<title>Attention mechanisms for an image coding system</title>

JPEG 2000 is a new image coding system that uses state-of-the-art compression techniques based on wavelet technology. As interactive multimedia technologies evolve, the requirements for the file format used to store the image data continue to evolve. The size and bit depth collected for an image to increase the resolution and extend the dynamic range and color gamut. Discrete Wavelet transform based embedded image coding method is the basis of JPEG2000. Image compression algorithm for the proper use and display of the image is a requirement for digital photography.

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Image Compression Using Neural Networks: A Review

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v17i14.26059 ◽

2021 ◽

Vol 17 (14) ◽

pp. 135-153

Author(s):

Haval Tariq Sadeeq ◽

Thamer Hassan Hameed ◽

Abdo Sulaiman Abdi ◽

Ayman Nashwan Abdulfatah

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Compression ◽

Image Coding ◽

Image Data ◽

Coding System ◽

Memory Space ◽

Coding Efficiency ◽

Huge Data ◽

The Neural Network

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.

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