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A New Multistage Lattice Vector Quantization with Adaptive Subband Thresholding for Image Compression

Abstract

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.

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Correspondence to M. F. M. Salleh.

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Keywords

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