1 April 1997 Design of an adaptive genetic learning neural network system for image compression
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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.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jianmin Jiang, Jianmin Jiang, Darren Butler, 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); doi: 10.1117/12.269778; https://doi.org/10.1117/12.269778
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