This paper presents a new VQ technique called the SA-K algorithm which incorporates the simulated annealing mechanism into Kohonen's competitive learning to produce high quality codebooks. With a proper temperature schedule, the SA-K algorithm asymptotically becomes a descent competitive learning algorithm and both the centroid and the nearest neighbor conditions for optimality are satisfied, while the SA technique guarantees that the SA-K algorithm performs in a globally optimal manner. Experimental comparisons among the SA-K, Kohonen learning algorithm (KLA) and LBG algorithm for speech source data are given. The novel algorithm consistently shows the advantage over the KLA and LBG algorithm in the design of vector quantizers with different codebook sizes.<>


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A new vector quantization algorithm based on simulated annealing


    Contributors:
    Zhenya He (author) / Chenwu Wu (author) / Jun Wang (author) / Ce Zhu (author)


    Publication date :

    1994-01-01


    Size :

    246273 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A New Vector Quantization Algorithm Based on Simulated Annealing

    He, Z. / Wu, C. / Wang, J. et al. | British Library Conference Proceedings | 1994


    Iterative sIB Algorithm based on Simulated Annealing

    Yuan, H. / Ye, Y. / Deng, J. | British Library Online Contents | 2010


    An adaptive hybrid vector quantization algorithm

    Romriell, Joseph / Budge, Scott | AIAA | 1993


    Super-resolution algorithm based on weighted vector quantization

    Kuo, T.-M. / Tai, S.-C. | British Library Online Contents | 2014


    An Adaptive Hybrid Vector Quantization Algorithm

    Romriel, J. / Budge, S. / AIAA | British Library Conference Proceedings | 1993