Image compression by self-organized Kohonen map

被引：83

作者：

Amerijckx, C ^{[1
]}

Verleysen, M

Thissen, P

Legat, JD

机构：

[1] Univ Catholique Louvain, Microelect Lab, B-1348 Louvain, Belgium

[2] VLSI Technol, Sophia Antipolis, France

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 1998年 / 9卷 / 03期

关键词：

discrete cosine transforms; entropy coder; image processing; JPEG; self-organizing feature maps; variable length codes; vector quantization;

D O I：

10.1109/72.668891

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a compression scheme for digital still images, by using the Kohonen's neural network algorithm, not only for its vector quantization feature, but also for its topological property. This property allows an increase of about 80% for the compression rate. Compared to the JPEG standard, this compression scheme shows better performances (in terms of PSNR) for compression rates higher than 30.

引用

页码：503 / 507

页数：5

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