Learning-Driven Lossy Image Compression; A Comprehensive Survey

• URL: http://arxiv.org/abs/2201.09240v1
• Date: Sun, 23 Jan 2022 12:11:31 GMT
• Title: Learning-Driven Lossy Image Compression; A Comprehensive Survey
• Authors: Sonain Jamil, Md. Jalil Piran, and MuhibUrRahman
• Abstract summary: This paper aims to survey recent techniques utilizing mostly lossy image compression using machine learning (ML) architectures. We divide all of the algorithms into several groups based on architecture. Various discoveries for the researchers are emphasized and possible future directions for researchers.
• Score: 3.1761172592339375
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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