (2012) A GPS-free method for vehicle future movement directions prediction using SOM for VANET. Appl Intell 36(3):685–697

    Article  Google Scholar 

  4. Kang J-G, Kim S, An S-Y, Oh S-Y (2012) A new approach to simultaneous localization and map building with implicit model learning using neuro evolutionary optimization. Appl Intell 36(1):242–269

    Article  Google Scholar 

  5. Tzovaras D, Kompatsiaris I, Strintzis MG (1999) 3D object articulation and motion estimation in model-based stereoscopic videoconference image sequence analysis and coding. Signal Process Image Commun 14(10):817–840

    Article  Google Scholar 

  6. Barron JL, Fleet DJ, Beauchemin SS (1994) Performance of optical flow techniques. Int J Comput Vis 12(1):43–77

    Article  Google Scholar 

  7. Skowronski J (1999) Pel recursive motion estimation and compensation in subbands. Signal Process Image Commun 14:389–396

    Article  Google Scholar 

  8. Huang T, Chen C, Tsai C, Shen C, Chen L (2006) Survey on block matching motion estimation algorithms and architectures with new results. J VLSI Signal Process 42:297–320

    Article  MATH  Google Scholar 

  9. MPEG4 (2000) information technology coding of audio visual objects part 2: visual, JTC1/SC29/WG11, ISO/IEC14469-2 (MPEG-4Visual)

  10. H.264 (2003) Joint video team (JVT) of ITU-T and ISO/IEC JTC1, Geneva, JVT of ISO/IEC MPEG and ITU-T VCEG, JVT-g050r1, draft ITU-TRec. and final draft international standard of joint video specification (ITU-T Rec.H.264-ISO/IEC14496-10AVC)

  11. Jain JR, Jain AK (1981) Displacement measurement and its application in interframe image coding. IEEE Trans Commun COM-29:1799–1808

    Article  Google Scholar 

  12. Jong H-M, Chen L-G, Chiueh T-D (1994) Accuracy improvement and cost reduction of 3-step search block matching algorithm for video coding. IEEE Trans Circuits Syst Video Technol 4:88–90

    Article  Google Scholar 

  13. Li R, Zeng B, Liou ML (1994) A new three-step search algorithm for block motion estimation. IEEE Trans Circuits Syst Video Technol 4(4):438–442

    Article  Google Scholar 

  14. Lu J, Liou ML (1997) A simple and efficient search algorithm for block-matching motion estimation. IEEE Trans Circuits Syst Video Technol 7(2):429–433

    Article  Google Scholar 

  15. Po L-M, Ma W-C (1996) A novel four-step search algorithm for fast block motion estimation. IEEE Trans Circuits Syst Video Technol 6(3):313–317

    Article  Google Scholar 

  16. Zhu S, Ma K-K (2000) A new diamond search algorithm for fast block-matching motion estimation. IEEE Trans Image Process 9(2):287–290

    Article  MathSciNet  Google Scholar 

  17. Nie Y, Ma K-K (2002) Adaptive rood pattern search for fast block-matching motion estimation. IEEE Trans Image Process 11(12):1442–1448

    Article  Google Scholar 

  18. Liaw Y, Lai JZC, Hong Z (2009) Fast block matching using prediction and rejection criteria. Signal Process 89:1115–1120

    Article  MATH  Google Scholar 

  19. Liu L, Feig E (1996) A block-based gradient descent search algorithm for block motion estimation in video coding. IEEE Trans Circuits Syst Video Technol 6(4):419–422

    Article  Google Scholar 

  20. Saha A, Mukherjee J, Sural S (2011) A neighborhood elimination approach for block matching in motion estimation. Signal Process Image Commun 26(8–9):438–454

    Article  Google Scholar 

  21. Chow KHK, Liou ML (1993) Generic motion search algorithm for video compression. IEEE Trans Circuits Syst Video Technol 3:440–445

    Article  Google Scholar 

  22. Saha A, Mukherjee J, Sural S (2008) New pixel-decimation patterns for block matching in motion estimation. Signal Process Image Commun 23:725–738

