Partial distance search (PDS) is a method of reducing the amount of computation required for vector quantization encoding. The method is simple and general enough to be incorporated into many fast encoding algorithms....
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Partial distance search (PDS) is a method of reducing the amount of computation required for vector quantization encoding. The method is simple and general enough to be incorporated into many fast encoding algorithms. This paper describes a simple improvement to PDS based on principal components analysis (PCB), which rotates the codebook without altering the interpoint distances. Like PDS, this new method fan be used to improve many fast encoding algorithms. The algorithm decreases the decoding time of PDS by as much as 44 %, and decreases the decoding time of k-d trees by as much as 66% on common vectorquantization benchmarks.
A practical high-throughput architecture and its implementation for real-time coding of television-quality signals are presented. The architecture is directed toward the implementation of multistage vector quantizatio...
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A practical high-throughput architecture and its implementation for real-time coding of television-quality signals are presented. The architecture is directed toward the implementation of multistage vectorquantization (VQ), as the authors\' simulation results show that the latter is more suitable for real-time coding. However, the implementation is suitable for both single-stage and multistage VQ. The functional blocks of the VQ encoder system have been designed and implemented in VLSI technology. The VQ encoding scheme designed has an encoding delay of 25 clock cycles and is independent of the codebook size.
A new fast nearest-neighbor algorithm is described that uses principal component analysis to build an efficient search tree. At each node in the tree, the data set is partitioned along the direction of maximum varianc...
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A new fast nearest-neighbor algorithm is described that uses principal component analysis to build an efficient search tree. At each node in the tree, the data set is partitioned along the direction of maximum variance. The search algorithm efficiently uses a depth-first search and a new elimination criterion. The new algorithm was compared to 16 other fast nearest-neighbor algorithms on three types of common benchmark data sets including problems from time series prediction and image vectorquantization. This comparative study illustrates the strengths and weaknesses of all of the leading algorithms. The new algorithm performed very well on all of the data sets and was consistently ranked among the top three algorithms.
Traditional fast k-nearest neighbor search algorithms based on pyramid structures need either many extra memories or long search time. This paper proposes a fast k-nearest neighbor search algorithm based on the wavele...
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Traditional fast k-nearest neighbor search algorithms based on pyramid structures need either many extra memories or long search time. This paper proposes a fast k-nearest neighbor search algorithm based on the wavelet transform, which exploits the important information hiding in the transform coefficients to reduce the computational complexity. The study indicates that the Haar wavelet transform brings two kinds of important pyramids. Two elimination criteria derived from the transform coefficients are used to reject those impossible candidates. Experimental results on texture classification verify the effectiveness of the proposed algorithm. (C) 2009 Elsevier Inc. All rights reserved.
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