# Deep Image Semantic Communication Model for Artificial Intelligent Internet of Things Source: [Deep Image Semantic Communication Model for Artificial Intelligent Internet of Things]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:57:41.618Z Authors: Li Ping Qian, Yi Zhang, Sikai Lyu, Huijie Zhu, Yuan Wu, Xuemin Sherman Shen, Xiaoniu Yang Published: 2023-11-06T07:43:42.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()