Skip to main content
SpringerLink
Log in

Block Matching Algorithms for the Estimation of Motion in Image Sequences: Analysis

  • MATHEMATICAL THEORY OF IMAGES AND SIGNALS REPRESENTING, PROCESSING, ANALYSIS, RECOGNITION, AND UNDERSTANDING
  • Published:
Pattern Recognition and Image Analysis Aims and scope Submit manuscript

Abstract

Several video coding standards and techniques have been introduced for multimedia applications, particularly h.26x series for video processing. These standards employ motion estimation process for reducing the amount of data that is required to store or transmit the video. Motion estimation process is an inextricable part of the video coding as it removes the temporal redundancy between successive frames of video sequences. This paper is about these motion estimation algorithms, their search procedures, complexity, advantages, and limitations. A survey of motion estimation algorithms including full search algorithm, many fast search, and fast full search block-based algorithms has been presented. An evaluation of up to date motion estimation algorithms, based on a number of empirical results on several test video sequences, is presented as well.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.

Similar content being viewed by others

REFERENCES

  1. Z. Chen, J. Xu, Y. He, and J. Zheng, “Fast integer-pel and fractional-pel motion estimation for H.264/AVC,” J. Visual Commun. Image Representation 17, 264–290 (2006). https://doi.org/10.1016/j.jvcir.2004.12.002

    Article  Google Scholar 

  2. C.-H. Cheung and L.-M. Po, “A novel cross-diamond search algorithm for fast block motion estimation,” IEEE Trans. Circuits Syst. Video Technol. 12, 1168–1177 (2002). https://doi.org/10.1109/TCSVT.2002.806815

    Article  Google Scholar 

  3. C.-H. Cheung and L.-M. Po, “Novel cross-diamond-hexagonal search algorithms for fast block motion estimation,” IEEE Trans. Multimedia 7, 16–22 (2005). https://doi.org/10.1109/TMM.2004.840609

    Article  Google Scholar 

  4. G. Cote, B. Erol, M. Gallant, and F. Kossentini, “H.263+: Video coding at low bit rates,” IEEE Trans. Circuits Syst. Video Technol. 8, 849–866 (1998). https://doi.org/10.1109/76.735381

    Article  Google Scholar 

  5. S. Dikbas, T. Arici, and Y. Altunbasak, “Fast motion estimation with interpolation-free sub-sample accuracy,” IEEE Trans. Circuits Syst. Video Technol. 20, 1047–1051 (2010). https://doi.org/10.1109/TCSVT.2010.2051283

    Article  Google Scholar 

  6. D. Droeschel, J. Stückler, S. Behnke, “Local multi-resolution representation for 6D motion estimation and mapping with a continuously rotating 3D laser scanner,” in IEEE Int. Conf. on Robotics and Automation (ICRA), Hong Kong, 2014 (IEEE, 2014), pp. 5221–5226. https://doi.org/10.1109/ICRA.2014.6907626

  7. F. Dufaux and F. Moscheni, “Motion estimation techniques for digital TV: a review and a new contribution,” Proc. IEEE 83, 858–876 (1995). https://doi.org/10.1109/5.387089

    Article  Google Scholar 

  8. X. Q. Gao, C. J. Duanmu, and C. R. Zou, “A Multilevel Successive Elimination Algorithm for block matching motion estimation,” IEEE Trans. Image Process. 9, 501–504 (2000). https://doi.org/10.1109/83.826786

    Article  Google Scholar 

  9. S.-Y. Huang, C.-Y. Cho, and J.-S. Wang, “Adaptive fast block-matching algorithm by switching search patterns for sequences with wide-range motion content,” IEEE Trans. Circuits Syst. Video Technol. 15, 1373–1384 (2005). https://doi.org/10.1109/TCSVT.2005.856931

    Article  Google Scholar 

  10. ITU-T Rec. H.263, “Video coding for low bit rate communication,” v1 (1995); v2 (1998); v3 (2000).

  11. ITU-T Rec. H.264 and ISO/IEC 14496–10 (MPEG4-AVC), “Advanced video coding for generic audiovisual services,” v1 (2003); v2 (2004); v3 (with FRExt) (2004); v4 (2005).

