# SC-CDM: Enhancing Quality of Image Semantic Communication with a Compact Diffusion Model

arXiv: https://arxiv.org/abs/2410.02121 · HTML: https://mlsanity.com/paper/2410.02121/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models.
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 3 Oct 2024.

## Placed in

- Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/)

## Builds on (most cited first, 11 of 11)

- [DiffIR: Efficient Diffusion Model for Image Restoration](https://mlsanity.com/paper/2303.09472/) (2023)
- [DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Coding](https://mlsanity.com/paper/2206.08100/) (2022)
- [WITT: A Wireless Image Transmission Transformer for Semantic Communications](https://mlsanity.com/paper/2211.00937/) (2022)
- [Wireless End-to-End Image Transmission System using Semantic Communications](https://mlsanity.com/paper/2302.13721/) (2023)
- [Generative Model Based Highly Efficient Semantic Communication Approach for Image Transmission](https://mlsanity.com/paper/2211.10287/) (2022)
- [Latent Semantic Diffusion-based Channel Adaptive De-Noising SemCom for Future 6G Systems](https://mlsanity.com/paper/2304.09420/) (2023)
- [Semantic-Forward Relaying: A Novel Framework Towards 6G Cooperative Communications](https://mlsanity.com/paper/2310.07987/) (2023)
- [SemantIC: Semantic Interference Cancellation Towards 6G Wireless Communications](https://mlsanity.com/paper/2310.12768/) (2023)
- [Wireless Transmission of Images With The Assistance of Multi-level Semantic Information](https://mlsanity.com/paper/2202.04754/) (2022)
- [Two-Way Semantic Transmission of Images without Feedback](https://mlsanity.com/paper/2306.08903/) (2023)
- [Scalable Extraction Based Semantic Communication for 6G Wireless Networks](https://mlsanity.com/paper/2407.11334/) (2024)

## Built on by (most cited first, 1 of 1)

- [Generative Semantic Communication: Architectures, Technologies, and Applications](https://mlsanity.com/paper/2412.08642/) (2024)

## Abstract (arXiv)

Semantic Communication (SC) is an emerging technology that has attracted much attention in the sixth-generation (6G) mobile communication systems. However, few literature has fully considered the perceptual quality of the reconstructed image. To solve this problem, we propose a generative SC for wireless image transmission (denoted as SC-CDM). This approach leverages compact diffusion models to improve the fidelity and semantic accuracy of the images reconstructed after transmission, ensuring that the essential content is preserved even in bandwidth-constrained environments. Specifically, we aim to redesign the swin Transformer as a new backbone for efficient semantic feature extraction and compression. Next, the receiver integrates the slim prior and image reconstruction networks. Compared to traditional Diffusion Models (DMs), it leverages DMs' robust distribution mapping capability to generate a compact condition vector, guiding image recovery, thus enhancing the perceptual details of the reconstructed images. Finally, a series of evaluation and ablation studies are conducted to validate the effectiveness and robustness of the proposed algorithm and further increase the Peak Signal-to-Noise Ratio (PSNR) by over 17% on top of CNN-based DeepJSCC.