Deep Architectures for Image Compression: A Critical Review

Deep learning architectures are now pervasive and filled almost all applications under image processing, computer vision, and biometrics. The attractive property of feature extraction of CNN has solved a lot of conventional image processing problems with much-improved performance & efficiency. The paper aimed to review over a hundred recent state-of-the-art techniques exploiting mostly lossy image compression using deep learning architectures. These deep learning algorithms consists of various architectures like CNN, RNN, GAN, autoencoders and variational autoencoders. We have classified all the algorithms under certain categories for the better and deep understanding. The review is written keeping in mind the contributions of researchers & the challenges faced by them. Various findings for the researchers along with some future directions for a new researcher have been significantly highlighted. Most of the papers reviewed in the compression domain are from the last four years using different methodologies. The review has been summarized by dropping a new outlook for researchers in the realm of image compression. (c) 2021 Elsevier B.V. All rights reserved.