Design of mobile communication image transmission compression algorithm based on convolutional neural network

Yunle Zhu

doi:10.1117/12.3026988

15 March 2024 Design of mobile communication image transmission compression algorithm based on convolutional neural network

Yunle Zhu

Author Affiliations +

Proceedings Volume 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023); 130751P (2024) https://doi.org/10.1117/12.3026988
Event: Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 2023, Kunming, China

Abstract

With the popularization of mobile communication applications, image transmission plays an increasingly important role in communication. However, due to the limitation of communication bandwidth and storage resources, efficient image compression has become an urgent need. The purpose of this study is to design an innovative mobile communication image transmission compression algorithm by introducing the deep learning technology of Convolutional Neural Network (CNN). In this algorithm, through the feature learning of convolution level and the hierarchical structure of CNN, the abstract features in the image are gradually extracted. This feature extraction process can not only capture the texture and structure information of the image effectively, but also has the advantage of resisting the common information loss problem in traditional compression methods. In order to enhance the learning ability of the network, we introduce residual connection, which improves the network's long-range dependence and attention to important areas. Experimental results show that compared with traditional compression methods, the algorithm based on CNN can maintain higher image quality at the same compression rate. The algorithm is more universal and can meet the needs of mobile communication image transmission in different types and scenarios. In addition, the impact on transmission quality is relatively small, and it can maintain good visual effects even at high compression rate.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yunle Zhu "Design of mobile communication image transmission compression algorithm based on convolutional neural network", Proc. SPIE 13075, Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023), 130751P (15 March 2024); https://doi.org/10.1117/12.3026988

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
7 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image compression

Image transmission

Mobile communications

Image quality

Image processing

Design

Convolution

Show All Keywords

Keywords/Phrases

Search In:

Publication Years