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Proceedings Paper

Improved training-set distribution model for the training of BP neural networks in CRT color conversion
Author(s): Ningfang Liao; Junsheng Shi; Weiping Yang
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Paper Abstract

For the training of the BP neural networks in CRT color conversion, some papers suggest using a uniformly distributed RGB training set model (URGB). However, this URGB model is single-directional. Therefore, when the number of the samples in a training set is under a certain amount, such as less than 51 2 (8 X 8 X 8), a URGB model may cause big prediction errors, especially in the backward conversion (XYZ to RGB). In this paper, we propose an improved training set model, with which a smaller training set can be drawn from a virtual URGB set. Our experimental results show that, an improved training set model can achieve a desired prediction accuracy in the whole CRT color space, even if the samples number in a training set is less than 512(8 X 8 X 8).

Paper Details

Date Published: 13 September 2002
PDF: 4 pages
Proc. SPIE 4922, Color Science and Imaging Technologies, (13 September 2002); doi: 10.1117/12.483132
Show Author Affiliations
Ningfang Liao, Beijing Institute of Technology (China)
Junsheng Shi, Beijing Institute of Technology (China)
Weiping Yang, Beijing Institute of Technology (China)

Published in SPIE Proceedings Vol. 4922:
Color Science and Imaging Technologies
Dazun Zhao; Ming Ronnier Luo; Kiyoharu Aizawa, Editor(s)

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