面向机器视觉任务的语义传输方案

Semantic Transmission Schemes for Machine Vision Tasks

随着信息通信、人工智能、物联网等技术不断融合,智慧城市、自动驾驶、工业互联网等垂直行业领域快速发展,由此产生了大量机器视觉任务以及海量的图像视频数据传输需求。语义通信技术作为一种高效的新型通信方式,关注传输数据的内在含义,通过从冗余的业务信源中提取语义信息进行编码传输,可以显著提升传输效率。针对图像信源传输及处理需求,结合语义分割、注意力机制等方式,探究了端到端图像语义传输方案。进而,面向图像重构任务,考虑动态信道条件,设计了基于卷积神经网络的端到端语义自适应传输框架,并利用信源信道联合编码方法,进一步提高传输性能。仿真分析表明,相比传统通信方案,所提方案在低信噪比条件下,实现峰值信噪比不低于30 dB的同时,实现结构相似性指数高于0.9,有效提升了图像重构质量。

With the continuous integration of information communications,artificial intelligence,and the Internet of Things,vertical industries such as smart cities,autonomous driving,and industrial Internet have experienced rapid developments.This expansion has led to a substantial increase in machine vision tasks and the needs for eficient transmission of massive image and video data.Semantic communication technology has emerged as a novel and efficient method that focuses on conveying the intrinsic meaning of transmitted data.By extracting semantic information from redundant service sources for encoding and transmission,it offers the potential to significantly improve transmission efficiency.To meet the demands for image source transmission and processing,an end-to-end image semantic transmission scheme is investigated via integrating se-mantic segmentation,atention mechanisms,and other advanced techniques.Furthermore,in image reconstruction tasks,considering dynamic channel conditions,an end-to-end semantic adaptive transmission framework is designed based on convolutional neural networks,and a joint source-channel coding method is used to further enhance transmission perfor-mance.Simulation results demonstrates that compared with traditional communication solutions,the proposed approach achieves a peak signal-tonoise ratio of no less than 30 dB under low signal-to-noise ratio conditions and meanwhile achieves a structural similarity index higher than 0.9,effectively enhancing the quality of image reconstruction.