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A New FPCM Based Vector Quantization Design for Image Compression in Wavelet Domain

R. Nagendran, P. Arockia Jansi Rani


This paper presents a new Vector Quantizer codebook design using Fuzzy Possibilistic C Means (FPCM) clustering technique. The idea is to achieve higher compression ratio based on clustering the wavelet coefficients of each Discrete Wavelet Transformed (DWT) band. The methodology applied here is to apply DWT to the whole image. The sub blocks are decomposed into one level wavelet where the coefficients are clustered using FPCM. The centroids of each cluster is arranged in the form of a codebook and indexed. The index values are coded and then transmitted across. The reconstructed image is constructed using the inverse DWT. The results show that the psycho-visual fidelity criteria (both subjective and objective measures) of the proposed FPCM technique are better than other existing clustering techniques for vector quantization.


Image Compression, Discrete Wavelet Transform, Fuzzy Possibilistic C Means, Vector quantization.

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