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Estimating Color Signal at Different Correlated Color Temperature of Daylight

机译:在日光的不同相关色温下估计色彩信号

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Color signal changes with change in illuminant information. This study focuses on estimating color signals at different Correlated Color Temperature (CCT) of daylight. We selected a set of color signals at different CCT of daylight for estimation. An experiment was conducted by generating color signals from 24 color samples of Macbeth Color Checker and 1645 daylight spectral power distributions (SPD). where CCT ranges from 3757K to 28322K. By uniform sampling of this, we collected 84 color signals from each color samples and combined them to form a training dataset. Principal Component Analysis (PCA) has been applied on the selected training dataset to find the basis vectors and the number of color signals needed for estimation. We apply the Wiener estimation with different order of polynomials to estimate the color signal of color samples. Interestingly, good estimation of all 1645 color signals of given color sample from Macbeth color chart, is obtained by selecting five best CCT color signals of that given color sample and with association to its third order polynomial. However, the results from high order polynomials yield to significant errors on Wiener estimation.
机译:颜色信号随光源信息的变化而变化。这项研究着重于估计不同日光相关色温(CCT)下的颜色信号。我们选择了在不同日光色温下的一组颜色信号进行估计。通过从24个Macbeth Color Checker颜色样本和1645日光光谱功率分布(SPD)生成颜色信号来进行实验。其中CCT的范围是3757K至28322K。通过对此进行统一采样,我们从每个颜色样本中收集了84个颜色信号,并将它们组合以形成训练数据集。主成分分析(PCA)已应用于选定的训练数据集,以查找估计所需的基向量和颜色信号的数量。我们将维纳估计与多项式的不同阶数一起用于估计颜色样本的颜色信号。有趣的是,通过选择给定颜色样本的五个最佳CCT颜色信号并关联其三阶多项式,可以从Macbeth色表中对给定颜色样本的所有1645个颜色信号进行良好估计。但是,高阶多项式的结果在Wiener估计中产生重大误差。

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