Fast encoding method for image vvector quantization based on multiple appropriate features to estimate euclidean distance

Zhibin Pan, Koji Kotani, Tadahiro Ohmi

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The encoding process of finding the best-matched codeword (winner) for a certain input vector in image vector quantization (VQ) is computationally very expensive due to a lot of k-dimensional Euclidean distance computations. In order to speed up the VQ encoding process, it is beneficial to firstly estimate how large the Euclidean distance is between the input vector and a candidate codeword by using appropriate low dimensional features of a vector instead of an immediate Euclidean distance computation. If the estimated Euclidean distance is large enough, it implies that the current candidate codeword could not be a winner so that it can be rejected safely and thus avoid actual Euclidean distance computation. Sum (1-D), L 2 norm (1-D) and partial sums (2-D) of a vector are used together as the appropriate features in this paper because they are the first three simplest features. Then, four estimations of Euclidean distance between the input vector and a codeword are connected to each other by the Cauchy-Schwarz inequality to realize codeword rejection. For typical standard images with very different details (Lena, F-16, Pepper and Baboon), the final remaining must-do actual Euclidean distance computations can be eliminated obviously and the total computational cost including all overhead can also be reduced obviously compared to the state-of-the-art EEENNS method meanwhile keeping a full search (FS) equivalent PSNR.

Original languageEnglish
Pages (from-to)161-169
Number of pages9
JournalOptical Review
Volume12
Issue number3
DOIs
Publication statusPublished - 2005 May 1

Keywords

  • Euclidean distance estimation
  • Fast encoding
  • Image vector quantization
  • Low dimensional features

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

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