标题： 基于深度学习的语义通信系统用于视频传输 显示英文标题

标题： Deep Learning Enabled Semantic Communication Systems for Video Transmission

摘要： 语义通信已成为提高下一代无线网络中高效传输的有前途的方法。 受语义通信在不同领域取得成功的启发，我们旨在从语义层面提供一种新的语义通信方案。 在本文中，我们提出了一种基于深度学习的视频传输语义通信系统，该系统压缩与语义相关的信息以提高传输效率。 特别是，我们利用双光流来估计帧间细节的残余信息。 我们还提出了一种特征选择模块和一种特征融合模块，在丢弃语义冗余特征的同时更加关注重要的语义相关内容。 我们采用了一个帧预测模块，从接收端接收到的信号中重建预测帧的语义特征。 为了增强系统的鲁棒性，我们提出了一种噪声注意力模块，该模块为提取的特征分配不同的重要性权重。 仿真结果表明，我们的方法在传输效率方面优于现有方法，在减少传输符号数量约33.3%的同时，平均提高了0.56dB的峰值信噪比（PSNR）性能。 显示英文摘要

摘要： Semantic communication has emerged as a promising approach for improving efficient transmission in the next generation of wireless networks. Inspired by the success of semantic communication in different areas, we aim to provide a new semantic communication scheme from the semantic level. In this paper, we propose a novel DL-based semantic communication system for video transmission, which compacts semantic-related information to improve transmission efficiency. In particular, we utilize the Bi-optical flow to estimate residual information of inter-frame details. We also propose a feature choice module and a feature fusion module to drop semantically redundant features while paying more attention to the important semantic-related content. We employ a frame prediction module to reconstruct semantic features of the prediction frame from the received signal at the receiver. To enhance the system's robustness, we propose a noise attention module that assigns different importance weights to the extracted features. Simulation results indicate that our proposed method outperforms existing approaches in terms of transmission efficiency, achieving about 33.3\% reduction in the number of transmitted symbols while improving the peak signal-to-noise ratio (PSNR) performance by an average of 0.56dB.