Vector Quantized Semantic Communication System
This addresses the need for efficient digital semantic communication systems in image transmission, though it is incremental as it builds on existing analog semantic communication methods.
The paper tackles the problem of digital semantic communication for image transmission by developing a vector quantized semantic communication system (VQ-DeepSC), which shows improved robustness over BPG and comparable MS-SSIM performance to DeepJSCC.
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 is more robustness than BPG in digital communication systems and has comparable MS-SSIM performance to the DeepJSCC method.