Equal-average hyperplane partitioning method for vector quantization of image data
Pattern Recognition Letters
A fast full search equivalent encoding method for vector quantization by using appropriate features
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 1
Application of Principal Component Analysis to Multikey Searching
IEEE Transactions on Software Engineering
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The encoding process of vector quantization (VQ) is computationally very expensive due to a lot of k-dimensional Euclidean distance computations. In order to speed up VQ's encoding process, it is very effective to use a computationally inexpensive distance estimation first to try to reject a candidate codeword instead of an immediate actual distance computation. If a successful rejection is achieved, the computational burden can be reduced because the actual distance computation becomes unnecessary. A very search-efficient VQ encoding method by using multiple projection axes has already been developed in the previous work, which is a general version of the central axis. In this paper, a further generalized version of this previous work is proposed, which can completely remove the constraints for selecting the projection axis in a diagonally symmetric way as required by the previous work. Meanwhile, a theoretical criterion of how to select an optimal projection axis for a candidate codeword is also given. Furthermore, in order to use the generalized multi projection axes simultaneously, the energy accumulation property in an orthogonal space is integrated. Experimental results confirmed the effectiveness of the proposed method.