Quantification of perceived macro-uniformity

Ki-Youn Lee; Yousun Bang; Heui-Keun Choh

doi:10.1117/12.877633

24 January 2011 Quantification of perceived macro-uniformity

Ki-Youn Lee, Yousun Bang, Heui-Keun Choh

Proceedings Volume 7867, Image Quality and System Performance VIII; 786704 (2011) https://doi.org/10.1117/12.877633
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

Macro-uniformity refers to the subjective impression of overall uniformity in the print sample. By the efforts of INCITS W1.1 team, macro-uniformity is categorized into five types of attributes: banding, streaks, mottle, gradients, and moiré patterns, and the ruler samples are generated with perceptual scales. W1.1 macro-uniformity ruler is useful for judging the levels of print defect, but it is not an easy task to reproduce the samples having the same perceptual scales at different times in different places. An objective quantification method is more helpful and convenient for developers to analyze print quality and design printing system components. In this paper, we propose a method for measuring perceived macro-uniformity for a given print using a flat-bed scanner. First, banding, 2D noise, and gradients are separately measured, and they are converted to the perceptual scales based on subjective results of each attribute. The correlation coefficients between the measured values of the attributes and the perceptual scales are 0.92, 0.97, and 0.86, respectively. Another subjective test is performed to find the relationship between the overall macro-uniformity and the three attributes. The weighting factors are obtained by the experimental result, and the final macro-uniformity grade is determined by the weighted sums of each attribute.

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Ki-Youn Lee, Yousun Bang, and Heui-Keun Choh "Quantification of perceived macro-uniformity", Proc. SPIE 7867, Image Quality and System Performance VIII, 786704 (24 January 2011); https://doi.org/10.1117/12.877633

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KEYWORDS

Printing

Scanners

Image filtering

Linear filtering

Visualization

Calibration

Smoothing

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