Rate-Adaptive Generative Semantic Communication Using Conditional Diffusion Models
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| Izdano u: | arXiv.org (Dec 23, 2024), p. n/a |
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| Glavni autor: | |
| Daljnji autori: | , |
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Cornell University Library, arXiv.org
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| Teme: | |
| Online pristup: | Citation/Abstract Full text outside of ProQuest |
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3100998076 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3100998076 | ||
| 045 | 0 | |b d20241223 | |
| 100 | 1 | |a Yang, Pujing | |
| 245 | 1 | |a Rate-Adaptive Generative Semantic Communication Using Conditional Diffusion Models | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 23, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Recent advances in deep learning-based joint source-channel coding (DJSCC) have shown promise for end-to-end semantic image transmission. However, most existing schemes primarily focus on optimizing pixel-wise metrics, which often fail to align with human perception, leading to lower perceptual quality. In this letter, we propose a novel generative DJSCC approach using conditional diffusion models to enhance the perceptual quality of transmitted images. Specifically, by utilizing entropy models, we effectively manage transmission bandwidth based on the estimated entropy of transmitted sym-bols. These symbols are then used at the receiver as conditional information to guide a conditional diffusion decoder in image reconstruction. Our model is built upon the emerging advanced mamba-like linear attention (MLLA) skeleton, which excels in image processing tasks while also offering fast inference speed. Besides, we introduce a multi-stage training strategy to ensure the stability and improve the overall performance of the model. Simulation results demonstrate that our proposed method significantly outperforms existing approaches in terms of perceptual quality. | |
| 653 | |a Image transmission | ||
| 653 | |a Diffusion rate | ||
| 653 | |a Semantics | ||
| 653 | |a Entropy | ||
| 653 | |a Image quality | ||
| 653 | |a Image reconstruction | ||
| 653 | |a Image enhancement | ||
| 653 | |a Information management | ||
| 653 | |a Image processing | ||
| 700 | 1 | |a Zhang, Guangyi | |
| 700 | 1 | |a Cai, Yunlong | |
| 773 | 0 | |t arXiv.org |g (Dec 23, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3100998076/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2409.02597 |