Although analog semantic communication systems have received considerable attention in the literature, there is less work on digital semantic communication systems. In this paper, we develop a deep learning (DL)-enabled vector quantized (VQ) semantic communication system for image transmission, named VQ-DeepSC. Specifically, we propose a convolutional neural network (CNN)-based transceiver to extract multi-scale semantic features of images and introduce multi-scale semantic embedding spaces to perform semantic feature quantization, rendering the data compatible with digital communication systems. Furthermore, we employ adversarial training to improve the quality of received images by introducing a PatchGAN discriminator. Experimental results demonstrate that the proposed VQ-DeepSC outperforms traditional image transmission methods in terms of SSIM.

翻译：虽然模拟语义通信系统在文献中受到相当重视,但在数字语义通信系统方面的工作较少,在本文中,我们开发了一个名为VQ-DeepSC的图像传输深层次学习(DL)驱动矢量量化(VQ)语义通信系统。具体地说,我们提议了一个基于革命神经网络的收发器,以提取图像的多尺度语义特征,并引入多尺度的语义嵌入空间,以进行语义特征量化,使数据与数字通信系统兼容。此外,我们采用对抗性培训,通过引入PatchGAN歧视器来提高接收图像的质量。实验结果表明,拟议的VQ-DepSC在SIM方面超越了传统的图像传输方法。