The work aims to study the color separation mechanism of XGamut software, construct a color separation prediction model to verify the correctness of the color separation mechanism, and evaluate the output effect of multi-channel high-fidelity color separation compared with the traditional four-color separation. With CGS's XGamut software as the object, the color separation mechanism was studied through software testing and experimental analysis. A color separation prediction model was developed using the random forest algorithm, with data from the Pantone color library divided into training and test sets to validate the predictive performance of the model. With the spectral data of the Pantone V3 color library as the reference, seven-color and four-color separations were generated using XGamut and ColorTuner, respectively, followed by proofing experiments. The outputs of the two separation methods were quantitatively evaluated using color difference (dE2000), metamerism index (MI), and root mean square error (RMSE). The results showed that the proposed prediction model can accurately predict the separation results of XGamut, thereby verifying the correctness of its separation mechanism. The proofing results further indicate that multichannel color separation outperforms traditional four-color separation. In addition, this study elucidates the high-fidelity color separation logic of XGamut and verifies the correctness of the color separation mechanism through the color separation prediction model. The proofing results demonstrate that seven-color separation provides a wider gamut and stronger spot-color reproduction capability than conventional four-color separation.
Key words
high-fidelity color separation /
XGamut /
Metamerism /
color management /
random forest (RF)
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