  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. Code available at
https://github.com/frapez1/SPIC
