In many color measurement applications, such as those for color calibration and profiling, "patch code" has been used
successfully for job identification and automation to reduce operator errors. A patch code is similar to a barcode, but is
intended primarily for use in measurement devices that cannot read barcodes due to limited spatial resolution, such as
spectrophotometers. There is an inherent tradeoff between decoding robustness and the number of code levels available
for encoding. Previous methods have attempted to address this tradeoff, but those solutions have been sub-optimal. In
this paper, we propose a method to design optimal patch codes via device characterization. The tradeoff between
decoding robustness and the number of available code levels is optimized in terms of printing and measurement efforts,
and decoding robustness against noises from the printing and measurement devices. Effort is drastically reduced relative
to previous methods because print-and-measure is minimized through modeling and the use of existing printer profiles.
Decoding robustness is improved by distributing the code levels in CIE Lab space rather than in CMYK space.
INCITS W1.1 is a project chartered to develop an appearance-based image quality standard. This paper summarizes the
work to date of the W1.1 Text and Line Quality ad hoc team, and describes the progress made in developing a Text
Quality test pattern and an analysis procedure based on experience with previous perceptual rating experiments.
Gamut mapping is a critical process in the output color management of a printer. In systems consisting of two or more
marking engines, these engines could in general have different output color gamuts. In special cases these gamuts can be
drastically different. When applying gamut mapping to a multi-engine printing system, which gamut to use is not
obvious. In the default case, i.e., if the multi-engine system is treated as a single engine with a single gamut, poor image
quality could result. In this paper, we propose gamut mapping strategies for multiple engine printing, which depend on
the relative importance of "color rendition" and "page-to-page color consistency" within a given document. In
particular, we propose a content-based gamut mapping, wherein the trade-offs between color rendition and page-to-page
color consistency can be automatically and dynamically made by segmenting the document and applying gamut mapping
according to the needs of the segmented components.
Mottle is a common defect in printing. Mottle evaluation is crucial in image quality assessment and system
diagnosis. In this paper, we present a new automatic mottle estimation method which improves the existing technologies
in two aspects. First, a modified mottle noise frequency range is proposed, which further separates the banding and
streak spectra from mottle spectrum. Second, a robust estimation algorithm is introduced. It is less sensitive to the
outliers that may appear in the measurement. These outliers include other defects within the mottle frequency range,
such as spots, or defects outside of mottle frequency range, but are strong enough that can not be completely eliminated
by normal spatial filtering.
The method of paired comparisons is often used in image quality evaluations. Psychometric scale values for quality
judgments are modeled using Thurstone's Law of Comparative Judgment in which distance in a psychometric scale
space is a function of the probability of preference. The transformation from psychometric space to probability is a
cumulative probability distribution.
The major drawback of a complete paired comparison experiment is that every treatment is compared to every other,
thus the number of comparisons grows quadratically. We ameliorate this difficulty by performing paired
comparisons in two stages, by precisely estimating anchors in the psychometric scale space which are spaced apart
to cover the range of scale values and comparing treatments against those anchors.
In this model, we employ a generalized linear model where the regression equation has a constant offset vector
determined by the anchors. The result of this formulation is a straightforward statistical model easily analyzed using
any modern statistics package. This enables model fitting and diagnostics.
This method was applied to overall preference evaluations of color pictorial hardcopy images. The results were
found to be compatible with complete paired comparison experiments, but with significantly less effort.
In September 2000, INCITS W1 (the U.S. representative of ISO/IEC JTC1/SC28, the standardization committee for office equipment) was chartered to develop an appearance-based image quality standard.(1),(2) The resulting W1.1 project is based on a proposal(3) that perceived image quality can be described by a small set of broad-based attributes. There are currently six ad hoc teams, each working towards the development of standards for evaluation of perceptual image quality of color printers for one or more of these image quality attributes. This paper summarizes the work in progress of the teams addressing the attributes of Macro-Uniformity, Colour Rendition, Gloss & Gloss Uniformity, Text & Line Quality and Effective Resolution.
In September 2000, INCITS W1 (the U.S. representative of ISO/IEC JTC1/SC28, the standardization committee for office equipment) was chartered to develop an appearance-based image quality standard.(1),(2) The resulting W1.1 project is based on a proposal(4) that perceived image quality can be described by a small set of broad-based attributes. There are currently five ad hoc teams, each working towards the development of standards for evaluation of perceptual image quality of color printers for one or more of these image quality attributes. This paper summarizes the work in progress of the teams addressing the attributes of Macro-Uniformity, Color Rendition, Text and Line Quality and Micro-Uniformity.
