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A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

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A novel image semantic communication method via dynamic decision generation network and generative adversarial

Shugang Liu^1,2, Zhan Peng³, Qiangguo Yu⁴

¹School of Physics and Electronic Science, Hunan University of Science and Technology, Xiangtan, 411201, China.

Scientific Reports

|August 23, 2024

Summary

This study introduces a new image semantic communication model for efficient image compression and high-quality reconstruction. The deep learning approach achieves significant compression ratios and low distortion, outperforming existing methods.

Keywords:

Dynamic decision generation network (DDGN)Generative adversarial network (GAN)Image semantic communication Joint source-channel coding (JSCC)

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Area of Science:

Computer Science
Electrical Engineering
Artificial Intelligence

Background:

Image semantic communication faces challenges in compression and reconstruction quality.
Efficiently transmitting images while maintaining fidelity is crucial for modern communication systems.

Purpose of the Study:

To propose a novel image semantic communication model integrating dynamic decision generation and generative adversarial networks.
To enhance image compression and reduce reconstruction distortion using deep learning.

Main Methods:

Utilized semantic encoding and a dynamic decision generation network for feature extraction and selection based on signal-to-noise ratio (SNR).
Employed a generative adversarial network (GAN) with adversarial and perceptual losses for improved image reconstruction at the receiver.
Implemented a deep learning-based joint source-channel coding approach.

Main Results:

Achieved a compression ratio (CR) of 81.5% and a peak SNR of 26 dB in an AWGN channel.
In a Rayleigh fading channel, the scheme yielded an 80.5% CR and 23 dB peak SNR.
Demonstrated low learned perceptual image patch similarity (<0.008) in both channels.

Conclusions:

The proposed semantic communication model offers a superior deep learning-based solution for joint source-channel coding.
The method effectively achieves high compression ratios and minimizes distortion in reconstructed images.
This approach significantly advances image transmission efficiency and quality.