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/v8i130193Abstract
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.
Cited by 35
Ziad Doughan, Rola Kassem, Ahmad M. El-Hajj · 2021 3rd IEEE Middle East and North Africa COMMunications Conference (MENACOMM) · 2021
Hu Shao, Bingtao Liu, Zongpeng Li · Electronics · 2023
Zhongyu Wang, Zhenling Su, Yexin Zhang · Communications in Computer and Information Science · 2025
Shaiba Akhter, Rahul Raj, Rupaban Subadar · Lecture Notes in Electrical Engineering · 2024
Rohan lal, Prashant Sharma, Devendra Kumar Patel · Communications in Computer and Information Science · 2023
Related research
- Automatic Segmentation of Organ at Risk in Head and Neck Cancer CT Images Using Medical Open Network for Artificial Intelligence (MONAI) with Deep Learning Techniques — shares topic coverage
- Diagnostic Accuracy of Artificial Intelligence for Breast Cancer Detection: A Systematic Review — shares topic coverage
- Leveraging Deep Learning Algorithms for Predicting Power Outages and Detecting Faults: A Review — shares topic coverage
- Bridging Prenatal Diagnostics and AI: A Systematic Review and Meta-Analysis of the Efficacy of Advanced Algorithms in Identifying Congenital Fetal Abnormalities — shares topic coverage
- Applications of Deep Learning in Predicting the Risk of Metabolic Syndrome from Lifestyle and Behavioral Factors: A Scoping Review — shares topic coverage
Article metrics
Real usage data collected on this platform.
0
Page views
0
PDF downloads
0
Outbound clicks
35
Citations
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
No views recorded yet.
Traffic sources
Referring site, by host.
No traffic recorded yet.
Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.