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.
Highlights
There have been significant efforts in producing efficient image coding algorithms based on the wavelet transform and vector quantization (VQ) [1,2,3,4]
multiresolution adaptive vector quantization (MRAVQ) technique has been extended to video coding in [5] to form the adaptive joint subband vector quantization (AJVQ)
The grey (8-bit) “Goldhill,” “camera,” “Lena,” and “Clown” images of size 256 × 256 are used to test the effect of adaptive subband thresholding to the multistage LVQ (MLVQ) image compression scheme
Summary
There have been significant efforts in producing efficient image coding algorithms based on the wavelet transform and vector quantization (VQ) [1,2,3,4]. The image coding scheme based on the wavelet transform and vector quantization in [1, 2] searches for the significant subband coefficients by comparing them to a threshold value at the initial compression stage. This is followed by a EURASIP Journal on Advances in Signal Processing quadtree modelling process of the significant data location. A new technique for searching the significant subband coefficients based on an adaptive thresholding scheme is presented.
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