Language-Oriented Semantic Latent Representation for Image Transmission
<|reference_start|>Language-Oriented Semantic Latent Representation for Image Transmission: 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/ .<|reference_end|>
@article{cicchetti2024language-oriented,
    title={Language-Oriented Semantic Latent Representation for Image Transmission},
    author={Giordano Cicchetti, Eleonora Grassucci, Jihong Park, Jinho Choi,
  Sergio Barbarossa, and Danilo Comminiello},
    journal={arXiv preprint arXiv:2405.09976},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.09976},
    primaryClass={cs.CV eess.SP}
}