论文标题
在多层次语义信息的帮助下无线传输图像
Wireless Transmission of Images With The Assistance of Multi-level Semantic Information
论文作者
论文摘要
以语义为导向的通信被认为是通过仅传输数据语义来提高带宽效率的有望。在本文中，我们提出了一种用于无线图像传输的多级语义意识通信系统，名为MLSC图像，该系统基于深度学习技术，并以端到端方式进行了培训。特别是，所提出的模型包括一个多级语义特征提取器，该图既提取高级语义信息，例如文本语义和分段语义，以及低级语义信息，例如图像的局部空间详细信息。我们采用验证的图像标题来捕获文本语义和验证的图像分割模型，以获得分割语义。然后将这些高级和低级语义特征通过关节语义和通道编码组合和编码，并编码为符号，以通过物理通道传输。数值结果验证了所提出的语义通信系统的有效性和效率，尤其是在有限的带宽条件下，这表明高级语义在图像压缩中的优势。
Semantic-oriented communication has been considered as a promising to boost the bandwidth efficiency by only transmitting the semantics of the data. In this paper, we propose a multi-level semantic aware communication system for wireless image transmission, named MLSC-image, which is based on the deep learning techniques and trained in an end to end manner. In particular, the proposed model includes a multilevel semantic feature extractor, that extracts both the highlevel semantic information, such as the text semantics and the segmentation semantics, and the low-level semantic information, such as local spatial details of the images. We employ a pretrained image caption to capture the text semantics and a pretrained image segmentation model to obtain the segmentation semantics. These high-level and low-level semantic features are then combined and encoded by a joint semantic and channel encoder into symbols to transmit over the physical channel. The numerical results validate the effectiveness and efficiency of the proposed semantic communication system, especially under the limited bandwidth condition, which indicates the advantages of the high-level semantics in the compression of images.
