Digital Color Output Conformity to ISO12647-7 Standards (GRACoL 2013 [CGATS21-2-CRPC6]) With the Use of Statistical Process Control (SPC)

Introduction

In a quest to empower students to better understand quality improvement techniques, this applied research examined the industry standard printing process and quality management practices, similar to those a student would encounter upon entering into the industry. Hence, for a student to consistently deliver a quality print, managing and controlling color from the input device to a multicolor output device is a major concern for the graphics and imaging educator.

Modern printing has evolved from a craft-oriented field toward a color management science. This is demanding a greater color control among the display, input/color capturing, and output devices (printing and non-printing) and the substrates used in the printing and imaging industry. The quality of color image reproduction of any type of printing (digital or traditional) is largely influenced by the properties of substrate/paper (Wales, 2009). A modern and up-to-date commercial printing workflow requires a color management system (CMS) to produce a quality color printing. A CMS enables the color producer (printer operator or the designer) to deliver accurate output colors regardless of device color capacities with the use of proper color management techniques (see Figure 1).

Figure 1.

Schematic of PCS of CMS (courtesy of Adobe Systems, Inc.).

Analyzing the color image by examining its quantitative attributes eliminates the subjective judgment of color quality evaluation of printed colors or colors in nature. Color managed digital printing workflow (CMDPW) is represented through schematic illustrations of activities that reflect the systematic organization of analog and digital devices used during the print and image production process (see Figure 2). A print ready e-file (.PDF or.JPEG or.PSD or PostScript, etc.) is likely to be manipulated and later printed by an array of output digital devices (computer-to-plate [CTP], digital printers and printing presses).

Figure 2.

Schematic of CMW at laboratory for this research.

Color management workflow (CMW) or CMS uses a set of hardware tools and software applications working together to create accurate color among various input (image capturing), display, and output devices (printing). A CMS consists of device profiles (characterization of devices), which control and document the working performance of the scanner, monitor, and printer. A device color transformation engine (Color Management [matching] Module [method] or CMM) is one that interprets the color data among the scanner, the display, and the printer. The gamut compensation mechanism of the CMS addresses differences among the color capabilities of input, display, and output device. A device-independent color space (profile connection space, or PCS) is a space through which all color transformation occurs from one device-dependent color space to another (see Figure 1). The PCS is based on the spaces (color models) derived from CIE color space (color model). This device color characterization file (profile) passes in and out of the PCS to complete the color transformation (Fleming and Sharma, 2002). The PCS of the CMS is the central hub of the CMS in which a particular color value is considered absolute and not subject to interpretation (Fleming and Sharma, 2002).

Given that each family of devices tends to create and produce color differently, the challenge is to manage color consistency across the entire workflow. Digital color print reproduction involves physical/mechanical interaction among the imaging cylinder, dry/liquid toner, and the substrate (Avramovic and Novakovic, 2012). The outcome of this interaction is the color print. A review of numerous reports revealed that the study of color is a science and only the optical aspects of color are quantitatively analyzable and measurable. The human eye, however, perceives color more subjectively, which poses a challenge at times for the printing and image reproduction industry. Input (scanners or digital cameras) and output (monitors or printers) devices produce colors differently because they depend on their own color capabilities. The CMS simplifies and improves the reproduction of color images accurately from device to device.

Statistical Process Control Tools

Statistical process control (SPC)—the creation of internal standards—and equipment performance tracking are becoming increasingly important in the CMDPW (Kipman, 2001). In a given CMDPW scenario in which visual methods are currently being used to evaluate image quality in production printing, measurement methods can either replace or augment results adding objective judgment, traceability, and validity. Therefore, this increased efficiency becomes an added benefit to the CMDPW. Quality improvement practices represent a leading approach to the essential, and often challenging, task of managing organizational change (Burnes, 2000). SPC is, in turn, a key approach to quality improvement (Wheeler and Chambers, 1992). SPC was developed in the 1920s by the physicist Walter Shewhart to improve industrial manufacturing (Thor et al., 2007).

In a CMDPW, use of SPC is a scientific technique to control, manage, analyze, and improve the performance of a process by eliminating special causes of variation in the digital printing process (Thor et al., 2007). It is a powerful collection of problem-solving tools useful in achieving process stability and improving capability through education on variability (Abtew et al., 2018). There are seven original SPC tools: flow-charts, check-sheets, histograms, Pareto diagrams, cause-and-effect diagrams, scatter diagrams, and control charts (Mears, 1995). SPC enables us to control quality characteristics of the methods, machines, workforce, and products. There are two kinds of variations that can occur in all manufacturing processes, including the digital printing, which causes subsequent variations in the final product (Abtew et al., 2018). The first is known as the common cause of variation and consists of the variation inherent in the process as it is designed (Abtew et al., 2018). It may include variations in temperature, properties of raw materials, ink/toner, etc. The second kind of variation is known as a special cause of variation and happens less frequently than the first (Abtew et al., 2018). For an example, failed calibration and characterization methods could be carried into the production processes.

