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Method and apparatus for encoding and decoding data utilizing data compression and neural networks

机译:利用数据压缩和神经网络编码和解码数据的方法和装置

摘要

A method structure for the compression of data utilizes an encoder which effects a transform with the aid of a coding neural network, and a decoder which includes a matched decoding neural network with effects almost the inverse transform of the encoder. The method puts in competition M coding neural networks (30.sub.1 to 30.sub.M) wherein M 1 positioned at the transmission end which effects a same type of transform and the encoded data of one of which are transmitted, after selection (32, 33) at a given instant, towards a matched decoding neural network which forms part of a set of several matched neural networks (60. sub.1 to 60.sub.Q) provided at the receiver end. Learning is effected on the basis of predetermined samples. The encoder may comprise, in addition to the coding neural network (30.sub.1 to 30.sub.M), a matched decoding neural network (35.sub.1 to 35.sub.M) so as to effect the selection (32, 33) of the best coding neural network in accordance with an error criterion.
机译:用于数据压缩的方法结构利用编码器和编码器,该编码器借助编码神经网络来实现变换,该解码器包括匹配的解码神经网络,其几乎实现编码器的逆变换。该方法将竞争性M编码神经网络(30-1至30M)放入其中,其中M> 1位于传输端,其实现相同类型的变换,并且其中之一的编码数据在传输之后在给定时刻向匹配解码神经网络进行选择(32、33),该匹配解码神经网络形成了在接收器端提供的一组几个匹配神经网络(60.sub.1至60.sub.Q)的一部分。学习是基于预定样本进行的。除了编码神经网络(30到30 M)之外,编码器还可以包括匹配的解码神经网络(1到35 M)以实现选择误差准则的最佳编码神经网络(32、33)。

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