论文标题
基于生成模型的高效语义通信方法用于图像传输
Generative Model Based Highly Efficient Semantic Communication Approach for Image Transmission
论文作者
论文摘要
近年来，已经探索了基于深度学习（DL）的语义交流方法来有效地传输图像。在本文中，我们提出了一种基于生成模型的语义通信，以进一步提高图像传输的效率并保护私人信息。特别是，发射器通过利用GAN反转方法的生成模型从原始图像中提取可解释的潜在表示。我们还使用隐私过滤器和知识库来删除私人信息，并用知识库中的自然特征替换它。模拟结果表明，与现有方法相比，我们提出的方法可实现接收图像的可比质量，同时显着降低了通信成本。
Deep learning (DL) based semantic communication methods have been explored to transmit images efficiently in recent years. In this paper, we propose a generative model based semantic communication to further improve the efficiency of image transmission and protect private information. In particular, the transmitter extracts the interpretable latent representation from the original image by a generative model exploiting the GAN inversion method. We also employ a privacy filter and a knowledge base to erase private information and replace it with natural features in the knowledge base. The simulation results indicate that our proposed method achieves comparable quality of received images while significantly reducing communication costs compared to the existing methods.