  12. J. Jain and A. Jain, “Displacement measurement and its application in interframe image−coding,” IEEE T. Commun. 29, 1799–1808 (1981).  https://doi.org/10.1109/TCOM.1981.1094950

    Article  Google Scholar 

  13. J.-J. Tsai and H.-M. Hang, “On adaptive pattern selection for block motion estimation algorithms,” in IEEE Int. Conf. on Acoustics, Speech Signal Processing–ICASSP’07, Honolulu, 2007 (IEEE, 2007), pp. 1173–1176. https://doi.org/10.1109/ICASSP.2007.366122

  14. J.-Bin Xu, L.-M. Po, and C.-K. Cheung, “Adaptive motion tracking block matching algorithms for video coding”, IEEE Trans. Circuits Syst. Video Technol. 9, 1025–1029 (1999). https://doi.org/10.1109/76.795056

    Article  Google Scholar 

  15. J.-H. Jung, H.-S. Lee, J. H. Lee, and D.-J. Park, “A novel template matching scheme for fast full-search boosted by an integral image,” IEEE Signal Process. Lett. 17, (2010). https://doi.org/10.1109/LSP.2009.2032452

  16. J.-N. Kim, D.-K. Kang, S.-C. Byun, I.-L. Lee, and B.‑H. Ahn, “A fast full-search motion estimation algorithm using sequential rejection of candidates from hierarchical decision structure,” IEEE Trans. Broadcasting 48, 43–46 (2002). https://doi.org/10.1109/11.992854

    Article  Google Scholar 

  17. J T. Koga, K. linuma, A. Hirano, Y. Iijima, and T. Ishiguro, “Motion compensated interframe coding for video conferencing,” in Proc. Nat. Telecommun. Conf. (1981), pp. C9.6.1–C9.6.5.

  18. C.-M. Kuo, Y.-H. Kuan, C.-H. Hsieh, and Y.-H. Lee, “A novel prediction-based directional asymmetric search algorithm for fast block-matching motion estimation,” IEEE Trans. Circuits Syst. Video Technol. 19, 893–899 (2009).

    Article  Google Scholar 

  19. S. C. Kwatra, C-M Lin, and W. A. Whyte, “An adaptive algorithm for motion compensated color image coding,” IEEE Trans. Commun. 35, 747–754 (1987). https://doi.org/10.1109/TCOM.1987.1096840

    Article  Google Scholar 

  20. L.-M. Po and W.-C. Ma, “A novel four-step search algorithm for fast block motion estimation,” IEEE Trans. Circuits Syst. Video Technol. 6, 313–317 (1996). https://doi.org/10.1109/76.499840

    Article  Google Scholar 

  21. L.-M. Po, K.-H. Ng, K.-W. Cheung, K.-M. Wong, Y. Md. Salah Uddin, and C.-W. Ting, “Novel directional gradient descent searches for fast block motion estimation,” IEEE Trans. Circuits Syst. Video Technol. 19, 1189–1195 (2009). https://doi.org/10.1109/TCSVT.2009.2020320

    Article  Google Scholar 

  22. L.-M. Po, C.-W. Ting, K.-M. Wong, and K.-H. Ng, “Novel point oriented inner searches for fast block motion estimation,” IEEE Trans. Multimedia 9, 9–15 (2007). https://doi.org/10.1109/TMM.2006.886330

    Article  Google Scholar 

  23. J.-B. Lee and H. Kalva, The VC−1 and H.264 Video Compression Standards for Broadband Video Services, Multimedia Systems and Applications (Springer, Boston, 2008). https://doi.org/10.1007/978-0-387-71043-3

  24. L.-W. Lee, J.-F. Wang, J.-Y. Lee, and J.-D. Shie, “Dynamic search window adjustment and interlaced search for block-matching algorithm,” IEEE Trans. Circuits Syst. Video Technol. 3, 85–87 (1993). https://doi.org/10.1109/76.180692