A control chart is the best tool for determining whether a process (CMDPW) is in-control or not in-control. An in-control process is one that lacks “assignable causes” or “special causes” of variation. This means that the processes output will be consistent over time. This is not to say that the process will be capable of meeting your needs or your customer’s expectations, just that the results will be relatively consistent (Blevins, 2001). Good color reproduction requires consistency in the CMDPW. Accurate color reproduction is dependent upon several factors. One of the factors is the cyan, magenta, yellow, and black (CMYK) ink densities and quality of device profiles. To achieve acceptable printing results, it is important to establish aim points (target values or control limits [CLs] of the process) and monitor the aim point consistency during the production process. With the use of specific process control techniques (SPC tool), one can determine whether the consistency is in-control or not in-control. If the average density or color deviation values (delta E) and range of the process fall between the established aim points, the process is said to be within the specific process control. Contrarily, if the color deviation and density values are not within the aim points, they would be not in-control (out of control).

Purpose of the Research

The experiment was conducted in a CMDPW. The purpose of this applied study was to determine the colorimetric variation among the printed colors’ overall average color (CMYK RGB) deviation (ΔE) in the electrophotographic color printing process over a period of time (100 days). To determine the statistically significant process variation among these color deviations over a period of time (OAPOT), a control chart analysis technique (SPC tool) was employed. Reference colorimetric values are the threshold deviations (acceptable color deviations) as outlined in the ISO12647-7 standards. To accomplish this, the following guiding objectives were established: determine the deviation in color printing average/overall (CMYK+RGB) ΔE over a period of time (100 days) by comparing the printed colorimetry against the reference colorimetry.

Limitations of the Research

No engineering, mathematics, physics, statistical equations, or computational methods were manually developed/utilized/applied during the experiment or data analysis stage. Industry standard software applications were used. For this research, limitations in the technology of the graphics laboratory were acknowledged. Prior to printing and measuring the samples, the digital color output printing device and color measuring instruments (spectrophotometer and densitometer) were calibrated against the recommended reference. The print condition associated with this experiment was characterized by, but not restricted to, the inherent limitations: colored images (TC1617x, ISO300, and ISO12647-7) chosen for printing. Additionally, the desired rendering intent applied, type of digital printer, type of paper, type of toner, resolution, screening technique, color output profiles, and calibration data applied are acknowledged. Several variables affected the facsimile reproduction of color images in the CMDPW, and most were mutually dependent. The scope of the research was limited to the color laser (electrophotographic) digital printing system (printing proof/printing), substrates, types of color measuring devices, and color management and control applications (data collection, data analysis, profile creation, and profile inspection) used within the university graphics laboratory. Findings were not expected to be generalizable to other CMDPW environments. It is quite likely, however, that others will find the method used and data collected both useful and meaningful. The research methodology, experimental design, and statistical analysis were selected to align with the purpose of the research, taking into account the aforementioned limitations.

Research Methodology

The digital color printing device used in this experiment is a Konica-Minolta (KM) bizHub C6000 Digital Color Press. It uses a Creo IC-307 raster image process (RIP) application (front-end system). A two-page custom test image (12” × 18” size) was created for proofing and printing use for the experiment (see Figure 3). Table 1 presents the variables, materials, conditions, and equipment associated with this experiment.

Figure 3.

Test target image.

The test target contained the following elements: generic images for subjective evaluation of color, ISO12647-7 Control Strips (2013, three-tier), and a TC1617x target for gamut/profile creation. Colorimetric, densitometric, and spectrophotometric data were extracted by using an X-Rite Eye-One Spectrophotometer and an X-Rite i1iO Scanning Spectrophotometer from the color printed samples for the analysis. The process (CMDPW) was monitored for a total of 100 days. A total of 100 samples of target color images were printed/collected for daily measurement/analysis purpose; 100 prints were noted by the letter “N” (N = 100). Of 100 samples, 80 samples (n = 80) were randomly selected and measured, noted by the letter “n” (n = 80). This sample size was selected in order of the specific confidence interval (α = 0.05). Glass and Hopkins (1996) provide an objective method to determine the sample size when the size of the total population is known. This sample size is needed to ensure the reliability of data is accurate. It is well documented that a large sample size is more representative of the sampling (subjects) population (Glass and Hopkins, 1996). The following formula was used to determine the required sample size, which was 80 (n) printed sheets for this study (Glass and Hopkins, 1996):

