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.

翻译：语义通信作为一种实现高效智能通信的有前景范式已引起广泛关注。然而，高效图像语义通信在测试压缩比低于训练阶段时面临挑战。针对此问题，我们提出一种创新的语义知识库赋能生成式语义通信系统，用于图像分类与图像生成任务。具体而言，采用包含类别级信息的轻量级语义知识库指导语义通信过程，实现仅传输相关索引。该方法可促进源端完成图像分类任务，并显著降低传输负载。同时，语义知识库中的类别级知识通过可控生成能力赋能图像生成任务，使其在资源受限场景下得以生成高质量图像。此外，引入语义准确率作为新指标，验证基于语义知识库的语义传输性能。评估结果表明，所提方法优于基准方案，尤其在低信噪比环境下能以最小传输开销实现卓越性能。