Paper
10 November 2020 Optimal color selection for root-polynomial color correction
Long Ma, Xue Liu
Author Affiliations +
Proceedings Volume 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence; 115840D (2020) https://doi.org/10.1117/12.2579640
Event: Third International Conference on Image, Video Processing and Artificial Intelligence, 2020, Shanghai, China
Abstract
The characterization of a set of target sample can be obtained from a specific sample set. The precision of the model largely depends on the number of the characterization samples and the number of colors. In this paper, a sample selection method combining the third-order root polynomial color correction(RPCC) model is proposed. This method is named Maxmingfc, which is based on the Maxminc method proposed by Cheung et al. And takes GFC evaluation criteria as the criteria for selecting differences between samples, it can use fewer samples to obtain higher characterization model precision. This sample selection method is used to obtain characteristic models under different cameras for different training sample sets and test sample sets, and color difference is used for evaluation. Compared with Hardeberg, Maxminc, Maxmins, Maxsums and Maxsumc methods, the sample selection method of Maxmingfc can obtain the more accurate characterization model, which is superior to other existing sample selection methods.
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Long Ma and Xue Liu "Optimal color selection for root-polynomial color correction", Proc. SPIE 11584, 2020 International Conference on Image, Video Processing and Artificial Intelligence, 115840D (10 November 2020); https://doi.org/10.1117/12.2579640
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KEYWORDS
RGB color model

Cameras

Colorimetry

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