VECTOR QUANTIZATION USING TREE-STRUCTURED SELF-ORGANIZING FEATURE MAPS

被引：9
|
作者：
CHIUEH, TD
TANG, TT
CHEN, LG
机构：
[1] Department of Electrical Engineering, National Taiwan University, Taipei
来源：
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS | 1994年 / 12卷 / 09期
关键词：
D O I：
10.1109/49.339928
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we propose a binary-tree structure neural network model suitable for structured clustering, During and after training, the centroids of the clusters in this model always form a binary tree in the input pattern space, This model is used to design tree search vector quantization codebooks for image coding, Simulation results show that the acquired codebook not only produces better-quality images but also achieves a higher compression ratio than conventional tree search vector quantization. When source coding is applied after VQ, the new model performs better than the generalized Lloyd algorithm in terms of distortion, bits per pixel, and encoding complexity for low-detail and medium-detail images.
引用
收藏
页码：1594 / 1599
页数：6
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