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Enhanced Downhill Simplex Search for Fast Video Motion Estimation

  • Hwai-Chung Fei
  • Chun-Jen Chen
  • Shang-Hong Lai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3767)

Abstract

Block-based motion estimation can be regarded as a function minimization problem in a finite two-dimensional space. Therefore, fast block-based motion estimation can be achieved by using an efficient function minimization algorithm instead of using a predefined search pattern, such as the diamond search. The downhill simplex search algorithm is an efficient derivative-free function minimization algorithm. In this paper, we propose several enhanced schemes to improve the efficiency of applying the downhill simplex search algorithm to motion estimation. The proposed enhanced schemes include a new initialization process, a special rounding method, and an early-stop error function evaluation procedure. Experimental results on several benchmarking videos show superior performance of the proposed algorithm over some existing fast block matching methods.

Keywords

Motion Vector Motion Estimation Full Search Diamond Search Simplex Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hwai-Chung Fei
    • 1
  • Chun-Jen Chen
    • 1
  • Shang-Hong Lai
    • 1
  1. 1.Department of Computer ScienceNational Tsing Hua UniversityHsinchuTaiwan, R.O.C

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