End-to-End Generative Semantic Communication Powered by Shared Semantic Knowledge Base
arxiv(2024)
摘要
Semantic communication has drawn substantial attention as a promising
paradigm to achieve effective and intelligent communications. However,
efficient image semantic communication encounters challenges with a lower
testing compression ratio (CR) compared to the training phase. To tackle this
issue, we propose an innovative semantic knowledge base (SKB)-enabled
generative semantic communication system for image classification and image
generation tasks. Specifically, a lightweight SKB, comprising class-level
information, is exploited to guide the semantic communication process, which
enables us to transmit only the relevant indices. This approach promotes the
completion of the image classification task at the source end and significantly
reduces the transmission load. Meanwhile, the category-level knowledge in the
SKB facilitates the image generation task by allowing controllable generation,
making it possible to generate favorable images in resource-constrained
scenarios. Additionally, semantic accuracy is introduced as a new metric to
validate the performance of semantic transmission powered by the SKB.
Evaluation results indicate that the proposed method outperforms the benchmarks
and achieves superior performance with minimal transmission overhead,
especially in the low SNR regime.
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