A Hybrid Wireless Image Transmission Scheme with Diffusion
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Abstract (arXiv)
We propose a hybrid joint source-channel coding (JSCC) scheme, in which the conventional digital communication scheme is complemented with a generative refinement component to improve the perceptual quality of the reconstruction. The input image is decomposed into two components: the first is a coarse compressed version, and is transmitted following the conventional separation based approach. An additional component is obtained through the diffusion process by adding independent Gaussian noise to the input image, and is transmitted using DeepJSCC. The decoder combines the two signals to produce a high quality reconstruction of the source. Experimental results show that the hybrid design provides bandwidth savings and enables graceful performance improvement as the channel quality improves.
Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Aug 2023.
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Citing papers per year: 2023: 6; 2024: 2; 2025: 3; 2026: 1.
12 papers.
- 2023High Perceptual Quality Wireless Image Delivery with Denoising Diffusion Modelscited by 23
- 2024Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraintscited by 19
- 2023Generative AI for Physical Layer Communications: A Surveycited by 17
- 2025Semantics-Guided Diffusion for Deep Joint Source-Channel Coding in Wireless Image Transmissioncited by 16
- 2023CommIN: Semantic Image Communications as an Inverse Problem with INN-Guided Diffusion Modelscited by 14
- 2023Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communicationscited by 10
- 2024Semantic Successive Refinement: A Generative AI-aided Semantic Communication Frameworkcited by 9
- 2023Diffusion Models for Wireless Communicationscited by 6
- 2025SING: Semantic Image Communications using Null-Space and INN-Guided Diffusion Modelscited by 5
- 2026DiT-JSCC: Rethinking Deep JSCC with Diffusion Transformers and Semantic Representationscited by 1
- 2023Denoising Diffusion Probabilistic Models for Hardware-Impaired Communicationscited by 0
- 2025TOAST: Task-Oriented Adaptive Semantic Transmission over Dynamic Wireless Environmentscited by 0
- 2026DiT-JSCC: Rethinking Deep JSCC with Diffusion Transformers and Semantic Representations6 Jan 2026
- 2025TOAST: Task-Oriented Adaptive Semantic Transmission over Dynamic Wireless Environments27 Jun 2025
- 2025SING: Semantic Image Communications using Null-Space and INN-Guided Diffusion Models16 Mar 2025
- 2025Semantics-Guided Diffusion for Deep Joint Source-Channel Coding in Wireless Image Transmission2 Jan 2025
- 2024Semantic Successive Refinement: A Generative AI-aided Semantic Communication Framework31 Jul 2024
- 2024Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraints26 Jul 2024
- 2023Generative AI for Physical Layer Communications: A Survey9 Dec 2023
- 2023Conditional Denoising Diffusion Probabilistic Models for Data Reconstruction Enhancement in Wireless Communications30 Oct 2023
- 2023Diffusion Models for Wireless Communications11 Oct 2023
- 2023CommIN: Semantic Image Communications as an Inverse Problem with INN-Guided Diffusion Models2 Oct 2023
- 2023High Perceptual Quality Wireless Image Delivery with Denoising Diffusion Models27 Sep 2023
- 2023Denoising Diffusion Probabilistic Models for Hardware-Impaired Communications15 Sep 2023