Implementation of a vector quantization codebook design technique based on a competitive learning artificial neural network |
| |
Authors: | Stanley C Ahalt Prakoon Chen Cheng-Taou Chou Tzyy-Ping Jung |
| |
Affiliation: | (1) Department of Electrical Engineering, The Ohio State University, 2015 Neil Avenue, 43210 Columbus, Ohio, USA |
| |
Abstract: | We describe an implementation of a vector quantization codebook design algorithm based on the frequencysensitive 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.This work was supported by a Cray University Research Award and by NASA Lewis research grant number NAG3-1164. |
| |
Keywords: | Neural networks vector quantization parallel computing image compression |
本文献已被 SpringerLink 等数据库收录! |
|