Advertisement

Adaptive Weighted Median Filter for Motion Estimation

  • Prashant BhalgeEmail author
  • Salim Amdani
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 28)

Abstract

Smoothing techniques can be constructive to the motion vectors that can detect defective vectors and put forward alternatives. The substitute motion vectors can be used in place of those recommended by the block match algorithm. If frames are going to be interpreted by the receiver then motion vector amendment is expected to be precious. Design of an adaptive weighted median filter whose weights alters according to the confined characteristics is possible which can be used for smoothing intention.

Keywords

Block distortion measure Motion compensation Motion estimation Video compression 

References

  1. 1.
    Alparone, L., Barni, M., Bartolini, F., Caldelli, R.: Regularization of optic flow estimates by means of weighted vector median filtering. IEEE Trans. Image Process. 8, 1462–1467 (1999)CrossRefGoogle Scholar
  2. 2.
    Alparone, L., Barni, M., Bartolini, F., Santurri, L.: An improved H.263 decoder relying on weighted median filtering of motion vectors. IEEE Trans. Circuits Syst. Video Technol. 11, 235–240 (2001)Google Scholar
  3. 3.
    Liu, Z., Song, Y., Ikenaga, T., Gota, S.: Low pass filter based variable block size motion estimation algorithm for H.264. In: IEEE International Conference ICASSP, pp. 253–256 (2006)Google Scholar
  4. 4.
    Huang, A., Nguyen, T.: A multistage motion vector processing method for motion compensated frame interpolation. IEEE Trans. Image Process. 17, 694–708 (2008)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Guo, J., Kim, J.: Adaptive motion vector smoothing for improving side information in distributed video coding. J. Inf. Process. Syst. 7,103–110 (2011)Google Scholar
  6. 6.
    Stengel, M., Bauszat, P., Eisemann, M., Eisemann, E., Magnor, M.: Temporal video filtering and exposure control for perceptual motion blur. IEEE Trans. Visual. Comput. Graphics 21, 663–671 (2015)CrossRefGoogle Scholar
  7. 7.
    Jain, A.: Fundamentals of Digital Image Processing, 1st edn. PHI Learning (2010)Google Scholar
  8. 8.
    Haylin, S., Kailath, T.: Adaptive filter theory. LPE, Pearson (2002)Google Scholar
  9. 9.
    Gamcova, M., Marchevsky, S., Gamec, J.: Higher efficiency of motion estimation methods. J. Radioengineering 13, 58–64 (2004)Google Scholar
  10. 10.
    Bhalge, P., Amdani, S.: Modified Hexagonal search for motion estimation. In: Proceeding of IEEE international conference ICICCS-2017, Madurai (2017)Google Scholar
  11. 11.
    Wai, L.: Efficient Block Matching Motion Estimation Algorithms for Video Coding. Thesis, City University of Hong Kong (2004)Google Scholar
  12. 12.
    Halsall, F.: Multimedia Communication. Pearson Education (2001)Google Scholar

Copyright information

© Springer International Publishing AG  2018

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentBabasaheb Naik College of Engineering, PusadPusadIndia

Personalised recommendations