Abstract
In conventional lossless transmission (CLT)-based real-time video transmission (RTVT), the user-perceived quality of the transmitted video frames decreases significantly even when there exists 1% packet loss. To improve the quality of experience of RTVT with lossy channels, we propose a semantic communication-based deep lossy transmission (DLT) paradigm by using a deep video semantic coding (DeepVSC) model to achieve end-to-end deep joint source-channel coding in RTVT, such that the quality of the recovered video frames can be significantly improved in lossy transmission scenarios by leveraging the strong data compression and error correction capabilities of DeepVSC. We present the basic framework of DLT, compare it with the CLT system, and an illustrative test shows that DLT can recover the image when packet loss rate (PLR) is 80%, while in CLT the images failed to be reconstructed when the PLR is 10%. We also summarize the research challenges of DLT to motivate more future research efforts in this area.
| Original language | English |
|---|---|
| Pages | 69-76 |
| Number of pages | 8 |
| Volume | 14 |
| No | 2 |
| Specialist publication | IEEE Consumer Electronics Magazine |
| DOIs | |
| State | Published - 2025 |
Bibliographical note
Publisher Copyright:© 2012 IEEE.
Funding
The author would like to thank the anonymous reviewers for their valuable comments which helped them improve the quality and presentation of this article. This work was supported in part by the King Saud University, Riyadh, Saudi Arabia, through the Researchers Supporting Project RSP2024R184 and in part by the Science, Technology & Innovation Project of Xiongan New Area 2022XAGG0114.
| Funders | Funder number |
|---|---|
| King Saud University | RSP2024R184, 2022XAGG0114 |
| King Saud University |
ASJC Scopus subject areas
- Human-Computer Interaction
- Hardware and Architecture
- Computer Science Applications
- Electrical and Electronic Engineering