New adaptive partial distortion search using clustered pixel matching error characteristic

Ko Cheung Hui, Wan Chi Siu, Yui Lam Chan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

42 Citations (Scopus)


In order to reduce the computation load, many conventional fast block-matching algorithms have been developed to reduce the set of possible searching points in the search window. All of these algorithms produce some quality degradation of a predicted image. Alternatively, another kind of fast block-matching algorithms which do not introduce any prediction error as compared with the full-search algorithm is to reduce the number of necessary matching evaluations for every searching point in the search window. The partial distortion search (PDS) is a well-known technique of the second kind of algorithms. In the literature, many researches tried to improve both lossy and loss-less block-matching algorithms by making use of an assumption that pixels with larger gradient magnitudes have larger matching errors on average. Based on a simple analysis, it is found that, on average, pixel matching errors with similar magnitudes tend to appear in clusters for natural video sequences. By using this clustering characteristic, we propose an adaptive PDS algorithm which significantly improves the computation efficiency of the original PDS. This approach is much better than other algorithms which make use of the pixel gradients. Furthermore, the proposed algorithm is most suitable for motion estimation of both opaque and boundary macroblocks of an arbitrary-shaped object in MPEG-4 coding.
Original languageEnglish
Pages (from-to)597-607
Number of pages11
JournalIEEE Transactions on Image Processing
Issue number5
Publication statusPublished - 1 May 2005


  • Motion estimation
  • Partial distortion search(PDS)
  • Video coding

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Graphics and Computer-Aided Design
  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition


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