Digital image compression is an important technique in digital image processing. To improve its performance, we attempt to speed up the design process and achieve the highest compression ratio where possible. For speed improvement, we used a fast Kohonen self-organizing neural network algorithm to achieve big saving in codebook construction time. For compression purpose, we propose a new approach, called fast transformed vector quantization (FTVQ), by combining together the features of speed improvement, transform coding and vector quantization. We use several experiments to demonstrate the feasibility of this FTVQ approach.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Image compression using fast transformed vector quantization


    Beteiligte:
    Li, R. (Autor:in) / Kim, J. (Autor:in)


    Erscheinungsdatum :

    01.01.2000


    Format / Umfang :

    293634 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Image Compression Using Fast Transformed Vector Quantization

    Li, R. / Kim, J. | British Library Conference Proceedings | 2000


    Image compression using transformed vector quantization

    Li, R. Y. / Kim, J. / Al-Shamakhi, N. | British Library Online Contents | 2002


    Object-based SAR image compression using vector quantization

    Venkatraman, M. / Kwon, H. / Nasrabadi, N.M. | IEEE | 2000


    Object-based SAR image compression using vector quantization

    Venkatraman, M. / Kwon, H. / Nasrabadi, N.M. | Tema Archiv | 2000