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The encoding process of vector quantization (VQ) is a time bottleneck to its practical application. In order to speed up the process of VQ encoding, it is possible to estimate the Euclidean distance first with just a lighter computation to try to reject a candidate codeword. In order to estimate the Euclidean distance, appropriate features of a vector become necessary. By using the famous statistical features of the sum and variance for a k-dimensional vector and furthermore for its two corresponding (k/2)-dimensional subvectors, it is easy to estimate the Euclidean distance so as to reject most of the unlikely codewords for a certain input vector (Guan, L and Kamel, M., 1992; Lec, C.H. and Chen, L H., 1994; Baek, S. et al., 1997; Pan, J.S. et al., 2003). Because it is very heavy to compute the variance of a k-dimensional vector online, a new feature, which is based on the variances of two subvectors, is constructed to estimate the Euclidean distance. Meanwhile, a modified more memory-efficient data structure is proposed for storing all features of a vector to reduce extra memory requirement compared to the latest previous work (Pan, J.S. et al., 2003). Experimental results confirmed that the proposed method is more search efficient.
Date of Conference: 18-19 Nov. 2004