TY - GEN
T1 - Fast search method for image vector quantization based on equal-average equal-variance and partial sum concept
AU - Pan, Zhibin
AU - Kotani, Koji
AU - Ohmi, Tadahiro
PY - 2005
Y1 - 2005
N2 - The encoding process of image vector quantization (VQ) is very heavy due to it performing a lot of k-dimensional Euclidean distance computations. In order to speed up VQ encoding, it is most important to avoid unnecessary exact Euclidean distance computations as many as possible by using features of a vector to estimate how large it is first so as to reject most of unlikely codewords. The mean, the variance, L2 norm and partial sum of a vector have been proposed as effective features in previous works for fast VQ encoding. Recently, in the previous work [6], three features of the mean, the variance and L2 norm are used together to derive an EEENNS search method, which is very search efficient but still has obvious computational redundancy. This paper aims at modifying the results of EEENNS method further by introducing another feature of partial sum to replace L2 norm feature so as to reduce more search space. Mathematical analysis and experimental results confirmed that the proposed method is more search efficient compared to [6].
AB - The encoding process of image vector quantization (VQ) is very heavy due to it performing a lot of k-dimensional Euclidean distance computations. In order to speed up VQ encoding, it is most important to avoid unnecessary exact Euclidean distance computations as many as possible by using features of a vector to estimate how large it is first so as to reject most of unlikely codewords. The mean, the variance, L2 norm and partial sum of a vector have been proposed as effective features in previous works for fast VQ encoding. Recently, in the previous work [6], three features of the mean, the variance and L2 norm are used together to derive an EEENNS search method, which is very search efficient but still has obvious computational redundancy. This paper aims at modifying the results of EEENNS method further by introducing another feature of partial sum to replace L2 norm feature so as to reduce more search space. Mathematical analysis and experimental results confirmed that the proposed method is more search efficient compared to [6].
UR - http://www.scopus.com/inward/record.url?scp=33750548605&partnerID=8YFLogxK
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U2 - 10.1109/ICME.2005.1521702
DO - 10.1109/ICME.2005.1521702
M3 - Conference contribution
AN - SCOPUS:33750548605
SN - 0780393325
SN - 9780780393325
T3 - IEEE International Conference on Multimedia and Expo, ICME 2005
SP - 1440
EP - 1443
BT - IEEE International Conference on Multimedia and Expo, ICME 2005
T2 - IEEE International Conference on Multimedia and Expo, ICME 2005
Y2 - 6 July 2005 through 8 July 2005
ER -