基于最小二乘支持向量机的扫描仪特征化

Scanner Characterization Based on Least Squares Support Vector Machine

目的基于最小二乘支持向量机回归(LSSVR),研究扫描仪图像输入设备的特征化方法。方法以ColorChecker SG标准色卡为目标,通过最小二乘支持向量机建立RGB三通道值到CIE Lab色度值的非线性映射模型,采用基于交叉验证的网格搜索确定模型最优参数,优化LSSVR模型,实现彩色扫描仪的色度特征化。结果所建模型的训练集R-squared为0.996,验证集R-squared为0.998,训练集与验证集的CIEDE2000平均色差分别为1.1463,1.2754。结论 LSSVR模型能够较好地实现彩色扫描仪色度特征化,泛化能力较强,此模型可有效地提高彩色扫描仪特征化的精度且计算处理速度更快。

The work aims to study the characterization method of scanner image input device based on the least squares support vector machine regression(LSSVR).With the ColorChecker SG standard color card as the target,a nonlinear mapping model of RGB three-channel value to CIE Lab value was established with the least squares support vector machine.The cross-validation grid search was used to determine the optimal parameters of the model and the LSSVR model was optimized to achieve the chromaticity characterization of color scanner.The R-squared of the model's training set was 0.996,the R-squared of the validation set was 0.998,and the average color differences of CIEDE2000 of the training set and the validation set were 1.1463 and 1.2754,respectively.LSSVR model can better realize the chromaticity characterization of color scanners,and has strong generalization ability.It also can effectively improve the characterization accuracy and has a faster calculation processing speed.