With the rapid development of Artificial Intelligent Internet of Things (AIoT), the image data from AIoT devices has been witnessing the explosive increasing. In this paper, a novel deep image semantic communication model is proposed for the efficient image communication in AIoT. Particularly, at the transmitter side, a high-precision image semantic segmentation algorithm is proposed to extract the semantic information of the image to achieve significant compression of the image data. At the receiver side, a semantic image restoration algorithm based on Generative Adversarial Network (GAN) is proposed to convert the semantic image to a real scene image with detailed information. Simulation results demonstrate that the proposed image semantic communication model can improve the image compression ratio and recovery accuracy by 71.93% and 25.07% on average in comparison with WebP and CycleGAN, respectively. More importantly, our demo experiment shows that the proposed model reduces the total delay by 95.26% in the image communication, when comparing with the original image transmission.

翻译：随着人工智能物联网的快速发展，来自AIoT设备的图像数据呈现爆发式增长。本文提出了一种新颖的深度图像语义通信模型，用于实现AIoT中高效的图像通信。具体而言，在发送端，本文提出了一种高精度图像语义分割算法，用于提取图像的语义信息，从而实现图像数据的显著压缩。在接收端，提出了一种基于生成对抗网络的语义图像恢复算法，将语义图像转换为带有细节信息的真实场景图像。仿真结果表明，与WebP和CycleGAN相比，本文提出的图像语义通信模型平均可提升图像压缩比71.93%，恢复精度提升25.07%。更重要的是，演示实验表明，与原始图像传输相比，该模型在图像通信中将总延迟降低了95.26%。