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
         
        
        
        
        
            fDate : 
2/1/1996 12:00:00 AM
         
        
        
        
            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;
         
        
        
            Journal_Title : 
Image Processing, IEEE Transactions on