Wireless Transmission of Images With The Assistance of Multi-level Semantic Information
This work addresses bandwidth constraints in wireless image transmission for communication systems, but it is incremental as it builds on existing semantic communication and deep learning techniques.
The authors tackled the problem of bandwidth-efficient wireless image transmission by proposing a multi-level semantic communication system that extracts and combines high-level (text and segmentation) and low-level (spatial details) semantic features, achieving improved efficiency under limited bandwidth conditions.
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.