• n = the required sample size

• χ² = the table value of chi-square for 1 degree of freedom at the desired confidence level (3.84)

• N = the total population size

• P = the population proportion that it is desired to estimate (0.50)

• d = the degree of accuracy expressed as a proportion (0.05)

CMW Setup for the Digital Press

The digital output device used in this experiment is a KM C6000bizHub color printer (or digital press). KM C6000 uses a Creo IC-307 RIP server (front-end system)–based workflow application. This workflow application (the RIP) enables the printer (or designer or operator) to emulate/simulate (see Figure 4) the color device to print as per the ISO12647 series digital color printing production standards. The RIP provides software tools for calibrating the printer and creating an ICC device profile.

Figure 4.

Color transformation of device emulation/simulation (proof/printing).

This simulation process requires the characterization data (an ICC profile) from the printing device (destination printer) as well as the data from the target device (an ISO12647-7 reference colorimetry or other target device profile). Figure 4 illustrates how a device emulation is accomplished from characterizations of the proofing and target (printing) devices (Raja, 2002). Emulation/simulation is not possible without two profiles. In this scenario, target colors are the source colors outlined in the General Requirement for the Applications in the Commercial Offset Lithography (GRACoL 2013) standards, and the printed colors are the destination colors of a device (KM bizHUB C6000 digital press). Many up-to-date CMYK workflows emulate the printed colors to GRACoL standards by default, because the GRACoL reference colorimetry provides an optimal color balance based on the traditional CMYK (offset printing) workflows (Xerox, 2009).

Digital press calibration

A calibration process means standardizing the performance of the devices according to the device manufacturer specifications so that the results of the devices are repeatable. Prior to printing the patches/target image, the printer was calibrated for amplitude modulated (AM) screening technique with a 1200 × 1200 dots per inch (DPI) resolution as per the manufacturer’s specifications for a 100-lb gloss-coated paper. The calibration chart (see Figure 5) was printed and measured by using an X-Rite Eye-One Spectrophotometer.

Figure 5.

Calibration CMYK chart.

A calibration step in a CMDPW is designed to ensure repeatable results by standardizing the performance of the devices according to the device manufacturer’s specifications. The calibration curve consists of the maximum and minimum printable (or acceptable) solid ink densities (SIDs) of each color (CMYK) used for the printing. The calibration data (range of CMYK densities) were saved in the calibration lookup tables (LUTs) of the RIP, and a calibration curve was created (see Figure 6). Measured and reference (or target) SID values are extracted from the digital press LUTs of the RIP. Printed test target TC1617x (Figure 3, right side target-TC1617x) was used for the output device profile (ODP) creation process.

Figure 6.

Calibration of a digital printing press.

Printing with target (source) and DP

As stated earlier, AM screening technique was applied during the calibration and profile creation process in the experiment. The CMDPW was considered a group (process) within the experiment. A group involves a set of print parameters, such as a digital halftone screening technique (AM), a calibration curve (of AM screened), a color SP (GRACoL 2013 for CRPC-6), and a color DP of a digital press (AM screened). As parameters illustrate in Figure 7 (Schematic illustration of sequence of print parameters for CMDPW), the test target of 12” × 18” (two pages) was printed for use in the experiment. The test target contained the following elements: TC1617x target, ISO12647-7 (2013) control strip, ISO300, and custom images of color and black/white for subjective evaluation of color. A total of 100 sheets/samples were printed for the screening technique used by enabling the color management technique at the RIP. The digital press calibration curve, DP, and SPs all were applied during the printing (see Figure 7).

Figure 7.

Schematic illustration of sequence of print parameters for CMDPW.

