Semantic communication, rather than on a bit-by-bit recovery of the transmitted messages, focuses on the meaning and the goal of the communication itself. In this paper, we propose a novel semantic image coding scheme that preserves the semantic content of an image, while ensuring a good trade-off between coding rate and image quality. The proposed Semantic-Preserving Image Coding based on Conditional Diffusion Models (SPIC) transmitter encodes a Semantic Segmentation Map (SSM) and a low-resolution version of the image to be transmitted. The receiver then reconstructs a high-resolution image using a Denoising Diffusion Probabilistic Models (DDPM) doubly conditioned to the SSM and the low-resolution image. As shown by the numerical examples, compared to state-of-the-art (SOTA) approaches, the proposed SPIC exhibits a better balance between the conventional rate-distortion trade-off and the preservation of semantically-relevant features.

翻译：语义通信关注的是通信本身的含义和目标，而非对传输消息的逐比特恢复。本文提出一种新颖的语义图像编码方案，该方案在保持图像语义内容的同时，确保了编码速率与图像质量之间的良好平衡。所提出的基于条件扩散模型的语义保持图像编码(SPIC)方案中，发射端编码语义分割图(SSM)和待传输图像的低分辨率版本，接收端则利用双重条件约束于SSM和低分辨率图像的去噪扩散概率模型(DDPM)重建高分辨率图像。数值示例表明，与现有最优(SOTA)方法相比，所提SPIC方案在传统率失真权衡与语义相关特征保持之间展现出更优的平衡性能。