An efficient method of constructing L1-Type norm feature to estimate euclidean distance for fast vector quantization

Zhibin Pan, Tadahiro Ohmi, Koji Kotani

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In order to speed up the seazch process of vector quantization (VQ), it is most important to avoid actually computing k-dimensional Euclidean distance as many as possible. In order to fmd abest-matched codeword (winner) in the codebook for a certain input vector, it is a general way to roughly estimate other than exactly compute Euclidean distance immediately for the purpose of rejecting a candidate codeword. The lower dimensional features of a vector such as sum or the mean (L1-type norm) and L2 norm are widely used for this purpose. Obviously, how to construct a suitable feature is a core problem for estimating Euclidean distance. In this paper, an efficient method of constructing L1-type norm feature is proposed by introducing a reference vector. In addition, the criterion on how to select an optimal reference vector is also given. Experimental results confirmed the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)269-274
Number of pages6
JournalIntelligent Automation and Soft Computing
Issue number3
Publication statusPublished - 2006 Jan


  • Estimation
  • Euclidean distance
  • Fast encoding
  • L-type norm feature
  • Vector quantization

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence

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