Fast searching algorithm for vector quantisation based on features of vector and subvector

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.