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  • Research Article
  • Open Access
  • A New Multistage Lattice Vector Quantization with Adaptive Subband Thresholding for Image Compression

    EURASIP Journal on Advances in Signal Processing20072007:092928

    • Received: 22 December 2005
    • Accepted: 2 February 2007
    • Published:


    Lattice vector quantization (LVQ) reduces coding complexity and computation due to its regular structure. A new multistage LVQ (MLVQ) using an adaptive subband thresholding technique is presented and applied to image compression. The technique concentrates on reducing the quantization error of the quantized vectors by "blowing out" the residual quantization errors with an LVQ scale factor. The significant coefficients of each subband are identified using an optimum adaptive thresholding scheme for each subband. A variable length coding procedure using Golomb codes is used to compress the codebook index which produces a very efficient and fast technique for entropy coding. Experimental results using the MLVQ are shown to be significantly better than JPEG 2000 and the recent VQ techniques for various test images.


    • Entropy
    • Test Image
    • Significant Coefficient
    • Image Compression
    • Code Procedure

    Authors’ Affiliations

    Institute for Signal Processing and Communications, Department of Electronic and Electrical Engineering, University of Strathclyde, Royal College Building, Glasgow, G1 1XW, UK


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    © M. F. M. Salleh and J. Soraghan. 2007

    This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.