IMAGE COMPRESSION USING SELF-ORGANIZATION NETWORKS

被引：33

作者：

CHEN, OTC ^{[1
]}

SHEU, BJ ^{[1
]}

FANG, WC ^{[1
]}

机构：

[1] CALTECH, JET PROPULS LAB, PASADENA, CA 91109 USA

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY | 1994年 / 4卷 / 05期

关键词：

IMAGE PROCESSING; VECTOR QUANTIZATION; NEURAL NETWORK;

D O I：

10.1109/76.322995

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：

A self-organization neural network architecture is used to implement vector quantization for image compression. A modified self-organization algorithm, which is based on the frequency-sensitive cost function and centroid learning rule, is utilized to construct the codebooks. Performances of this frequency-sensitive self-organization network and a conventional algorithm for vector quantization are compared. The proposed method is quite efficient and can achieve near-optimal results. Good adaptivity for different statistics of source data can also be achieved.

引用

页码：480 / 489

页数：10

共 20 条

[1]

[Anonymous], 1992, SPRINGER INT

[2]

CHEN OTC, 1992, IEEE INT C ACOUSTICS, V2, P385

[3]

DESIENO D, 1988, P IEEE INT C NEURAL, V1, P117

[4] ON THE STRUCTURE OF VECTOR QUANTIZERS [J].