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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Image compression using fast transformed vector quantization


    Contributors:
    Li, R. (author) / Kim, J. (author)


    Publication date :

    2000-01-01


    Size :

    293634 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    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 Archive | 2000