A lossless image compression using deep learning.

Variational Autoencoders (VAEs) is a type of generative model that can be used for image compression. The steps involved in using VAEs for lossless image compression are pre-processing, encoding, sampling, decoding, loss function, training and compression. The input image is first pre-processed to convert it into a format that can be used as input to the VAE. Typically, this process entails normalizing the pixel values to fall within the range of 0 to 1, followed by transforming the image into a flattened vector. The encoder network receives the pre-processed image and condenses it into a reduced-dimensional representation. The encoder network consists of multiple layers of neural networks that reduce the dimensionality of the input image while capturing the important features of the image. The output of the encoder network is a mean vector and a variance vector, which are used to sample a compressed representation of the image. The compressed representation of the image is sampled from a probability distribution that is defined by the mean vector and variance vector output by the encoder network. This sampling process makes VAEs a generative model. The decoder network takes in the compressed representation of the image as input and reconstructs the original image. The loss function used in VAEs is a combination of a reconstruction loss and a regularization loss. The VAE undergoes training through back-propagation to minimize its loss function. Once the VAE is trained, it can be used to compress new images. The encoder network condenses the input image into a reduced-dimensional representation, which can then be stored or transmitted. In order to achieve secure image transmission we can perform an image encryption using Elliptic Curve Cryptography (ECC). Variational Autoencoders for lossless image compression is a promising research area that will provide good results in achieving high-quality compression while preserving the entirety of the data within the original image. [ABSTRACT FROM AUTHOR]