Relief printing technology developed by Océ allows the superposition of several layers of colorant on different types of
media which creates a variation of the surface height defined by the input to the printer. Evaluating the reproduction
accuracy of distinct surface characteristics is of great importance to the application of the relief printing system. Therefore,
it is necessary to develop quality metrics to evaluate the relief process. In this paper, we focus on the third dimension of
relief printing, i.e. height information. To achieve this goal, we define metrics and develop models that aim to evaluate relief
prints in two aspects: overall fidelity and surface finish. To characterize the overall fidelity, three metrics are calculated:
Modulation Transfer Function (MTF), difference and root-mean-squared error (RMSE) between the input height map and
scanned height map, and print surface angle accuracy. For the surface finish property, we measure the surface roughness,
generate surface normal maps and develop a light reflection model that serves as a simulation of the differences between
ideal prints and real prints that may be perceived by human observers. Three sets of test targets are designed and printed by
the Océ relief printer prototypes for the calculation of the above metrics: (i) twisted target, (ii) sinusoidal wave target, and
(iii) ramp target. The results provide quantitative evaluations of the printing quality in the third dimension, and demonstrate
that the height of relief prints is reproduced accurately with respect to the input design. The factors that affect the printing
quality include: printing direction, frequency and amplitude of the input signal, shape of relief prints. Besides the above
factors, there are two additional aspects that influence the viewing experience of relief prints: lighting condition and
viewing angle.
Wavelets are a powerful tool that can be applied to problems in image processing and analysis. They provide a multi-scale
decomposition of an original image into average terms and detail terms that capture the characteristics of the image at
different scales. In this project, we develop a figure of merit for macro-uniformity that is based on wavelets. We use the
Haar basis to decompose the image of the scanned page into eleven levels. Starting from the lowest frequency level, we
group the eleven levels into three non-overlapping separate frequency bands, each containing three levels. Each frequency
band image consists of the superposition of the detail images within that band. We next compute 1-D horizontal and
vertical projections for each frequency band image. For each frequency band image projection, we develop a structural
approximation that summarizes the essential visual characteristics of that projection. For the coarsest band comprising
levels 9,10,11, we use a generalized square-wave approximation. For the next coarsest band comprising levels 6,7,8, we
use a piecewise linear spline approximation. For the finest bands comprising levels 3,4,5, we use a spectral decomposition.
For each 1-D approximation signal, we define an appropriate set of scalar-valued features. These features are used to
design two predictors one based on linear regression and the other based on the support vector machine, which are trained
with data from our image quality ruler experiments with human subjects.
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