Image Compression Using Neural Networks: A Review


  • Haval Tariq Sadeeq Duhok Polytechnic University
  • Thamer Hassan Hameed University of Duhok
  • Abdo Sulaiman Abdi Duhok Polytechnic University
  • Ayman Nashwan Abdulfatah Duhok Polytechnic University



Image Compression, Neural Networks, Artificial Neural Network, Data Compression


Computer images consist of huge data and thus require more memory space. The compressed image requires less memory space and less transmission time. Imaging and video coding technology in recent years has evolved steadily. However, the image data growth rate is far above the compression ratio growth, Considering image and video acquisition system popularization. It is generally accepted, in particular that further improvement of coding efficiency within the conventional hybrid coding system is increasingly challenged. A new and exciting image compression solution is also offered by the deep convolution neural network (CNN), which in recent years has resumed the neural network and achieved significant success both in artificial intelligent fields and in signal processing. In this paper we include a systematic, detailed and current analysis of image compression techniques based on the neural network. Images are applied to the evolution and growth of compression methods based on the neural networks. In particular, the end-to-end frames based on neural networks are reviewed, revealing fascinating explorations of frameworks/standards for next-generation image coding. The most important studies are highlighted and future trends even envisaged in relation to image coding topics using neural networks.

Author Biographies

Haval Tariq Sadeeq, Duhok Polytechnic University

Information Technology Department

Abdo Sulaiman Abdi, Duhok Polytechnic University

Information Technology Department




How to Cite

Sadeeq, H. T., Hameed, T. H., Abdi, A. S., & Abdulfatah, A. N. (2021). Image Compression Using Neural Networks: A Review. International Journal of Online and Biomedical Engineering (iJOE), 17(14), pp. 135–153.