Design of an adaptive genetic learning neural network system for image compression

Jianmin Jiang; Darren Butler

doi:10.1117/12.269778

1 April 1997 Design of an adaptive genetic learning neural network system for image compression

Jianmin Jiang, Darren Butler

Proceedings Volume 3030, Applications of Artificial Neural Networks in Image Processing II; (1997) https://doi.org/10.1117/12.269778
Event: Electronic Imaging '97, 1997, San Jose, CA, United States

Abstract

In this paper, we describe a genetic learning neural network system to vector quantize images directly to achieve data compression. The genetic learning algorithm is designed to have two levels: One is at the level of code words in which each neural network is updated through reproduction every time an input vector is processed. The other is at the level of code-books in which five neural networks are included in the gene pool. Extensive experiments on a group of image samples show that the genetic algorithm outperforms other vector quantization algorithms which include competitive learning, frequency sensitive learning and LBG.

Citation Download Citation

Jianmin Jiang and Darren Butler "Design of an adaptive genetic learning neural network system for image compression", Proc. SPIE 3030, Applications of Artificial Neural Networks in Image Processing II, (1 April 1997); https://doi.org/10.1117/12.269778

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