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.
References (38)
- 1 Image Super-Resolution Using Deep Convolutional Networks [DOI]
- 2 Accurate Image Super-Resolution Using Very Deep Convolutional Networks [DOI]
- 3 Recent advances in convolutional neural networks [DOI]
- 4 Discrete Cosine Transform [DOI]
- 5 Full Resolution Image Compression with Recurrent Neural Networks [DOI]
- 6 Hadamard transform image coding [DOI]
- 7 Artificial neural networks in hardware: A survey of two decades of progress [DOI]
- 8 Learning Convolutional Networks for Content-Weighted Image Compression [DOI]
- 9 Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches [DOI]
- 10 An End-to-End Compression Framework Based on Convolutional Neural Networks [DOI]
- 11 Large-Scale JPEG Image Steganalysis Using Hybrid Deep-Learning Framework [DOI]
- 12 D3: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images [DOI]
- 13 Learning End-to-End Lossy Image Compression: A Benchmark [DOI]
- 14 Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images [DOI]
- 15 Machine learning and Region Growing for Breast Cancer Segmentation [DOI]
- 16 JPEG Artifacts Reduction via Deep Convolutional Sparse Coding [DOI]
- 17 Image compression algorithms in wireless multimedia sensor networks: A survey [DOI]
- 18 Multi-Level Fusion in Ultrasound for Cancer Detection based on Uniform LBP Features [DOI]
- 19 Deep learning network for blind image quality assessment [DOI]
- 20 Spatially adaptive image compression using a tiled deep network [DOI]
- 21 Facial Beauty Prediction and Analysis based on Deep Convolutional Neural Network: A Review [DOI]
- 22 Lossless image compression in the remote sensing applications [DOI]
- 23 Deep Learning for Ligand-Based Virtual Screening in Drug Discovery [DOI]
- 24 Image Compression with Encoder-Decoder Matched Semantic Segmentation [DOI]
- 25 An efficient block based lossless compression of medical images [DOI]
- 26 A Hardware Architecture for Better Portable Graphics (BPG) Compression Encoder [DOI]
- 27 A Deep Learning Based Distributed Smart Surveillance Architecture using Edge and Cloud Computing [DOI]
- 28 Efficient Deep Neural Network for Digital Image Compression Employing Rectified Linear Neurons [DOI]
- 29 Applications of Artificial Neural Networks: A Review [DOI]
- 30 Image compression analysis using multistage vector quantization based on discrete wavelet transform [DOI]
- 31 Bat Optimization Based Vector Quantization Algorithm for Medical Image Compression [DOI]
- 32 Research on fractal image compression hybrid algorithm based on convolutional neural network and gene expression programming [DOI]
- 33 Depthwise Separable Convolutional Neural Network for Image Forensics [DOI]
- 34 A new end-to-end image compression system based on convolutional neural networks [DOI]
- 35 Grading image retrieval based on CNN deep features [DOI]
- 36 Lossy Image Compression with Filter Bank Based Convolutional Networks [DOI]
- 37 A Comparative study on Image and Video Compression Techniques [DOI]
- 38 Variable Rate Image Compression with Recurrent Neural Networks [DOI]
Cited by 57
Mariam Ibrahim, Ruba Elhafiz, Haneen Okasha · European Conference on Artificial Intelligence · 2024
M. M. Abdulqadir · Indonesian Journal of Computer Science · 2024
L. Mochurad · Technologies · 2024
G. Renuka, S. Vidhusha · 2024 5th International Conference on Innovative Trends in Information Technology (ICITIIT) · 2024
Muhammad Rafi Muttaqin, Y. Herdiyeni, Agus Buono · International Journal of Advances in Intelligent Informatics · 2023
D. S. Candra, K. Ulfa, F. H. Rasyidy · 2023 IEEE International Conference on Aerospace Electronics and Remote Sensing Technology (ICARES) · 2023
Anish Nagarsenker, P. Khandekar, Minal Deshmukh · International journal of electrical and computer engineering systems · 2023
P. Nithya, T. Vengattaraman, M. Sathya · REST Journal on Data Analytics and Artificial Intelligence · 2023
Savaram Shaliniswetha · ICTACT Journal on Image and Video Processing · 2022
M. Gashnikov · 2022 Ural-Siberian Conference on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT) · 2022
Showing 35 of 57 known citations — external sources report more than can currently be individually listed.
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.
71
Page views
0
PDF downloads
5
Outbound clicks
57
Citations
Views over time
Views by country
Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".
Traffic sources
Referring site, by host.
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.