An Efficient Technique for Implementing an Image-Compression Neural Algorithm on Concurrent Multiprocessor Architectures

Fabio Ancona, Stefano Rovetta, Rodolfo Zunino
University of Genoa Genova Italy



Engineering Applications of Artificial Intelligence
Volume 10, Issue 6, December 1997. Pages 573-580
Copyright (c) Elsevier Science Ltd.



Abstract

The paper describes a parallel implementation of a neural algorithm
performing vector quantization for very low bit-rate video
compression on toroidal-mesh multiprocessor systems. The neural
model considered is a plastic version of the Neural Gas algorithm,
whose features are suitable for implementations on toroidal mesh
topologies. The architecture adopted, and the data-allocation
strategy, enhance the method's scaling properties and remarkable
efficiency. The parallel approach is supported by a theoretical
analysis of the efficiency of the overall structure. Experimental
results on a significant testbed and the fit between predicted
and measured values confirm the validity of the parallel approach.


Author Keywords

Image compression; neural networks; parallel architectures;
experimental and theoretical results 


Index Terms

Image compression; Neural networks; Learning algorithms;
Vector quantization; Parallel processing systems;
Computer architecture; Neural algorithms;
Concurrent multiprocessor architectures