Vector Quantization using Grey-based Competitive Learning Network in the MDT Domain
نویسنده
چکیده
Based on Vector Quantization (VQ), a Grey-based Competitive Learning Network (GCLN) in the Mean value / Difference value Transform (MDT) domain is proposed. In this paper, the grey theory is applied to a two-layer Modify Competitive Learning Network (MCLN) in order to generate optimal solution for VQ. In accordance with the degree of similarity measures between training vectors and codevectors, the grey relational analysis is used to measure the relationship degree among them. The information transformed by mean value / difference value operation was separated into mean value and detailed coefficients. Then the detailed coefficients are trained using the proposed method to generate a better codebook in VQ. The compression performances using the proposed approach are compared with GCLN and conventional vector quantization LBG method, experimented results show that valid and promising performance can be obtained using the GCLN and proposed approach.
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