Title :
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
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;
Conference_Titel :
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location :
Seattle, WA
Print_ISBN :
0-7803-0164-1
DOI :
10.1109/IJCNN.1991.155216
