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Approximating the CIECAM02 color appearance model by means of neural networks

机译:通过神经网络逼近CIECAM02颜色外观模型

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摘要

An artificial neural network used to realize the approximating problem of the color appearance model (CAM) CIECAM02 in color management is demonstrated. GretagMacbeth ColorChecker Charts, which now are widely used in calibration of digital camera, are chosen as samples to implement the forward and reverse color appearance models. When the predictive results are evaluated, for forward model, the output color appearance space is converted to the uniform color space based on CAM and is evaluated, while for reverse model, because the prediction precision is insufficient, we try to convert the color appearance space, which is the cylinder space, to the cube space similar to the red, green, and blue (RGB) space, and the results show that the precision is obviously improved.
机译:演示了一种人工神经网络,用于实现颜色管理中的颜色外观模型(CAM)CIECAM02的近似问题。现在选择广泛用于数码相机校准的GretagMacbeth ColorChecker Charts作为示例,以实现正向和反向颜色外观模型。对预测结果进行评估时,对于正向模型,基于CAM将输出的颜色外观空间转换为均匀的颜色空间并进行评估,而对于反向模型,由于预测精度不足,我们尝试对颜色外观空间进行转换。 ,即圆柱空间,到类似于红色,绿色和蓝色(RGB)空间的立方体空间,结果表明精度明显提高。

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