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.!13
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机译：摘要：图像压缩可以通过使用随机人工神经网络（SANN）来实现。这个想法是将图像存储在随机神经网络的稳定分布中。给定输入图像f $εF，可以找到一个SANN t $εT，使得该SANN的平衡分布为给定图像f。因此，输入图像f被编码为SANN t的规范。从F（图像空间）到T（SANN的参数空间）的映射定义了SANN转换。为了完成SANN转换，必须解决SANN方程。在本文中，我们将首先介绍两种类型的SANN方程。然后，我们将开发一种算法来求解SANN方程。！13
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