A small number of general visual attributes have been recognized as essential in describing image quality. These include micro-uniformity, macro-uniformity, colour rendition, text and line quality, gloss, sharpness, and spatial adjacency or temporal adjacency attributes. The multiple-part International Standard discussed here was initiated by the INCITS W1 committee on the standardization of office equipment to address the need for unambiguously documented procedures and methods, which are widely applicable over the multiple printing technologies employed in office applications, for the appearance-based evaluation of these visually significant image quality attributes of printed image quality. 1,2 The resulting proposed International Standard, for which ISO/IEC WD 19751-13 presents an overview and an outline of the overall procedure and common methods, is based on a proposal that was predicated on the idea that image quality could be described by a small set of broad-based attributes.4 Five ad hoc teams were established (now six since a sharpness team is in the process of being formed) to generate standards for one or more of these image quality attributes. Updates on the colour rendition, text and line quality, and gloss attributes are provided.
It is well-known that many sub-attributes of line quality contribute to the perception of the overall line quality. But the relative importance of these sub-attributes is not clear, nor is there a method available for combining them into one representative number for overall line quality. To address these issues, we have designed and conducted a series of psychophysical experiments, which explore the shape of the human visual transfer functions (VTF) relevant to the perception of three selected sub-attributes: lumpiness, waviness and raggedness. We found that human sensitivity to these sub-attributes can be represented by VTF’s of the same shape but with relative perception weighting factors of 6:4:3 respectively. Based on this, we have proposed an approach to assess overall line quality. In our method, we first pre-process the line image acquired and extract certain profiles relevant to line quality measurement. A set of corresponding VTF’s is then applied to these profiles to calculate the various sub-attributes. Finally, overall line quality is determined by the weighted combination of these individual sub-attributes. These preference weights (1:1:3 for lumpiness, waviness and raggedness respectively) are different from the perception weights mentioned earlier. Our preliminary results show that this measurement correlates well with human perception of overall line quality, for the sub-attributes studied.
The color rendition ad hoc team of INCITS W1.1 is working to address issues related to color and tone reproduction for printed output and its perceptual impact on color image quality. The scope of the work includes accuracy of specified colors with emphasis on memory colors, color gamut, and the effective use of tone levels, including issues related to contouring. The team has identified three sub-attributes of color rendition: (1) color quantization -- defined as the ability to merge colors where needed, (2) color scale -- defined as the ability to distinguish color where needed, and (3) color fidelity -- defined as the ability to match colors. Visual definitions and descriptions of how these sub-attributes are perceived have been developed. The team is presently defining measurement methods for these, with the first of the sub-attributes considered being color quantization. More recently, the problem of measuring color fidelity has been undertaken. This presentation will briefly review the definitions and appearance of the proposed sub-attributes. The remainder of the discussion will focus on the progress to date of developing test targets and associated measurement methods to quantify the color quantization and color fidelity sub-attributes.
This paper describes a regression model for predicting customer preference from objective image quality metrics for black and white printers, copiers and multifunction systems. In order to quantify customer preference for monochrome images the quantitative preference system was previously developed. Using this system, a preference survey with five different customer-type documents was used to obtain the preference data. Objective image quality metrics were obtained from a scanner-based measurement system. Using this regression model, typically 80% or more of the variation of the overall preference can be explained by six objective image quality metrics: Relative TRC Error; Mottle; Visual Noise; Visual Structure; Streaks and Bands; and Relative Dynamic Range Reduction. The results also provide the relative significance of these attributes for the different kinds of customer images.
This paper describes the status and technical progress of the INCITS W1.1 Text and Line Quality ad hoc team. The team has defined the scope of the work and developed several perceptual sub-attributes which comprise the Text Quality and Line Quality attributes, together with preliminary drafts of test patterns suitable for their quantification. Data is provided on initial attempts at quantifying some Text Quality sub-attributes.
A technique is proposed for estimating the surface of the color gamut of an output device, in 3D colorimetric space. The method employs a modified convex hull algorithm. This approach is shown to be more general, and more accurate, than existing techniques. Simple numerical metrics are derived from this surface description: namely the gamut volume in 3D space; and the percentage of colors from the Pantone Matching System which fall within the gamut.
SC682: An Introduction to the Science and Technology of Image Quality of Printing Systems
Image quality is, ultimately, a measure of preference, a very subjective phenomenon. This tutorial will explore the quantitative analysis of image quality of digital color printing systems, using established scientific principles where possible and ad hoc empiricism where necessary, to find the relationships between physical measurements, human perception, and preference. The basic properties of the human visual system will be discussed, in particular the sensitivity to color and spatial frequency, which are critical to determining human perception of image quality defects. Application of such a representation of human vision to development of objective metrics for selected image quality defects will be illustrated. A system for classifying overall image quality in terms of a set of image quality attributes will be presented and analyzed, and emerging standards on perceptual image quality of printed images, based on this system, will be reviewed.