IMPLEMENTATION OF A VECTOR QUANTIZATION CODEBOOK DESIGN TECHNIQUE BASED ON A COMPETITIVE LEARNING ARTIFICIAL NEURAL NETWORK

被引：0
|
作者：
AHALT, SC
CHEN, PK
CHOU, CT
JUNG, TP
机构：
[1] Department of Electrical Engineering, The Ohio State University, Columbus, 43210, Ohio
来源：
JOURNAL OF SUPERCOMPUTING | 1992年 / 5卷 / 04期
关键词：
NEURAL NETWORKS; VECTOR QUANTIZATION; PARALLEL COMPUTING; IMAGE COMPRESSION;
D O I：
10.1007/BF00127951
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
We describe an implementation of a vector quantization codebook design algorithm based on the frequency-sensitive competitive learning artificial neural network. The implementation, designed for use on high-performance computers, employs both multitasking and vectorization techniques. A C version of the algorithm tested on a CRAY Y-MP8/864 is discussed. We show how the implementation can be used to perform vector quantization, and demonstrate its use in compressing digital video image data. Two images are used, with various size codebooks, to test the performance of the implementation. The results show that the supercomputer techniques employed have significantly decreased the total execution time without affecting vector quantization performance.
引用
收藏
页码：307 / 330
页数：24
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