1. A method for training an image compression system, wherein the image compression system comprises: a first image input terminal for receiving a first image and a plurality of second image input terminals for receiving a plurality of second images; a first convolutional neural network module configured to respectively predict each of the second images to obtain a plurality of predicted images corresponding to the plurality of second images in a one-to-one relationship; an image discrepancy acquisition module configured to determine the discrepant features between each of the second images and the corresponding predicted image, and take the discrepant features as a discrepant feature set; a second convolutional neural network module configured to determine updated features corresponding to the first images in a one-to-one relationship; an image superposition module configured to superpose the updated feature with the corresponding first image to obtain a superposed image; and an output terminal configured to output the superposed image obtained by the image superposition module and the discrepant feature set determined by the image discrepancy acquisition module as a compressed image; wherein the first convolutional neural network module is configured to predict each of the second images respectively according to the first image so as to obtain the plurality of predicted images corresponding to the plurality of second images in a one-to-one relationship, and the second convolutional neural network module updates the discrepant feature set to obtain the updated features; or the second convolutional neural network module updates the plurality of second images to obtain the updated features, and the first convolutional neural network module is configured to predict the plurality of second images respectively according to the superposed image so as to obtain the plurality of predicted images corresponding to the plurality of second images in a one-to-one relationship, wherein the method comprises the steps of: inputting a pre-set image as an image to be compressed into the image compression system, and respectively adjusting the weight of each filtering unit in each convolutional layer of a first convolutional neural network module and a second convolutional neural network module until the value of the following cost function L is the minimum: L = ∑ m = 1 M ⁢  Dm  p +  A - LR  q wherein Dm is the m-th discrepant feature in a discrepant feature set, M is the number of the discrepant features in the discrepant feature set; A is a superposed image finally outputted from the image compression system; LR is a low-resolution image corresponding to the pre-set image; and p and q are natural numbers; wherein  X  1 = ∑ n = 0 N ⁢ x ⁡ ( n ) ;  X  2 = ∑ n = 0 N ⁢ x 2 ⁡ ( n ) ; x(n) is the n-th element in X, and N is the total number of the elements in X.