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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

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Published on: August 13, 2014

Feature-Driven Semantic Communication for Efficient Image Transmission.

Ji Zhang^1,2,3, Ying Zhang^1,2, Baofeng Ji^1,2

¹School of Mathematics and Statistics, Henan University of Science and Technology, Luoyang 471000, China.

Entropy (Basel, Switzerland)

|April 26, 2025

Summary

Semantic communication improves image transmission by non-uniformly quantizing features. This method prioritizes important data, reducing bandwidth needs while maintaining image quality.

Keywords:

image feature extraction non-uniform quantization semantic communication

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Last Updated: May 10, 2025

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Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

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Published on: March 20, 2017

Area of Science:

Computer Science
Electrical Engineering
Information Theory

Background:

Semantic communication enhances transmission efficiency by conveying information's core meaning.
Current image semantic communication systems often use uniform feature compression, neglecting feature importance for recovery.
Bandwidth limitations are a critical challenge in real-world image transmission.

Purpose of the Study:

To propose a novel semantic communication system for image transmission that addresses bandwidth constraints.
To introduce non-uniform quantization techniques for differential feature processing.
To improve image recovery quality under limited bandwidth.

Main Methods:

Developed a semantic communication system incorporating non-uniform quantization.
Implemented a dynamic bit allocation algorithm for features based on their contribution to receiver-end tasks.
Varied quantization levels according to feature importance for image reconstruction.

Main Results:

The proposed system significantly reduces bandwidth requirements compared to uniform processing.
Image reconstruction quality is maintained or improved under bandwidth constraints.
Non-uniform feature processing demonstrates superior performance over uniform methods.

Conclusions:

Non-uniform quantization is an effective strategy for semantic image communication under bandwidth limitations.
Dynamic bit allocation based on feature importance optimizes data transmission.
The developed system offers a practical solution for efficient and high-quality image transmission in constrained environments.