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Mar 1, 2013 · Vector quantization (VQ) is a well-known method for image compression but its encoding process is very heavy computationally.
In order to speed up VQ encoding, it is most important to avoid unnecessary Euclidean distance computations (k-D) as much as possible by a lighter (no ...
Sum difference, partial sum difference and Manhattan distance are computed as the multi estimations of. Euclidean distance and they are connected to each other ...
Zhibin Pan, Tadahiro Ohmi, Koji Kotani: A Fast Encoding Method for Vector Quantization by using Multi Euclidean Distance Estimations. Intell. Autom.
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 ...
Oct 22, 2024 · This paper proposed a fast vector quantization encoding algorithm called difference pyramid search (DPS). According to the formation of the ...
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In order to speed up the VQ encoding process, it is beneficial to firstly estimate how large the Euclidean distance is between the input vector and a candidate ...
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Sep 22, 2003 · A number of fast encoding algorithms (see Reference section) of VQ based on the Euclidean distance have been developed. Partial Distance ...
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They represent a vector by a short code composed of a number of sub- space quantization indices. They efficiently estimate the euclidean distance between two ...
The encoding process of image vector quantization (VQ) is very heavy due to it performing a lot of k-dimensional. Euclidean distance computations.
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