A fast mean-distance-ordered partial codebook search algorithm for image vector quantization

A new fast search algorithm for vector quantization using the mean of image vectors is proposed. The codevectors are sorted according to their component means, and the search for the codevector having the minimum Euclidean-distance to a given input vector starts with the one having the minimum mean-...

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Bibliographic Details
Published in:IEEE transactions on circuits and systems. 2, Analog and digital signal processing Analog and digital signal processing, 1993-09, Vol.40 (9), p.576-579
Main Authors: Ra, S.-W., Kim, J.-K.
Format: Article
Language:English
Subjects:
Applied sciences
Bit rate
Circuit testing
Computational complexity
Computational modeling
Digital signal processing
Euclidean distance
Exact sciences and technology
Image processing
Information, signal and communications theory
Nearest neighbor searches
Signal processing
Signal processing algorithms
Statistics
Telecommunications and information theory
Vector quantization
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Summary:A new fast search algorithm for vector quantization using the mean of image vectors is proposed. The codevectors are sorted according to their component means, and the search for the codevector having the minimum Euclidean-distance to a given input vector starts with the one having the minimum mean-distance to it, making use of our observation that the two codevectors are close to each other in most real images. The search is then made to terminate as soon as a simple yet novel test reports that any remaining vector in the codebook should have a larger Euclidean distance. Simulations show that the number of calculations can be reduced to as low as a fourth the number achievable by an algorithm known as the partial distance method.< >
ISSN:1057-7130
1558-125X
DOI:10.1109/82.257335