Improved training-set distribution model for the training of BP neural networks in CRT color conversion

Ningfang Liao; Junsheng Shi; Weiping Yang

doi:10.1117/12.483132

13 September 2002 Improved training-set distribution model for the training of BP neural networks in CRT color conversion

Ningfang Liao, Junsheng Shi, Weiping Yang

Author Affiliations +

Proceedings Volume 4922, Color Science and Imaging Technologies; (2002) https://doi.org/10.1117/12.483132
Event: Photonics Asia, 2002, Shanghai, China

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).

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Ningfang Liao, Junsheng Shi, and Weiping Yang "Improved training-set distribution model for the training of BP neural networks in CRT color conversion", Proc. SPIE 4922, Color Science and Imaging Technologies, (13 September 2002); https://doi.org/10.1117/12.483132

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