Translator Disclaimer
2 September 1993 Image compression and SANN equations
Author Affiliations +
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.
© (1993) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ying Liu "Image compression and SANN equations", Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993);


Fractal equations and their solutions
Proceedings of SPIE (June 10 1993)
Boltzmann machines for image-block coding
Proceedings of SPIE (March 28 1995)
Extensions of fractal theory
Proceedings of SPIE (August 19 1993)
Image compression using Boltzmann machines
Proceedings of SPIE (October 29 1993)
Unification of several image compression methods
Proceedings of SPIE (June 01 1994)
Pattern recognition using stochastic cellular automata
Proceedings of SPIE (September 01 1993)

Back to Top