A real-time VLSI neuroprocessor for adaptive image compression based upon frequency-sensitive competitive learning

Author

Fang, Wai-Chi ; Sheu, Bing J. ; Chen, Oscal T C

Volume

i

fYear

1991

fDate

8-14 Jul 1991

Firstpage

429

Abstract

The frequency-sensitive competitive learning (FSCL) algorithm and its associated VLSI neuroprocessor have been developed for adaptive vector quantisation (AVQ). Simulation results show that the FSCL algorithm is capable of producing a good-quality codebook for AVQ at high compression ratios of more than 20 in real time. This VLSI neural-network-based vector quantization design includes a fully parallel vector quantizer and a pipelined codebook generator to provide an effective data compression scheme. It provides a computing capability as high as 3.33 billion connections per second. Its performance can achieve a speedup of 750 compared with SUN-3/60 and a compression ratio of 33 at a signal-to-noise ratio of 23.81 dB

Keywords

VLSI; computerised picture processing; data compression; learning systems; neural nets; real-time systems; adaptive image compression; adaptive vector quantisation; compression ratios; data compression; frequency-sensitive competitive learning; neural network; parallel vector quantizer; pipelined codebook generator; real-time VLSI neuroprocessor; signal-to-noise ratio; CMOS technology; Data compression; Frequency; Image coding; Image reconstruction; Neural networks; Power capacitors; Speech coding; Vector quantization; Very large scale integration;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on

Conference_Location

Seattle, WA

Print_ISBN

0-7803-0164-1

Type

conf

DOI

10.1109/IJCNN.1991.155216

Filename

155216