## Abstract

The encoding process of vector quantization (VQ) is a time bottleneck preventing its practical applications. In order to speed up VQ encoding, it is very effective to use lower dimensional features of a vector to estimate how large the Euclidean distance between the input vector and a candidate codeword could be so as to reject most unlikely codewords. The three popular statistical features of the average or the mean, the variance, and L_{2} norm of a vector have already been adopted in the previous works individually. Recently, these three statistical features were combined together to derive a sequential EEENNS search method in [6], which is very efficient but still has obvious computational redundancy. This Letter aims at giving a mathematical analysis on the results of EEENNS method further and pointing out that it is actually unnecessary to use L_{2} norm feature anymore in fast VQ encoding if the mean and the variance are used simultaneously as proposed in IEENNS method. In other words, L_{2} norm feature is redundant for a rejection test in fast VQ encoding. Experimental results demonstrated an approximate 10-20% reduction of the total computational cost for various detailed images in the case of not using L_{2} norm feature so that it confirmed the correctness of the mathematical analysis.

Original language | English |
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Pages (from-to) | 2218-2222 |

Number of pages | 5 |

Journal | IEICE Transactions on Information and Systems |

Volume | E88-D |

Issue number | 9 |

DOIs | |

Publication status | Published - 2005 Sep |

## Keywords

- EEENNS method
- Encoding performance
- Fast search
- IEENNS method
- Statistical features
- Vector quantization

## ASJC Scopus subject areas

- Software
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
- Artificial Intelligence