    Article  Google Scholar 

  25. C.-H. Lee and L.-H. Chen, “A fast motion estimation algorithm based on the block sum pyramid,” IEEE Trans. Image Process. 6, 1587–1591 (1997). https://doi.org/10.1109/83.641419

    Article  Google Scholar 

  26. R. Li, B. Zeng, and M. L. Liou, “A new three-step search algorithm for block motion estimation,” IEEE Trans. Circuits Syst. Video Technol. 4, 438–442 (1994). https://doi.org/10.1109/76.313138

    Article  Google Scholar 

  27. W. Li and E. Salari, “Successive elimination algorithm for motion estimation,” IEEE Trans. Image Process. 4, 105–107 (1995). https://doi.org/10.1109/83.350809

    Article  Google Scholar 

  28. Y. Lin and Y. C. Wang, “Improved parabolic prediction-based fractional search for H.264/AVC video coding,” Image Process. IET 3, 261–271 (2009). https://doi.org/10.1049/iet-ipr.2008.0192

    Article  Google Scholar 

  29. L.-K. Liu and E. Feig, “A block-based gradient descent search algorithm for block motion estimation in video coding,” IEEE Trans. Circuits Syst. Video Technol. 6, 419–422 (1996). https://doi.org/10.1109/76.510936

    Article  Google Scholar 

  30. S.-W. Liu, S.-D. Wei, and S.-H. Lai, “Fast optimal motion estimation based on gradient-based adaptive multilevel successive elimination,” IEEE Trans. Circuits Syst. Video Technol. 18, 156–160, (2008). https://doi.org/10.1109/TCSVT.2007.913973

    Article  Google Scholar 

  31. L. Luo, C. Zou, X. Gao, and Z. He, “A new prediction search algorithm for block motion estimation in video coding,” IEEE Trans. Consum. Electron. 43, 56–61, (1997). https://doi.org/10.1109/30.580385

    Article  Google Scholar 

  32. H. G. Musmann, P. Pirsch, and H.-J. Grallert, “Advances in picture coding,” Proc. IEEE 73, 523–548, (1985). https://doi.org/10.1109/PROC.1985.13183

    Article  Google Scholar 

  33. K.-H. Ng, L.-M. Po, and K.-M. Wong, “Search patterns switching for motion estimation using rate of error descent,” in IEEE Int. Conf. on Multimedia and Expo, Beijing, 2007 (IEEE, 2007), pp. 1583–1586. https://doi.org/10.1109/ICME.2007.4284967

  34. K.-H. Ng, L.-M. Po, K.-M. Wong, C.-W. Ting, and K.-W. Cheung, “A search patterns switching algorithm for block motion estimation,” IEEE Trans. Circuits Syst. Video Technol. 19, 753–759 (2009). https://doi.org/10.1109/TCSVT.2009.2017414

    Article  Google Scholar 

  35. Y. Nie and K.-K. Ma, “Adaptive rood pattern search for fast block matching motion estimation,” IEEE Trans. Image Process. 11, 1442–1449 (2002). https://doi.org/10.1109/TIP.2002.806251

    Article  Google Scholar 

  36. M. Nieuwenhuisen, S. Behnke, “Hierarchical planning with 3D local multiresolution obstacle avoidance for micro aerial vehicles,” in ISR/Robotik 2014; 41st Int. Symp. on Robotics, Munich, 2014 (VDE, 2014), pp. 1–7.

  37. K. K. Ma and G. Qiu, “An improved adaptive rood pattern search for fast block-matching motion estimation in JVT/H.26L,” in Proc. of the 2003 Int. Symp. on Circuits and Systems, ISCAS’03, Bangkok, 2003 (IEEE, 2003), vol. 2, pp. 25–28. https://doi.org/10.1109/ISCAS.2003.1206072

  38. P. I. Hosur and K. K. Ma, “Motion vector field adaptive fast motion estimation,” in Second Int. Conf. on Information, Communications, and Signal Processing (ICICS’99), Singapore, 1999.

