Abstract
In order to speed up the seazch process of vector quantization (VQ), it is most important to avoid actually computing k-dimensional Euclidean distance as many as possible. In order to fmd abest-matched codeword (winner) in the codebook for a certain input vector, it is a general way to roughly estimate other than exactly compute Euclidean distance immediately for the purpose of rejecting a candidate codeword. The lower dimensional features of a vector such as sum or the mean (L1-type norm) and L2 norm are widely used for this purpose. Obviously, how to construct a suitable feature is a core problem for estimating Euclidean distance. In this paper, an efficient method of constructing L1-type norm feature is proposed by introducing a reference vector. In addition, the criterion on how to select an optimal reference vector is also given. Experimental results confirmed the effectiveness of the proposed method.
Original language | English |
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Pages (from-to) | 269-274 |
Number of pages | 6 |
Journal | Intelligent Automation and Soft Computing |
Volume | 12 |
Issue number | 3 |
DOIs | |
Publication status | Published - 2006 Jan |
Keywords
- Estimation
- Euclidean distance
- Fast encoding
- L-type norm feature
- Vector quantization
ASJC Scopus subject areas
- Software
- Theoretical Computer Science
- Computational Theory and Mathematics
- Artificial Intelligence