GAN-Based Semantic Communication for Image Transmission in IoV

2026-08-28

AI Summary

本文提出基于GAN的语义通信框架，通过金字塔注意力网络和优先级保留机制提高视觉数据传输效率与保真度，实验显示其在分割精度和图像质量上优于现有方法。

Abstract

For cooperative perception in the internet of vehicles, this paper proposes a generative adversarial network-based semantic communication framework to address the efficiency and fidelity bottlenecks of traditional communication systems in visual data transmission under limited bandwidth and dynamic channel conditions. At the transmitter, the framework adopts a pyramid attention network to extract semantic label maps and introduces a semantic priority preservation mechanism. It assigns differentiated weights to distinct semantic categories based on driving safety, guiding bit allocation and loss function design. At the receiver, an image reconstruction module integrating a coarse to-fine multi-resolution generator and multi-scale discriminator is designed. Combined with the temporal consistency branch, spatial pyramid pooling and class-aware convolutional layers, it achieves high-fidelity reconstruction of high-quality images from corrupted semantic labels. The model is trained with combined adversarial, feature matching and perceptual losses, effectively improving semantic consistency and visual realism of generated images. Experimental results on the Cityscapes dataset show that the proposed method outperforms existing counterparts in both semantic segmentation accuracy and reconstructed image quality, and maintains stable reconstruction performance under AWGN and Rayleigh channels.

Problem

• GAN
• semantic communication
• image transmission
• internet of vehicles
• bandwidth limitation

Innovation

• Generative Adversarial Network
• Semantic Communication
• Pyramid Attention Network
• Semantic Priority Preservation
• Multi-Resolution Generator