The group printing performances were monitored for a period of 100 days to determine the fluctuations in the color consistency (4th C of CMW) by printing multiple printing jobs on the same type of paper with the same print sequences. Prior to start of printing for the day, the test target (Figure 3) was printed and measured for colorimetric and densitometric data, and the sample was kept aside for a later-stage analysis. A total of 100 target images were printed/collected for the analysis. Of the 100 printed samples (from 100 days, N = 100), data were generated from the randomly pulled 80 printed samples (80 days samples, n = 80). The test image consists of a printed ISO12647-7 (2013) control strip (see Figure 3, right side, top bar). By using Eye-One-Pro spectrophotometer with interface application, such as the CGS-ORIS Certified WEB, the printed image was measured against the GRACoL2013_CRPC6 reference data. Measured colorimetric data (CIELAB) from the ISO12647-7 (2013) control strip were used to determine the color deviations. Data derived from the ISO12647-7 (2013) control strip (sample) is the difference between the characterization data set (full IT8.7/4 target) and the sample. The control strip (wedge) image is intended primarily as a control device for pre-press proofs but may also be used to control production printers or presses. The wedge has three rows and 84 patches, and it contains only a small sub-sample of the total printable color gamut. The wedge contains too few patches to prove an accurate match to a specification like GRACoL or SWOP (Specifications for Web Offset Publications). It does contain enough patches to monitor the stability of a system that has previously been tested with a target such as the IT8.7/4 (CMYK target image). The reference file content for this image (IT8.7/4) are the CMYK dot percentage values and the nominal CIELAB characterization data values for the GRACoL 2013-CRPC6 reference. Colorimetric, densitometric, and spectrophotometric computations were used to determine the color deviations. Colorimetric formulae and formats were presented in the following section (“Data Analysis and Research Findings”) for each of the color deviations/attributes investigated.

Data Analysis and Research Findings

Colorimetric computations and SPC methods were used for the color deviations and process variations. Analyzed collected data are presented in the following pages and tables. Subjective judgment on color difference or any deviations was not used in this particular study because the subjective judgment of color differences could differ from person to person. For example, people see colors in an image not by isolating one or two colors at a time (Goodhard and Wilhelm, 2003) but by mentally processing contextual relationships between colors where the changes in lightness (value), hue, and chroma (saturation) contribute independently to the visual detection of spatial patterns in the image (Goodhard and Wilhelm, 2003). Instruments such as colorimeters and spectrophotometers eliminate subjective errors of color evaluation perceived by human beings.

Overall color variation (ΔE) of printed jobs versus GRACoL 2013 reference in a CMDPW

The CIE L* a* b* values associated with the CMYK+RGB colors of printed jobs versus GRACoL 2013 (CGATS21-2-CRPC6 [reference]) are compiled in Table 2 (IDEAlliance, 2014). Numerical color differences (ΔE) were found when comparing the average printed colors versus GRACoL within all seven colors (CMYK+RGB). Also, noticeable visual color differences were found in the solid color area. Overall, both groups of images are similar in colors (see Figure 9), with the exception of the printed image consisting of lower L* a* b* values. This results in producing the slightly higher ΔE for these colors.

Figure 8.

Schematic of L* a* b* and c* h* coordinates.

Figure 9.

2D gamut of printed image versus GRACoL 2013-CRPC-6 reference.

This higher color deviation (yellow, black, red, and green) might be the result of the substrate (paper) or toners used (age, condition, quality, etc.) or measurement error. These are the darker colors that produced lower L* value and in turn affected the slightly higher color deviation. The 2D color gamut comparison (see Figure 9) reveals that the colors of the printed image closely match the reference colors. The goal was to determine the consistency/deviations among various attributes of colors over a period of 100 days in a CMDPW. The comparison is an indication that, in a CMW, color matching of a target image can be achieved from device to device regardless of device color characterization and original colors. Subjective judgment was not used for the color comparison.

The ND curve is not symmetrical around the mean (average), but it is skewed to the left (see Figure 10) showing that the average color deviation (ACD) is lower than the median of ACD ($\overline{\mathrm{X}}$ = 2.978, Med = 3.015, SD = 0.437). GRACoL 2013 guidelines indicate the acceptable ACD is 3.00 (ΔE₍₂₀₀₀₎ ≤ 3.00). Most of the printed jobs produced ΔE₍₂₀₀₀₎ ≤ 3.00. The ACD values are more frequent in occurrence to the left (see Figure 10) than the right of $\overline{\mathrm{X}}$. The standard error (Std Err or SE) of ACD is 0.048. It determines the reliability/accuracy of the average ACD of the CMYK RGB colors in the process. A small SE is an indication that the produced average is a more accurate reflection of the actual population mean. A larger sample size will normally result in a smaller SE, whereas the SD is not directly affected by sample size. Further normality validation was performed by visually evaluating the ACD of CMYK RGB values by plotting in the Quantile-Quantile (Q-Q) chart (see Figure 11). It plots the quantiles of ACD values (values that split a data set into equal portions) of the data set instead of every individual data point of the collected data. Also, a Q-Q plot is easier to interpret when there is a large sample size (in this case, N = 100, n = 80).

