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Proceedings Paper

Image compression and SANN equations
Author(s): Ying Liu
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Paper Abstract

Image compression can be achieved by using stochastic artificial neural networks (SANN). The idea is to store an image in 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 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 SANN transformation. To complete a SANN transformation, an SANN equation has to be solved. In this paper, we will first introduce two types of SANN equations. Then, we will develop an algorithm to solve SANN equation.

Paper Details

Date Published: 2 September 1993
PDF: 12 pages
Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); doi: 10.1117/12.152561
Show Author Affiliations
Ying Liu, Savannah State College (United States)

Published in SPIE Proceedings Vol. 1965:
Applications of Artificial Neural Networks IV
Steven K. Rogers, Editor(s)

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