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（AIoT）的快速发展，AIoT设备产生的图像数据呈爆炸式增长。本文针对AIoT中高效的图像通信问题，提出了一种新型深度图像语义通信模型。具体而言，在发送端，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大幅压缩；在接收端，提出了一种基于生成对抗网络（GAN）的语义图像恢复算法，用于将语义图像转化为包含细节信息的真实场景图像。仿真结果表明，与WebP和CycleGAN相比，所提出的图像语义通信模型平均可将图像压缩比和恢复精度分别提升71.93%和25.07%。更重要的是，我们的演示实验表明，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%。