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Gersho, A., Gray, R.M.: Vector quantization i: structure and performance. The simulation results show that the proposed method can effectively compress and encrypt images, and then obtain better compression image than stacked auto-encoder (SAE), while the algorithm is faster and easier encrypting and decrypting images and the decrypted image distortion rate is low and suitable for practical applications. In this paper, we use the standard image of 256 * 256 to do simulation experiments and use histogram and image correlation to analyze the results of encryption. Finally, we extract the data of based on a variational auto-encoder and perform division, then the data input the VAE generative model to encrypt image and analyze encryption images. Then, the peak signal-to-noise ratio (PSNR) and mean square error (MSE) are used to measure the compression effect and Set the number of iterations of the model. Firstly, we use multi-layer perceptual neural network to train the VAE model, and set parameters of the model to get the best model. The algorithm aims to encrypt and compress images by using a variational auto-encoder generative model. To solve the problem that the network security real-time transmits image, a new image encryption and compression method based on a variational auto-encoder (VAE) generative model is proposed in this paper.
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