# Deep Learning Enabled Semantic Communication Systems for Video Transmission

arXiv: https://arxiv.org/abs/2312.05062 · HTML: https://mlsanity.com/paper/2312.05062/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2023.

## Built on by (most cited first, 2 of 2)

- [Large Generative Model-assisted Talking-face Semantic Communication System](https://mlsanity.com/paper/2411.03876/) (2024)
- [Object-Attribute-Relation Representation Based Video Semantic Communication](https://mlsanity.com/paper/2406.10469/) (2024)

## Abstract (arXiv)

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.