Open Access

Center of Mass-Based Adaptive Fast Block Motion Estimation

  • Hung-Ming Chen1Email author,
  • Po-Hung Chen1,
  • Kuo-Liang Yeh2,
  • Wen-Hsien Fang2,
  • Mon-Chau Shie2 and
  • Feipei Lai1, 3
EURASIP Journal on Image and Video Processing20072007:065242

DOI: 10.1155/2007/65242

Received: 13 August 2006

Accepted: 29 January 2007

Published: 14 March 2007

Abstract

This work presents an efficient adaptive algorithm based on center of mass (CEM) for fast block motion estimation. Binary transform, subsampling, and horizontal/vertical projection techniques are also proposed. As the conventional CEM calculation is computationally intensive, binary transform and subsampling approaches are proposed to simplify CEM calculation; the binary transform center of mass (BITCEM) is then derived. The BITCEM motion types are classified by percentage of (0,0) BITCEM motion vectors. Adaptive search patterns are allocated according to the BITCEM moving direction and the BITCEM motion type. Moreover, the BITCEM motion vector is utilized as the initial search point for near-still or slow BITCEM motion types. To support the variable block sizes, the horizontal/vertical projections of a binary transformed macroblock are utilized to determine whether the block requires segmentation. Experimental results indicate that the proposed algorithm is better than the five conventional algorithms, that is, three-step search (TSS), new three-step search (N3SS), four three-step search (4SS), block-based gradient decent search (BBGDS), and diamond search (DS), in terms of speed or picture quality for eight benchmark sequences.

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Authors’ Affiliations

(1)
Department of Electrical Engineering, National Taiwan University
(2)
Department of Electronic Engineering, National Taiwan University of Science and Technology
(3)
Department of Computer Science and Information Engineering, National Taiwan University

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Copyright

© Hung-Ming Chen et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.