3 February 2014 Perceptual metrics and visualization tools for evaluation of page uniformity
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Abstract
Uniformity is one of the issues of most critical concern for laser electrophotographic (EP) printers. Typically, full coverage constant-tint test pages are printed to assess uniformity. Exemplary nonuniformity defects include mottle, grain, pinholes, and “finger prints". It is a real challenge to make an overall Print Quality (PQ) assessment due to the large coverage of a letter-size, constant-tint printed test page and the variety of possible nonuniformity defects. In this paper, we propose a novel method that uses a block-based technique to analyze the page both visually and metrically. We use a grid of 150 pixels × 150 pixels ( ¼ inch × ¼ inch at 600-dpi resolution) square blocks throughout the scanned page. For each block, we examine two aspects: behavior of its pixels within the block (metrics of graininess) and behavior of the blocks within the printed page (metrics of nonuniformity). Both ΔE (CIE 1976) and the L* lightness channel are employed. For an input scanned page, we create eight visual outputs, each displaying a different aspect of nonuniformity. To apply machine learning, we train scanned pages of different 100% solid colors separately with the support vector machine (SVM) algorithm. We use two metrics as features for the SVM: average dispersion of page lightness and standard deviation in dispersion of page lightness. Our results show that we can predict, with 83% to 90% accuracy, the assignment by a print quality expert of one of two grades of uniformity in the print.
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Minh Q. Nguyen, Renee Jessome, Steve Astling, Eric Maggard, Terry Nelson, Mark Shaw, Jan P. Allebach, "Perceptual metrics and visualization tools for evaluation of page uniformity", Proc. SPIE 9016, Image Quality and System Performance XI, 901608 (3 February 2014); doi: 10.1117/12.2038752; https://doi.org/10.1117/12.2038752
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