Language-Oriented Semantic Latent Representation for Image Transmission

Giordano Cicchetti, Eleonora Grassucci, Jihong Park, Jinho Choi, Sergio Barbarossa, Danilo Comminiello

Abstract: 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 pre-cisely capture sophisticated visual features such as spatial locations, color, and texture, incurring a significant percep-tual 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/.