Semantic communication has emerged as a promising technology for enhancing communication efficiency. However, most existing research emphasizes single-task reconstruction, neglecting model adaptability and generalization across multi-task systems. In this paper, we propose a novel generative semantic communication system that supports both image reconstruction and segmentation tasks. Our approach builds upon semantic knowledge bases (KBs) at both the transmitter and receiver, with each semantic KB comprising a source KB and a task KB. The source KB at the transmitter leverages a hierarchical Swin-Transformer, a generative AI scheme, to extract multi-level features from the input image. Concurrently, the counterpart source KB at the receiver utilizes hierarchical residual blocks to generate task-specific knowledge. Furthermore, the task KBs adopt a semantic similarity model to map different task requirements into pre-defined task instructions, thereby facilitating the feature selection of the source KBs. Additionally, we develop a unified residual block-based joint source and channel (JSCC) encoder and two task-specific JSCC decoders to achieve the two image tasks. In particular, a generative diffusion model is adopted to construct the JSCC decoder for the image reconstruction task. Experimental results show that our multi-task generative semantic communication system outperforms previous single-task communication systems in terms of peak signal-to-noise ratio and segmentation accuracy.

翻译：语义通信已成为提升通信效率的关键技术。然而，现有研究多集中于单任务重建，忽视了模型在多任务系统中的适应性与泛化能力。本文提出一种支持图像重建与分割任务的新型生成式语义通信系统。该方案基于收发双方构建的语义知识库，每个语义知识库由源知识库与任务知识库构成。发送端的源知识库采用分层Swin-Transformer生成式AI架构，从输入图像中提取多层次特征；接收端的对应源知识库则利用分层残差块生成任务特定知识。任务知识库通过语义相似度模型将不同任务需求映射至预定义任务指令，从而优化源知识库的特征选择机制。此外，我们设计了基于统一残差块的联合信源信道编码器及两个任务特定JSCC解码器，以协同完成两项图像任务。特别地，针对图像重建任务，采用生成式扩散模型构建JSCC解码器。实验结果表明：本多任务生成式语义通信系统在峰值信噪比与分割精度方面均优于现有单任务通信系统。