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
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