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arXiv · 2408.05455

Multimodal generative semantic communication based on latent diffusion model

Weiqi Fu·Lianming Xu·Xin Wu·Haoyang Wei·Li Wang
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Abstract

In emergencies, the ability to quickly and accurately gather environmental data and command information, and to make timely decisions, is particularly critical. Traditional semantic communication frameworks, primarily based on a single modality, are susceptible to complex environments and lighting conditions, thereby limiting decision accuracy. To this end, this paper introduces a multimodal generative semantic communication framework named mm-GESCO. The framework ingests streams of visible and infrared modal image data, generates fused semantic segmentation maps, and transmits them using a combination of one-hot encoding and zlib compression techniques to enhance data transmission efficiency. At the receiving end, the framework can reconstruct the original multimodal images based on the semantic maps. Additionally, a latent diffusion model based on contrastive learning is designed to align different modal data within the latent space, allowing mm-GESCO to reconstruct latent features of any modality presented at the input. Experimental results demonstrate that mm-GESCO achieves a compression ratio of up to 200 times, surpassing the performance of existing semantic communication frameworks and exhibiting excellent performance in downstream tasks such as object classification and detection.

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Weiqi Fu, Lianming Xu, Xin Wu, Haoyang Wei, Li Wang. 2024-08-10. Multimodal generative semantic communication based on latent diffusion model. https://arxiv.org/abs/2408.05455

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