Image compression using stochastic neural networks

Y Liu

doi:10.1109/icnn.1993.298788

Image compression using stochastic neural networks

Y Liu

https://doi.org/10.1109/icnn.1993.298788

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Publication Date: Mar 28, 1993

Citations: 1

Affiliation: Savannah State University

#Stochastic Artificial Neural Networks #Stochastic Neural Networks + Show 8 more

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Abstract

Image compression using stochastic artificial neural networks (SANNs) is studied. The ideal is to store an image in a stable distribution of a stochastic neural network. Given an input image f epsilon F, one can find a SANN t epsilon T such that the equilibrium distribution of this SANN is the given image f. Therefore, the input image, f, is encoded into a specification of a SANN, t. This mapping from F (image space) to T (parameter space of SANN) defines the SANN transformation. It is shown that the compression ratio R of the SANN transformation is R=O(n/(K (log n)/sup 2/)) where n is the number of pixels. To complete a SANN transformation, SANN equations must be solved. Two SANN equations are presented. The solution of SANN is briefly discussed. >

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