Goal-Oriented Semantic Communication for Wireless Image Transmission via Stable Diffusion
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910848961740800 |
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| author | Li, Nan Deng, Yansha |
| author_facet | Li, Nan Deng, Yansha |
| contents | Efficient image transmission is essential for seamless communication and collaboration within the visually-driven digital landscape. To achieve low latency and high-quality image reconstruction over a bandwidth-constrained noisy wireless channel, we propose a stable diffusion (SD)-based goal-oriented semantic communication (GSC) framework. In this framework, we design a semantic autoencoder that effectively extracts semantic information (SI) from images to reduce the transmission data size while ensuring high-quality reconstruction. Recognizing the impact of wireless channel noise on SI transmission, we propose an SD-based denoiser for GSC (SD-GSC) conditional on an instantaneous channel gain to remove the channel noise from the received noisy SI under known channel. For scenarios with unknown channel, we further propose a parallel SD denoiser for GSC (PSD-GSC) to jointly learn the distribution of channel gains and denoise the received SI. It is shown that, with the known channel, our SD-GSC outperforms state-of-the-art ADJSCC and Latent-Diff DNSC, improving Peak Signal-to-Noise Ratio (PSNR) by 32% and 21%, and reducing Fréchet Inception Distance (FID) by 40% and 35%, respectively. With the unknown channel, our PSD-GSC improves PSNR by 8% and reduces FID by 17% compared to MMSE equalizer-enhanced SD-GSC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00428 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Goal-Oriented Semantic Communication for Wireless Image Transmission via Stable Diffusion Li, Nan Deng, Yansha Image and Video Processing Efficient image transmission is essential for seamless communication and collaboration within the visually-driven digital landscape. To achieve low latency and high-quality image reconstruction over a bandwidth-constrained noisy wireless channel, we propose a stable diffusion (SD)-based goal-oriented semantic communication (GSC) framework. In this framework, we design a semantic autoencoder that effectively extracts semantic information (SI) from images to reduce the transmission data size while ensuring high-quality reconstruction. Recognizing the impact of wireless channel noise on SI transmission, we propose an SD-based denoiser for GSC (SD-GSC) conditional on an instantaneous channel gain to remove the channel noise from the received noisy SI under known channel. For scenarios with unknown channel, we further propose a parallel SD denoiser for GSC (PSD-GSC) to jointly learn the distribution of channel gains and denoise the received SI. It is shown that, with the known channel, our SD-GSC outperforms state-of-the-art ADJSCC and Latent-Diff DNSC, improving Peak Signal-to-Noise Ratio (PSNR) by 32% and 21%, and reducing Fréchet Inception Distance (FID) by 40% and 35%, respectively. With the unknown channel, our PSD-GSC improves PSNR by 8% and reduces FID by 17% compared to MMSE equalizer-enhanced SD-GSC. |
| title | Goal-Oriented Semantic Communication for Wireless Image Transmission via Stable Diffusion |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2408.00428 |