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.

提出了一种用于 AIoT 中高效图像通信的新型深度图像语义通信模型，通过语义分割算法实现对图像的高精度语义信息提取以实现图像数据的显著压缩，通过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具有详细信息的实景图像，结果表明，与 WebP 和 CycleGAN 相比，所提出的图像语义通信模型在图像压缩比和恢复准确度上平均分别提高了 71.93% 和 25.07％，而且与原始图像传输相比，我们的演示实验证明该模型可以将传输中的总延迟减少了 95.26％。