  In the new paradigm of semantic communication (SC), the focus is on
delivering meanings behind bits by extracting semantic information from raw
data. Recent advances in data-to-text models facilitate language-oriented SC,
particularly for text-transformed image communication via image-to-text (I2T)
encoding and text-to-image (T2I) decoding. However, although semantically
aligned, the text is too coarse to precisely capture sophisticated visual
features such as spatial locations, color, and texture, incurring a significant
perceptual difference between intended and reconstructed images. To address
this limitation, in this paper, we propose a novel language-oriented SC
framework that communicates both text and a compressed image embedding and
combines them using a latent diffusion model to reconstruct the intended image.
Experimental results validate the potential of our approach, which transmits
only 2.09\% of the original image size while achieving higher perceptual
similarities in noisy communication channels compared to a baseline SC method
that communicates only through text.The code is available at
https://github.com/ispamm/Img2Img-SC/ .
