@inproceedings{ea5e0470860142d48a1a0c09cf4881f8,
title = "A real-time VLSI neuroprocessor for adaptive image compression based upon frequency-sensitive competitive learning",
abstract = "The frequency-sensitive competitive learning (FSCL) algorithm and its associated VLSI neuroprocessor have been developed for adaptive vector quantization (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 (NNVQ) 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.",
author = "Fang, {Wai Chi} and Sheu, {Bing J.} and Chen, {Oscal T.C.}",
year = "1991",
language = "英语",
isbn = "0780301641",
series = "Proceedings. IJCNN-91-Seattle: International Joint Conference on Neural Networks",
publisher = "Publ by IEEE",
pages = "429--435",
editor = "Anon",
booktitle = "Proceedings. IJCNN-91-Seattle",
note = "International Joint Conference on Neural Networks - IJCNN-91-Seattle ; Conference date: 08-07-1991 Through 12-07-1991",
}