Improving Tone Prediction in Calibration of Electrophotographic Printers by Linear Regression: Environmental, Consumables and Tone-Level Factors

Yang, Chao-Lung; Kuo, Yan-Fu; Yih, Yuehwern; Chiu, George T.‐C.; Abramsohn, D. A.; Ashton, Gunnar; Allebach, Jan P.

doi:10.2352/j.imagingsci.technol.2010.54.5.050301

Cited by 4 publications

(2 citation statements)

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“…The calibration models generated by the proposed PCR methods, also referred to in this study as PCR models, are compared with the models generated by ordinary leastsquares regression methods, referred to as OLSR models, proposed by Yang et al 8 In each of the OLSR models, a single sensor measurement w j and selected disturbances d ij are used as explanatory variables to predict the tone value y j at each half-tone level, i.e.,…”

Section: Model Comparisonmentioning

confidence: 99%

“…A calibration model G can be written as y = G͑w , d͒, where y is tone values on paper, w is sensor measurements from on-board densitometers, and d is uncontrollable but measurable factors/disturbances collected in life test, such as temperature, humidity, and throughput. 8 The tone values y are the measured reflectances of the reproduced color patches printed at the designated half-tone levels. In this study, a tone value is defined as the Euclidian distance ͑⌬E͒ in CIE L ‫ء‬ a ‫ء‬ b ‫ء‬ space 9 between the color point of a primary color printed at a particular half-tone level and the substrate color.…”

Section: Calibration Modelmentioning

confidence: 99%

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Improving Tone Prediction in Calibration of Electrophotographic Printers by Linear Regression: Using Principal Components to Account for Co-Linearity of Sensor Measurements

Kuo¹,

Chiu²,

Yang³

et al. 2010

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This work employs principal component regression (PCR) to improve tone prediction accuracy for color electrophotography (EP). During calibration, primary color patches at different half-tone levels are printed on a belt and measured using on-board sensors. Regression models are developed to predict primary color tone values on output media from these on-board sensor measurements. The prediction accuracy of the regression models directly impacts the quality and consistency of color reproduction. Analyses have revealed a high degree of correlation among the on-board sensor measurements of the calibration patches from the same primary color. This indicates that multiple on-board sensor measurements are linearly correlated and using multiple on-board sensor measurements to predict the tone value may improve prediction accuracy if the collinearity of the measurements is taken into consideration. In this study, a PCR-based approach is applied to handle the multicollinear measurements in estimating the regression model coefficients. Experimental results show the proposed PCR models reduce root-mean-squared error by 24.7% over ordinary least-squares regression models.

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Section: Model Comparisonmentioning

confidence: 99%

Section: Calibration Modelmentioning

confidence: 99%