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Tourapis AM (2002) Enhanced predictive zonal search for single and multiple frame motion estimation. In: Proceedings of visual communications and image processing, California, USA, January 2002, pp 1069–1079

    Google Scholar 

  25. Chen Z, Zhou P, He Y, Chen Y (2002) Fast integer Pel and fractional Pel motion estimation for JVT, December, 2002, ITU-T. Doc. #JVT-F-017

  26. Nisar H, Malik AS, Choi T-S (2012) Content adaptive fast motion estimation based on spatio-temporal homogeneity analysis and motion classification. Pattern Recognit Lett 33:52–61

    Article  Google Scholar 

  27. Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  28. Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE international conference on neural networks, vol 4, pp 1942–1948

    Google Scholar 

  29. Lin C, Wu JL (1998) A lightweight genetic block-matching algorithm for video coding. IEEE Trans Circuits Syst Video Technol 8(4):386–392

    Article  MathSciNet  Google Scholar 

  30. Wu A, So S (2003) SVLSI implementation of genetic four-step search for block matching algorithm. IEEE Trans Consum Electron 49(4):1474–1481

    Article  Google Scholar 

  31. Yuan X, Shen X (2008) Block matching algorithm based on particle swarm optimization. In: International conference on embedded software and systems (ICESS2008), Sichuan, China

    Google Scholar 

  32. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulations 76:60–68

    Article  Google Scholar 

  33. Mahdavi M, Fesanghary M, Damangir E (2007) An improved harmony search algorithm for solving optimization problems. Appl Math Comput 188:1567–1579

    Article  MathSciNet  MATH  Google Scholar 

  34. Omran MGH, Mahdavi M (2008) Global-best harmony search. Appl Math Comput 198:643–656

    Article  MathSciNet  MATH  Google Scholar 

  35. Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization, harmony search theory and practice. Comput Methods Appl Mech Eng 194:3902–3933

    Article  MATH  Google Scholar 

  36. Lee KS, Geem ZW, Lee SH, Bae K-W (2005) The harmony search heuristic algorithm for discrete structural optimization. Eng Optim 37:663–684

    Article  MathSciNet  Google Scholar 

  37. Kim JH, Geem ZW, Kim ES (2001) Parameter estimation of the nonlinear Muskingum model using harmony search. J Am Water Resour Assoc 37:1131–1138

    Article  Google Scholar 

  38. Geem ZW (2006) Optimal cost design of water distribution networks using harmony search. Eng Optim 38:259–280

    Article  Google Scholar 

  39. Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82:781–798

    Article  Google Scholar 

  40. Ayvaz TM (2007) Simultaneous determination of aquifer parameters and zone structures with fuzzy c-means clustering and meta-heuristic harmony search algorithm. Adv Water Resour 30:2326–2338

    Article  Google Scholar 

  41. Geem ZW, Lee KS, Park YJ (2005) Application of harmony search to vehicle routing. Am J Appl Sci 2:1552–1557

    Article  Google Scholar 

  42. Geem ZW (2008) Novel derivative of harmony search algorithm for discrete design variables. Appl Math Comput 199(1):223–230

    Article  MathSciNet  MATH  Google Scholar 

  43. Cuevas E, Ortega-Sánchez N, Zaldivar D, Pérez-Cisneros M (2012) Circle detection by harmony search optimization. J Intell Robot Syst 66(3):359–376

    Article  Google Scholar 

  44. Jin Y (2005) Comprehensive survey of fitness approximation in evolutionary computation. Soft Comput 9:3–12

    Article  Google Scholar 

  45. Jin Y (2011) Surrogate-assisted evolutionary computation: recent advances and future challenges. Swarm Evol Comput 1:61–70

    Article  Google Scholar 

  46. Branke J, Schmidt C (2005) Faster convergence by means of fitness estimation. Soft Comput 9:13–20

    Article  Google Scholar 

  47. Zhou Z, Ong Y, Nguyen M, Lim D (2005) A study on polynomial regression and Gaussian process global surrogate model in hierarchical surrogate-assisted evolutionary algorithm. In: IEEE congress on evolutionary computation (ECiDUE’05), Edinburgh, United Kingdom, September 2–5, 2005, pp 2–5

