On fast and accurate block-based motion estimation algorithms using particle swarm optimization

  • Authors:

  • Jing Cai;W. David Pan

  • Affiliations:

  • Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USA;Department of Electrical and Computer Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USA

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2012

Quantified Score

Hi-index 0.07

Visualization

Abstract

Both fast and accurate block-matching algorithms are critical to efficient compression of video frames using motion estimation and compensation. While the particle swarm optimization approach holds the promise of alleviating the local optima problem suffered typically by existing very fast block matching methods, motion estimation algorithms based on particle swarm optimization in the literature appear to be either much slower than some leading fast block-matching methods for a given accuracy of motion estimation, or less accurate for a given computational complexity. In this paper, we show that the conventional particle swarm optimization approach, which was originally designed to solve general optimization problems where fast convergence of the algorithm might not be a primary concern, could be modified appropriately so that it could provide accurate motion estimation with very low computational cost in the specific context of video motion estimation. To this end, we proposed a new block matching algorithm based on a set of strategies adapted from the standard particle swarm optimization approach. Extensive simulations showed that the proposed method could achieve significant improvements over leading fast block matching methods including the diamond search and the cross-diamond search methods, in terms of both estimation accuracy and computational cost. In particular, the proposed method based on particle swarm optimization is not only much faster, but also remarkably more accurate (about 2dB higher in terms of the Peak Signal-to-Noise-Ratio) than the competing methods on video sequences with large motion.