Development of a softcopy environment for banding visibility assessment experiments and identification of inkjet printers for forensic applications
Abstract
In the first chapter of this report, we present new techniques for calibrating CRT monitors which improve the calibration efficiency significantly. We present a simple method to adjust the white point of the monitor accurately. We propose to use two separate tone curves to increase the accuracy of calibration. To increase the accuracy of linear stage of calibration, we optimize the linear transformation matrix in the uniform Lab space. Finally we develop a search algorithm to achieve very high accuracy calibration for experiments where a limited number of colors have to be displayed. In the second chapter, we present the development of a softcopy environment to conduct various experiments for investigating the visibility of banding. This environment includes the methodology to duplicate the print on the monitor, and a banding extraction technique. We validated the accuracy of this methodology by conducting a banding matching experiment. We used this platform to conduct banding visibility assessment experiments. One of them was a banding discrimination experiment. The results showed that for the printers investigated, a reduction of 6.5% in the banding magnitude will be just noticeable by an average observer. We were also able to find the detection thresholds of banding in grayscale images for three laser EP printers. Finally, we were able to compare the banding visibility of different printers quantitatively by conducting a cross-platform experiment. This methodology can form the basis for a metric for visibility of banding. In the third chapter, we present methodologies for identification of Inkjet printers for forensic applications. We investigate different texture features of the characters printed by inkjet printers for classification based on text-only documents. We check the in-model stability of these features by using a cost function. Finally we perform stepwise discriminant analysis to reduce the feature set.
Degree
Ph.D.
Advisors
Allebach, Purdue University.
Subject Area
Electrical engineering
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