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Research Article Open access CC BY 4.0

Image Compression Based on Deep Learning: A Review

Hajar Maseeh Yasin, Adnan Mohsin Abdulazeez

Asian Journal of Research in Computer Science · pp. 62–76 · Published 1 May 2021

10.9734/ajrcos/2021/v8i130193

Abstract

Image compression is an essential technology for encoding and improving various forms of images in the digital era. The inventors have extended the principle of deep learning to the different states of neural networks as one of the most exciting machine learning methods to show that it is the most versatile way to analyze, classify, and compress images. Many neural networks are required for image compressions, such as deep neural networks, artificial neural networks, recurrent neural networks, and convolution neural networks. Therefore, this review paper discussed how to apply the rule of deep learning to various neural networks to obtain better compression in the image with high accuracy and minimize loss and superior visibility of the image. Therefore, deep learning and its application to different types of images in a justified manner with distinct analysis to obtain these things need deep learning.

Image compression artificial neural networks deep learning recurrent neural networks convolutional neural networks

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