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Variation and Calibration Error in Electronic Imaging

In acquiring digital images, electronic systems not only detect optical signals but also convert them into a digital form for further image processing and exchange. Practical systems can introduce error during color calibration, and when acquiring image scene information. For large populations it is often assumed that the error can be modeled as a random variable having a zero mean. In the case of a single color instrument, camera or scanner, however, error due to deterioration of a physical standard, optical filter or detector will introduce a bias into the measurement or image data. This error is modified as the signals are transformed (processed) into their final form. An error-propagation method is shown to describe the influence of the dataprocessing path on the magnitude of bias error. This approach is related to the propagation of image noise, or variance. The analysis is applied in examples drawn from color-measurement and digital image processing.