  39. R. Srinivasan and K.R. Rao, “Predictive coding based on efficient motion estimation,” IEEE T. Commun. 33, 888–896 (1985). https://doi.org/10.1109/TCOM.1985.1096398

    Article  Google Scholar 

  40. L. Shen, Z. Zhang, Z. Liu, and W. Zhang, “An adaptive and fast fractional pixel search algorithm in H.264,” Signal Process. 87, 2629–2639 (2007). https://doi.org/10.1016/j.sigpro.2007.04.013

    Article  MATH  Google Scholar 

  41. B. C. Song and K.-W. Chun, “Multi-resolution block matching algorithm and its VLSI architecture for fast motion estimation in an MPEG-2 video encoder,” IEEE Trans. Circuits Syst. Video Technol. 14, 1119–1137 (2004). https://doi.org/10.1109/TCSVT.2004.833161

    Article  Google Scholar 

  42. J. Stuckler and S. Behnke, “Multi-resolution surfel maps for efficient dense 3D modeling and tracking,” J. Visual Commun. Image Representation 25, 137–147 (2014). https://doi.org/10.1016/j.jvcir.2013.02.008

    Article  Google Scholar 

  43. J. Y. Tham, S. Ranganath, M. Ranganath, and A. A. Kassim, “A novel unrestricted center-biased diamond search algorithm for block motion estimation,” IEEE Trans. Circuits Syst. Video Technol. 8, 369–377 (1998). https://doi.org/10.1109/76.709403

    Article  Google Scholar 

  44. A. M. Tourapis, O. C. Au, and M. L. Liou, “Predictive motion vector field adaptive search technique (PMVFAST) enhancing block based motion estimation,” Proc. SPIE 4310, 883–892 (2001). https://doi.org/10.1117/12.411871

    Article  Google Scholar 

  45. A. M. Tourapis, “Optimization model version 1.0,” in ISO/IEC JTC1/SC29/WG11 MPEG2000/N3324 (Noordwijkerhout, Netherlands, 2000).

  46. A. M. Tourapis, “Enhanced predictive zonal search for single and multiple frame motion estimation,” Proc. SPIE 4671, 1069–1079 (2002). https://doi.org/10.1117/12.453031

    Article  Google Scholar 

  47. F. Varray and H. Liebgott, “Multi-resolution transverse oscillation in ultrasound imaging for motion estimation,” IEEE Trans. Ultrason., Ferroelectr., Freq. Control 60, 1333–1342 (2013). https://doi.org/10.1109/TUFFC.2013.2707

    Article  Google Scholar 

  48. Y. Vatis and J. Ostermann, “Adaptive interpolation filter for H.264/AVC,” IEEE Trans. Circuits Syst. Video Technol. 19, 179–192 (2009). https://doi.org/10.1109/TCSVT.2008.2009259

    Article  Google Scholar 

  49. T. Wedi, “Adaptive interpolation filters and high-resolution displacements for video coding,” IEEE Trans. Circuits Syst. Video Technol. 16, 484–491 (2006). https://doi.org/10.1109/TCSVT.2006.870856

    Article  Google Scholar 

  50. H. Yin, H. Jia, H. Qi, X. Ji, X. Xie, and W. Gao, “A hardware-efficient multi-resolution block matching algorithm and its VLSI architecture for high definition MPEG-like video encoders,” IEEE Trans. Circuits Syst. Video Technol. 20, 1242–1254 (2010). https://doi.org/10.1109/TCSVT.2010.2058476

    Article  Google Scholar 

  51. Y.-H. Ko, H.-S. Kang, and S.-W. Lee, “Adaptive search range motion estimation using neighboring motion vector differences,” IEEE Trans. Consum. Electron. 57, 726–730 (2011). https://doi.org/10.1109/TCE.2011.5955214

