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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

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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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Document Type: Research Article

Affiliations: 1: School of Industrial Engineering, Purdue University, West Lafayette, Indiana 47907 2: School of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47907 3: School of Electrical and Computer Engineering, Purdue University, West Lafayette, Indiana 47907

Publication date: 2010-09-01

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  • The Journal of Imaging Science and Technology (JIST) is dedicated to the advancement of imaging science knowledge, the practical applications of such knowledge, and how imaging science relates to other fields of study. The pages of this journal are open to reports of new theoretical or experimental results, and to comprehensive reviews. Only original manuscripts that have not been previously published, nor currently submitted for publication elsewhere, should be submitted.

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    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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