A neural network based VLSI vector quantizer for real-time image compression
W.-C. Fang, B.J. Sheu, O.T.-C. Chen · 2002
A trainable VLSI neuroprocessor for adaptive vector quantization based upon the frequency-sensitive competitive learning algorithm has been developed for high-speed high-ratio image compression applications. Simulation results show that such an algorithm is capable of producing good-quality reconstructed image at compression ratios of more than 20. This design includes a fully parallel vector quantizer and a pipelined codebook generator which obtains a time complexity O(1) for each quantization vector. A 5*5-dimensional vector quantizer prototype chip has been designed, fabricated and tested. It contains 64 inner-product neural units and an extendable winner-take-all block. This mixed-signal chip occupies a compact Si area of 4.6*6.8 mm/sup 2/ in 2.0- mu m scalable CMOS technology.>