  In the realm of image processing and computer vision (CV), machine learning
(ML) architectures are widely applied. Convolutional neural networks (CNNs)
solve a wide range of image processing issues and can solve image compression
problem. Compression of images is necessary due to bandwidth and memory
constraints. Helpful, redundant, and irrelevant information are three different
forms of information found in images. This paper aims to survey recent
techniques utilizing mostly lossy image compression using ML architectures
including different auto-encoders (AEs) such as convolutional auto-encoders
(CAEs), variational auto-encoders (VAEs), and AEs with hyper-prior models,
recurrent neural networks (RNNs), CNNs, generative adversarial networks (GANs),
principal component analysis (PCA) and fuzzy means clustering. We divide all of
the algorithms into several groups based on architecture. We cover still image
compression in this survey. Various discoveries for the researchers are
emphasized and possible future directions for researchers. The open research
problems such as out of memory (OOM), striped region distortion (SRD),
aliasing, and compatibility of the frameworks with central processing unit
(CPU) and graphics processing unit (GPU) simultaneously are explained. The
majority of the publications in the compression domain surveyed are from the
previous five years and use a variety of approaches.
