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A Novel Competitive Learning Technique for the Design of Variable-Rate Vector Quantizers with Reproduction Vector Training in the Wavelet Domain

Wen-Jyi HWANG, Maw-Rong LEOU, Shih-Chiang LIAO, Chienmin OU

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Summary :

This paper presents a novel competitive learning algorithm for the design of variable-rate vector quantizers (VQs). The algorithm, termed variable-rate competitive learning (VRCL) algorithm, designs a VQ having minimum average distortion subject to a rate constraint. The VRCL performs the weight vector training in the wavelet domain so that required training time is short. In addition, the algorithm enjoys a better rate-distortion performance than that of other existing VQ design algorithms and competitive learning algorithms. The learning algorithm is also more insensitive to the selection of initial codewords as compared with existing design algorithms. Therefore, the VRCL algorithm can be an effective alternative to the existing variable-rate VQ design algorithms for the applications of signal compression.

Publication
IEICE TRANSACTIONS on Information Vol.E83-D No.9 pp.1781-1789
Publication Date
2000/09/25
Publicized
Online ISSN
DOI
Type of Manuscript
PAPER
Category
Image Processing, Image Pattern Recognition

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