Image Compression Using Deep Learning: Methods and Techniques

Authors

  • Arwa Sahib Abd-Alzhra Computer Science Department, Collage of Science, University of Baghdad, Baghdad, Iraq
  • Mohammed S. H. Al- Tamimi Computer Science Department, Collage of Science, University of Baghdad, Baghdad, Iraq https://orcid.org/0000-0002-6005-1964

DOI:

https://doi.org/10.24996/ijs.2022.63.3.34

Keywords:

Image Compression, Auto Encoders, vga, Lossless, Lossy, JPEG2000, JPEG, SSIM, ndpreserving function, PSNR

Abstract

     In recent years images have been used widely by online social networks providers or numerous organizations such as governments, police departments, colleges, universities, and private companies. It held in vast databases. Thus, efficient storage of such images is advantageous and its compression is an appealing application. Image compression generally represents the significant image information compactly with a smaller size of bytes while insignificant image information (redundancy) already been removed for this reason image compression has an important role in data transfer and storage especially due to the data explosion that is increasing significantly. It is a challenging task since there are highly complex unknown correlations between the pixels. As a result, it is hard to find and recover a well-compressed representation for images, and it also hard to design and test networks that are able to recover images successfully in a lossless or lossy way. Several neural networks and deep learning methods have been used to compress images. This article survey most common techniques and methods of image compression focusing on auto-encoder of deep learning.

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Published

2022-03-30

How to Cite

Abd-Alzhra, A. S. ., & Al- Tamimi, M. S. H. (2022). Image Compression Using Deep Learning: Methods and Techniques. Iraqi Journal of Science, 63(3), 1299–1312. https://doi.org/10.24996/ijs.2022.63.3.34

Issue

Section

Computer Science

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