  It is shown that neural networks (NNs) achieve excellent performances in
image compression and reconstruction. However, there are still many
shortcomings in the practical application, which eventually lead to the loss of
neural network image processing ability. Based on this, this paper proposes a
joint framework based on neural network and zoom compression. The framework
first encodes the incoming PNG or JPEG image information, and then the image is
converted into binary input decoder to reconstruct the intermediate state
image, next we import the intermediate state image into the zooming compressor
and re-pressurize it, and reconstruct the final image. From the experimental
results, this method can better process the digital image and suppress the
reverse expansion problem, and the compression effect can be improved by 4 to
10 times as much as that of using RNN alone, showing better ability in
application. In this paper, the method is transmitted over a digital image, the
effect is far better than the existing compression method alone, the Human
visual system can not feel the change of the effect.
