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- W1929415759 abstract "Central to vector quantization is the design of optimal code book. The construction of a globally optimal code book has been shown to be NP-complete. However, if the partition halfplanes are restricted to be orthogonal to the principal direction of the training vectors, then the globally optimal K-partition of a set of N D-dimensional data points can be computed in O((N+KM/sup 2/)D) time by dynamic programming, where M is the intensity resolution. This constrained optimization strategy improves the performance of vector quantizer over the classic LBG algorithm and the popular methods of tree-structured recursive greedy bipartition of the training data set. >" @default.
- W1929415759 created "2016-06-24" @default.
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- W1929415759 date "2003-01-02" @default.
- W1929415759 modified "2023-10-11" @default.
- W1929415759 title "Vector quantizer design by constrained global optimization" @default.
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- W1929415759 doi "https://doi.org/10.1109/dcc.1992.227468" @default.
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