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Proceedings Paper

Edge-preserving vector quantization using a neural network
Author(s): Xujun Ye; Zhineng Li
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Paper Abstract

Recently the vector quantization (VQ) has received considerable interests as a powerful image data compression technique. However, studies of image coding with VQ have revealed that VQ for image compression suffers from edge degradation in the reproduced images. In this paper, we describe an adaptive learning method of the edge preserving VQ based on Kohonen's self-organizing feature map neural network. The learning procedure is performed by extracting the edge of the whole image, then adaptively adjusting the learning rate that are determined by the edge information of the image block. Compared with direct image VQ coding, the experiment results show the reproduced images quality are well improved, at the same compression ratio.

Paper Details

Date Published: 30 September 1996
PDF: 7 pages
Proc. SPIE 2898, Electronic Imaging and Multimedia Systems, (30 September 1996); doi: 10.1117/12.253397
Show Author Affiliations
Xujun Ye, Zhejiang Univ. (China)
Zhineng Li, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 2898:
Electronic Imaging and Multimedia Systems
Chung-Sheng Li; Robert L. Stevenson; LiWei Zhou, Editor(s)

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