• DocumentCode
    1215364
  • Title

    Fast tree-structured nearest neighbor encoding for vector quantization

  • Author

    Katsavounidis, Ioannis ; Kuo, C. C Jay ; Zhang, Zhen

  • Author_Institution
    Dept. of Electr. Eng. Syst., Univ. of Southern California, Los Angeles, CA, USA
  • Volume
    5
  • Issue
    2
  • fYear
    1996
  • fDate
    2/1/1996 12:00:00 AM
  • Firstpage
    398
  • Lastpage
    404
  • Abstract
    This work examines the nearest neighbor encoding problem with an unstructured codebook of arbitrary size and vector dimension. We propose a new tree-structured nearest neighbor encoding method that significantly reduces the complexity of the full-search method without any performance degradation in terms of distortion. Our method consists of efficient algorithms for constructing a binary tree for the codebook and nearest neighbor encoding by using this tree. Numerical experiments are given to demonstrate the performance of the proposed method
  • Keywords
    computational complexity; tree searching; vector quantisation; algorithms; binary tree; codebook size; complexity reduction; fast tree-structured nearest neighbor encoding; full-search method; numerical experiments; performance; unstructured codebook; vector dimension; vector quantization; Annealing; Binary trees; Degradation; Encoding; Entropy; Image coding; Nearest neighbor searches; Neural networks; Testing; Vector quantization;
  • fLanguage
    English
  • Journal_Title
    Image Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1057-7149
  • Type

    jour

  • DOI
    10.1109/83.480778
  • Filename
    480778