Multiple block-size search algorithm for fast block motion estimation

  • Authors:

  • Ka-Ho Ng;Lai-Man Po;Ka-Man Wong;Chi-Wang Ting;Kwok-Wai Cheung

  • Affiliations:

  • Department of Electronic Engineering, City University of Hong Kong, Hong Kong, SAR, China;Department of Electronic Engineering, City University of Hong Kong, Hong Kong, SAR, China;Department of Electronic Engineering, City University of Hong Kong, Hong Kong, SAR, China;Department of Electronic Engineering, City University of Hong Kong, Hong Kong, SAR, China;Department of Computer Science, Chu Hai College of Higher Education, Hong Kong, SAR, China

  • Venue:
  • ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
  • Year:
  • 2009

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Abstract

Although variable block-size motion estimation provides significant video quality and coding efficiency improvement, it requires much higher computational complexity compared with fixed block size motion estimation. The reason is that the current motion estimation algorithms are mainly designed for fixed block size. Current variable block-size motion estimation implementation simply applies these existing motion estimation algorithms independently for different block sizes to find the best block size and the corresponding motion vector. Substantial computation is wasted because distortion data reuse among motion searches of different block sizes is not considered. In this paper, a motion estimation algorithm intrinsically designed for variable block-size video coding is presented. The proposed multiple block-size search (MBSS) algorithm unifies the motion searches for different block sizes into a single searching process instead of independently performing the search for each block size. In this unified search, the suboptimal motion vectors for different block sizes are used to determine the next search steps. Its prediction quality is comparable with that obtained by performing motion search for different block sizes independently while the computational load is substantially reduced. Experimental results show that the prediction quality of MBSS is similar to full search.