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Proceedings Paper

Block-predictive image coder of neural network in multiresolution domain
Author(s): ShengQiang Lou; HuangPu Ku; Liangzhu Zhou; Jiangwei Wang; Guangming Xu
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Paper Abstract

The redundancy of the multiresolution representation has been clearly demonstrated in the case of fractal images, but has not been fully recognized and exploited for general images. This paper presents a new image coder in which the similarity among blocks of different subbands is exploited by block prediction based on neural network. After a pyramid subband decomposition, the detail subbands are partitioned into a set of uniform non-overlapping blocks. In order to speed up the coding procedure and improve the coding efficiency, a new classifying criteria is presented, the blocks are classified into two sets: the simple block set and the edge block set. In our proposed method, the edge blocks are predicted from blocks in lower scale subband with same orientation through neural network. The simple blocks and predictive edge error blocks are coded with an arithmetic coder. Simulation results show that the method presented in this paper is a promising coding technique which is worth further research.

Paper Details

Date Published: 1 April 1997
PDF: 6 pages
Proc. SPIE 3030, Applications of Artificial Neural Networks in Image Processing II, (1 April 1997); doi: 10.1117/12.269780
Show Author Affiliations
ShengQiang Lou, National Univ. of Defense Technology (China)
HuangPu Ku, National Univ. of Defense Technology (China)
Liangzhu Zhou, National Univ. of Defense Technology (China)
Jiangwei Wang, National Univ. of Defense Technology (China)
Guangming Xu, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 3030:
Applications of Artificial Neural Networks in Image Processing II
Nasser M. Nasrabadi; Aggelos K. Katsaggelos, Editor(s)

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