# Generative Model Based Highly Efficient Semantic Communication Approach for Image Transmission

arXiv: https://arxiv.org/abs/2211.10287 · HTML: https://mlsanity.com/paper/2211.10287/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2022.

## Built on by (most cited first, 11 of 11)

- [Generative Semantic Communication: Diffusion Models Beyond Bit Recovery](https://mlsanity.com/paper/2306.04321/) (2023)
- [Language-Oriented Semantic Latent Representation for Image Transmission](https://mlsanity.com/paper/2405.09976/) (2024)
- [How to Evaluate Semantic Communications for Images with ViTScore Metric?](https://mlsanity.com/paper/2309.04891/) (2023)
- [Energy-Efficient Downlink Semantic Generative Communication with Text-to-Image Generators](https://mlsanity.com/paper/2306.05041/) (2023)
- [Semantic Edge-Cloud Communication for Real-Time Urban Traffic Surveillance with ViT and LLMs over Mobile Networks](https://mlsanity.com/paper/2509.21259/) (2025)
- [Enhancing Semantic Communication with Deep Generative Models -- An ICASSP Special Session Overview](https://mlsanity.com/paper/2309.02478/) (2023)
- [SC-CDM: Enhancing Quality of Image Semantic Communication with a Compact Diffusion Model](https://mlsanity.com/paper/2410.02121/) (2024)
- [Baseline-Relative Counterfactual Refinement for Bit-Aware Visual Token Communication](https://mlsanity.com/paper/2608.16192/) (2026)
- [Knowledge-Aided Semantic Communication Leveraging Probabilistic Graphical Modeling](https://mlsanity.com/paper/2408.04499/) (2024)
- [CASC: Condition-Aware Semantic Communication with Latent Diffusion Models](https://mlsanity.com/paper/2411.06552/) (2024)
- [Selective Amortization of Full-Budget Counterfactual Reasoning for Visual Token Communication](https://mlsanity.com/paper/2609.30756/) (2026)

## Abstract (arXiv)

Deep learning (DL) based semantic communication methods have been explored to transmit images efficiently in recent years. In this paper, we propose a generative model based semantic communication to further improve the efficiency of image transmission and protect private information. In particular, the transmitter extracts the interpretable latent representation from the original image by a generative model exploiting the GAN inversion method. We also employ a privacy filter and a knowledge base to erase private information and replace it with natural features in the knowledge base. The simulation results indicate that our proposed method achieves comparable quality of received images while significantly reducing communication costs compared to the existing methods.