Image compression is an important topic in remote sensing applications such as in memory-constrained or low-bandwidth environments. An evaluation of a machine learning compression framework jointly paired with a super resolution network, to restore compressed images, is performed. Image quality and compression metrics are analyzed against existing techniques.


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    Titel :

    A Learnable, Super Resolution-Based Framework for Enhancing Compressed Images


    Beteiligte:
    Essig, David (Autor:in) / Gnacek, Matthew (Autor:in) / Fan, David (Autor:in) / Hoffman, Marc (Autor:in) / Westberg, Stefan (Autor:in) / Ratliff, Bradley M. (Autor:in)


    Erscheinungsdatum :

    15.07.2024


    Format / Umfang :

    1600007 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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