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arXiv · 2311.02926

Deep Image Semantic Communication Model for Artificial Intelligent Internet of Things

Li Ping Qian·Yi Zhang·Sikai Lyu·Huijie Zhu·Yuan Wu·Xuemin Sherman Shen·Xiaoniu Yang
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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.

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Li Ping Qian, Yi Zhang, Sikai Lyu, Huijie Zhu, Yuan Wu, Xuemin Sherman Shen, Xiaoniu Yang. 2023-11-08. Deep Image Semantic Communication Model for Artificial Intelligent Internet of Things. https://arxiv.org/abs/2311.02926

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