Semantic communication, a promising candidate for 6G technology, has become a research hot spot. However, existing studies tend to focus more on image reconstruction rather than accurately transmitting semantic information at the pixel level. This paper introduces a novel approach using codec-based Masked AutoEncoders (MAE) for efficient image transmission. The proposed system compresses local information into low-dimensional latent vectors, improving system efficiency. We also design a selective module for enhanced image reconstruction and implement Noise Adversarial Training (NAT) to increase the system’s resilience to channel noise. Experimental results show that our method effectively improves downstream tasks while preserving image quality.


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    Titel :

    Semantic Communication for Efficient Image Transmission Tasks based on Masked Autoencoders


    Beteiligte:
    Wu, Jiale (Autor:in) / Wu, Celimuge (Autor:in) / Lin, Yangfei (Autor:in) / Bao, Jingjing (Autor:in) / Du, Zhaoyang (Autor:in) / Zhong, Lei (Autor:in) / Chen, Xianfu (Autor:in) / Ji, Yusheng (Autor:in)


    Erscheinungsdatum :

    10.10.2023


    Format / Umfang :

    1666593 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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