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Vector quantisation (VQ) is an efficient technique for data compression and retrieval. But its encoding requires expensive computation that greatly limits its practical use. A fast algorithm for VQ encoding on the basis of features of vectors and subvectors is presented. Making use of three characteristics of a vector: the sum, the partial sum and the partial variance, a four-step eliminating algorithm is introduced. The proposed algorithm can reject a lot of codewords, while holding the same quality of encoded images as the full search algorithm (FSA). Experimental results show that the proposed algorithm needs only a little computational complexity and distortion calculation against the FSA. Compared with the equal-average equal-variance equal-norm nearest neighbour search algorithm based on the ordered Hadamard transform, the proposed algorithm reduces the number of distortion calculations by 8 to 61%. The average number of operations of the proposed algorithm is %79% of that of Zhibin%s method for all test images. The proposed algorithm outperforms most of existing algorithms.
Date of Publication: December 2008