    Article  Google Scholar 

  52. Y.-W. Huang, S.-Y. Chien, B.-Y. Hsieh, and L.-G. Chen, “Global elimination algorithm and architecture design for fast block matching motion estimation,” IEEE Trans. Circuits Syst. Video Technol. 14, 898–907 (2004). https://doi.org/10.1109/TCSVT.2004.828321

    Article  Google Scholar 

  53. J. Zan, M. O. Ahmad, M. N. S. Swamy, “A multiresolution motion estimation technique with indexing,” IEEE Trans. Circuits Syst. Video Technol. 16, 157–165 (2006). https://doi.org/10.1109/TCSVT.2005.857304

    Article  Google Scholar 

  54. S. Zhu and K.-K. Ma, “A new diamond search algorithm for fast block-matching motion estimation,” IEEE Trans. Image Process. 9, 287–290 (2000). https://doi.org/10.1109/83.821744

    Article  Google Scholar 

  55. C. Zhu, X. Lin, and L.-P. Chau, “Hexagon-based search pattern for fast block motion estimation” IEEE Trans. Circuits Syst. Video Technol. 12, 349–355 (2002). https://doi.org/10.1109/TCSVT.2002.1003474

    Article  Google Scholar 

  56. C. Zhu, X. Lin, L. Chau, and L.-M. Po, “Enhanced hexagonal search for fast block motion estimation,” IEEE Trans. Circuits Syst. Video Technol. 14, 1210–1214 (2004). https://doi.org/10.1109/TCSVT.2004.833166

    Article  Google Scholar 

  57. C. Zhu, W.-S. Qi, and W. Ser, “Predictive fine granularity successive elimination for fast optimal block matching motion estimation,” IEEE Trans. Image Process. 14, 213–221 (2005). https://doi.org/10.1109/TIP.2004.840702

    Article  Google Scholar 

  58. B.-J. Zou, C. Shi, C.-H. Xu, and S. Chen, “Enhanced hexagonal-based search using direction-oriented inner search for motion estimation,” IEEE Trans. Circuits Syst. Video Technol. 20, 156–160 (2010). https://doi.org/10.1109/TCSVT.2009.2031461

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of CSE, MLR Institute of Technology, 500043, Hyderabad, India

    K. Srinivas Rao

  2. Department of ECE, Lendi Institute of Engineering and Technology, 535005, Vizianagaram, India

    A. V. Paramkusam

Authors
  1. K. Srinivas Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. V. Paramkusam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to K. Srinivas Rao or A. V. Paramkusam.

Ethics declarations

COMPLIANCE WITH ETHICAL STANDARDS

This article is a completely original work of its authors; it has not been published before and will not be sent to other publications until the PRIA Editorial Board decides not to accept it for publication.

Conflict of Interest

The authors declare that they have no conflicts of interest.

Additional information

Dr. K. Srinivas Rao is the Professor of Computer Science and Engineering Department at MLR Institute of Technology, Hyderabad, India. He obtained his PhD in Computer Science and Engineering from Anna University, Tamilnadu, India. He received his BTech and MTech degrees from Osmania University, Hyderabad. He is having more than 20 yr of teaching and research experience. His current researches are in the fields of data mining, image processing, and big data analytics.

Dr. A. V. Paramkusam is a Professor of Electronics and Communication Engineering at Lendi Institute of Engineering and he obtained his PhD in Electronics and Communication Engineering from the JNTU Hyderabad, India in 2015. He received BE and ME degrees in Electronics and communication from Andhra University, Visakhapatnam, India, in 1996 and 2004, respectively. His research interests include image compression, video coding, and video water marking. He is a life member of ISTE. He has outstanding contribution with 36 publications in the national, IEEE International Conferences, and reputed international journals (Springer and IET). He is reviewer for SCI and Scopus indexed journals.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Srinivas Rao, K., Paramkusam, A.V. Block Matching Algorithms for the Estimation of Motion in Image Sequences: Analysis. Pattern Recognit. Image Anal. 32, 33–44 (2022). https://doi.org/10.1134/S1054661822010072

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1054661822010072

Keywords:

Search

Navigation