    Google Scholar 

  48. Ratle A (2001) Kriging as a surrogate fitness landscape in evolutionary optimization. Artif Intell Eng Des Anal Manuf 15:37–49

    Article  Google Scholar 

  49. Lim D, Jin Y, Ong Y, Sendhoff B (2010) Generalizing surrogate-assisted evolutionary computation. IEEE Trans Evol Comput 14(3):329–355

    Article  Google Scholar 

  50. Ong Y, Lum K, Nair P (2008) Evolutionary algorithm with Hermite radial basis function interpolants for computationally expensive adjoint solvers. Comput Optim Appl 39(1):97–119

    Article  MathSciNet  MATH  Google Scholar 

  51. Luoa C, Shao-Liang Z, Wanga C, Jiang Z (2011) A metamodel-assisted evolutionary algorithm for expensive optimization. J Comput Appl Math. doi:10.1016/j.cam.2011.05.047

    Google Scholar 

  52. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Menlo Park

    MATH  Google Scholar 

  53. Li X, Xiao N, Claramunt C, Lin H (2011) Initialization strategies to enhancing the performance of genetic algorithms for the p-median problem. Comput Ind Eng. doi:10.1016/j.cie.2011.06.015

    Google Scholar 

  54. Xiao N (2008) A unified conceptual framework for geographical optimization using evolutionary algorithms. Ann Assoc Am Geogr 98:795–817

    Article  Google Scholar 

  55. Soak S-M, Lee S-W (2012) A memetic algorithm for the quadratic multiple container packing problem. Appl Intell 36(1):119–135

    Article  Google Scholar 

  56. Luque C, Valls JM, Isasi P (2011) Time series prediction evolving Voronoi regions. Appl Intell 34(1):116–126

    Article  Google Scholar 

  57. Montero E, Riff M-C (2011) On-the-fly calibrating strategies for evolutionary algorithms. Inf Sci 181:552–566

    Article  Google Scholar 

  58. Tan KC, Chiam SC, Mamun AA, Goh CK (2009) Balancing exploration and exploitation with adaptive variation for evolutionary multi-objective optimization. Eur J Oper Res 197(2):701–713

    Article  MATH  Google Scholar 

  59. Wang P, Zhang J, Xub L, Wang H, Feng S, Zhu H (2011) How to measure adaptation complexity in evolvable systems—a new synthetic approach of constructing fitness functions. Expert Syst Appl 38:10414–10419

    Article  Google Scholar 

  60. Tenne Y (2012) A computational intelligence algorithm for expensive engineering optimization problems. Eng Appl Artif Intell 25(5):1009–1021

    Article  Google Scholar 

  61. Büche D, Schraudolph NN, Koumoutsakos P (2005) Accelerating evolutionary algorithms with Gaussian process fitness function models. IEEE Trans Syst Man Cybern, Part C, Appl Rev 35(2):183–194

    Article  Google Scholar 

  62. Giannakoglou KC, Papadimitriou DI, Kampolis IC (2006) Aerodynamic shape design using evolutionary algorithms and new gradient-assisted metamodels. Comput Methods Appl Mech Eng 195:6312–6329

    Article  MATH  Google Scholar 

  63. Tai S-C, Chen Y-R, Chen Y-H (2007) Small-diamond-based search algorithm for fast block motion estimation. Signal Process Image Commun 22:877–890

    Article  Google Scholar 

  64. Joint Video Team Reference Software (2007). Version 12.2 (JM12.2). http://iphome.hhi.de/suehring/tml/download/

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  1. Departamento de Electrónica, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, C.P. 44430, Guadalajara, Jal, Mexico

    Erik Cuevas

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  1. Erik Cuevas
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Cuevas, E. Block-matching algorithm based on harmony search optimization for motion estimation. Appl Intell 39, 165–183 (2013). https://doi.org/10.1007/s10489-012-0403-7

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