Linear Hashtable Method Predicted Hexagonal Search Algorithm with Spatial Related Criterion

  • Yunsong Wu
  • Graham Megson
  • Zhengang Nie
  • F. N. Alavi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3540)

Abstract

The paper presents a novel Linear Hashtable Method Predicted Hexagonal Search (LHMPHS) method for block base motion compensation. It bases on the edge motion estimation algorithm called hexagonal search (HEXBS). Most current variances of hexagonal search are investigated. On the basis of research of previous algorithms, we proposed a Linear Hashtable Motion Estimation Algorithm (LHMEA). The proposed algorithm introduces hashtable into motion estimation. It uses information from the current frame. The criterion uses spatially correlated macroblock (MB)’s information. Except for coarse search, the spatially correlated information is also used in inner search. The performance of the algorithm is evaluated by using standard video sequences and the results are compared to current algorithms such as Full Search, Logarithmic Search etc. The evaluation considers the three important metrics: time, compression rate and PSNR.

Keywords

Motion Vector Motion Estimation Current Frame Search Point Full 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

  • Yunsong Wu
    • 1
  • Graham Megson
    • 1
  • Zhengang Nie
    • 2
  • F. N. Alavi
    • 3
  1. 1.Computer ScienceReading UniversityReadingUK
  2. 2.Beihang University 
  3. 3.Computer ScienceQueen Marry, University of London 

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