The USC Andrew and Erna Viterbi School of Engineering USC Signal and Image Processing Institute USC Ming Hsieh Department of Electrical Engineering University of Southern California

Technical Report USC-SIPI-263

“Fast Tree-Structured Nearest Neighbor Encoding for Vector Quantization”

by Ioannis Katsavounidis, C.-C. Jay Kuo and Zhen Zhang

June 1994

The nearest neighbor encoding problem with an unstructured codebook of an arbitrary size and vector dimension is examined in this research. 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 the nearest neighbor encoding by using this tree. Numerical experiments are given to demonstrate the performance of the proposed method.

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