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IFS Optimization Using Discrete Parameter Pools

Hiroyuki HONDA, Miki HASEYAMA, Hideo KITAJIMA

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Summary :

This paper proposes an Iterated Function System (IFS) which can reduce effects of quantization errors of the IFS parameters. The proposed method skips conventional analog-parameter search and directly selects optimum IFS parameters from pools of discrete IFS parameters. In conventional IFS-based image coding the IFS parameters are quantized after their analog optimum values are determined. The image reconstructed from the quantized parameters is degraded with errors that are traced back to quantization errors amplified in the iterated mappings. The effectiveness of this new realistic approach is demonstrated by simulation results over the conventional method.

Publication
IEICE TRANSACTIONS on Information Vol.E83-D No.2 pp.233-241
Publication Date
2000/02/25
Publicized
Online ISSN
DOI
Type of Manuscript
PAPER
Category
Image Processing, Image Pattern Recognition

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