Deep Image Semantic Communication Model for Artificial Intelligent Internet of Things
This addresses the problem of high data volume and latency in image transmission for AIoT devices, representing an incremental improvement with specific gains.
The paper tackles efficient image communication in Artificial Intelligent Internet of Things by proposing a deep image semantic communication model, which improves image compression ratio by 71.93% and recovery accuracy by 25.07% on average compared to baselines, and reduces total delay by 95.26% in demo experiments.
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.