Robust Semantic Transmission of Images with Generative Adversarial Networks

Qi He*, Haohan Yuan*, Daquan Feng, Bo Che*, Zhi Chen*, Xiang Gen Xia

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)

Abstract

Image compression and bit transmission are con-ducted separately in most existing methods for image trans-mission, leading to possible transmission failure or a waste of communication resource for a time-varying channel condition. This paper proposes a neural network-based image transmission system trained by generative adversarial networks (GANs) aiming to achieve robust transmission. Specifically, the deep semantic of an input image is extracted and represented as bit streams at the transmitter, and the receiver reconstructs the original image based on possible bit error and the same background knowledge as the transmitter. Experimental results show that the proposed robust transmission system trained by GAN can adapt to the current communication condition, and achieve a high-quality reconstruction even with a high transmission error rate and a smaller transmission data size than engineered codecs such as JPEG.

Original languageEnglish
Pages (from-to)3953-3958
Number of pages6
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event2022 IEEE Global Communications Conference, GLOBECOM 2022 - Virtual, Online, Brazil
Duration: 4 Dec 20228 Dec 2022

Keywords

  • generative adversarial net-work
  • image compression
  • image transmission
  • neural network

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