Figure 10.

Normal distribution curve of ACD of CMYK RGB colors.

Figure 11.

Q-Q plot of ACD of CMYK RGB colors in a CMDPW.

The skewness of the ND is −1.509 (with SE 0.048), and it is interpreted as the data are not symmetrical (see Table 3). It is negatively skewed (−1 and −0.5). The kurtosis of the ND of the ACD of CMYK RGB colors of the process is 3.931 (with SE 0.532). The distribution of ACD of CMYK RGB colors is leptokurtic (kurtosis of >3) because this type of distribution is longer and tails are fatter. The peak of the curve is higher and sharper, which means that data are heavy tailed or there is a profusion of outliers. If the kurtosis is +1.00 of the ND of the ACD of CMYK RGB colors, then the distribution would be too peaked; if there is an indication of −1.00 of the ND of the ACD of CMYK, the distribution would be too flat. Distributions exhibiting skewness and/or kurtosis that exceed these guidelines are considered non-normal (Hair et al., 2017), which the CMDPW was expected to produce. In the graphs (see Figures 10 and 11), normal distribution does not appear as a bell shape curve, and the Q-Q plot represents almost a straight line (see Figure 11).

The average and range control charts analysis was applied for further determining the process (CMDPW) ACD consistency (ΔE₍₂₀₀₀₎). The SPSS application was used to calculate/construct the graphs of the CLs of average (X-Bar) and range variation. The CL (X-Double Bar), upper control limit (UCL; X-Bar), and lower control limit (LCL; X-Bar) values associated with the ACD (CMYK RGB) variations of the CMDPW are compiled in Table 4.

Table 3.

Descriptives of Normality Test for Average Color Deviation [ΔE₍₂₀₀₀₎] in a CMDPW

Attributes/Variables	Statistics	Standard Error
Average Color Deviation [ACD (CMYK RGB)]	2.978	0.048
Acceptable ACD Threshold ΔE(2000)	≤ 3.00
Median (Med) of ACD	3.015
Standard Deviation (SD)	0.437
Skewness	-1.509	0.269
Kurtosis	3.931	0.532

Table 4.

Average Color Deviation (ACD) or Color Variation [ΔE₍₂₀₀₀₎] of Printed Samples in a CMDPW

	Printed Sample	Printed Samples
	n = 80	n = 80
Color	Mean (average)	Std. Deviation
Attributes/Variable	ni = 7	ni = 7
Mean Color Deviation [ΔE(2000)] of CMYK RGB	2.978	0.437
Printed Samples Average Color Deviation Control Limits
UCL $\overline{\mathrm{X}}$	4.828
CL (X-Double Bar)	2.978
LCL $\overline{\mathrm{X}}$	1.125

Table 5.

Average Color Deviation Range Variation [ΔE₍₂₀₀₀₎] of Printed Samples in a CMDPW

	Average	Average	Printed Sample
Color	Maximum	Minimum	n = 80
Attributes/Variables	ΔE(2000)	ΔE(2000)	Range Mean (average)
CMYK RGB	5.49	1.42	4.415
Printed Samples Range Color Deviation Control Limits
UCL (Range)			8.497
CL (R Bar)			4.415
LCL (Range)			0.334

Differences were found in the ACD of CMYK RGB values of printed colors when comparing with the GRACoL standards. However, overall the process was consistent from day to day over a period of 100 days (see Figure 12) and concluded to be in-control.

Figure 12.

Control chart of average color deviation of CMYK RGB in a CMDPW.

The ACD (ΔE₍₂₀₀₀₎) X-Bar (average) and R (range) variation were monitored by comparing them to calculated tolerances (CLs of the process). If the variations are within the tolerances, then the ACD of CMYK RGB colors are accepted. If the variations are not within the established tolerances, then they are not accepted. The X-Bar chart illustrates how the ACD of CMYK RGB varied over a period of time (100 days), while the R-chart illustrated the dispersion (variation) within the samples studied (monitored). These charts have three lines parallel to the x-axis, while the average values are parallel to the y-axis. The average and range color deviation (ΔE₍₂₀₀₀₎) values (see Figures 12 and 13) fall closely along the CL line (within the CLs) or below the CL indicating the ACD of the process was very consistent. ACD less than CL (average printed ΔE₍₂₀₀₀₎ = 2.978) is an indication of lower color deviation between the printed and reference.

Figure 13.

Control chart of color deviation range variation of CMYK RGB in a CMDPW.

Summary/Conclusions

This applied research experiment was conducted in a CMDPW. The workflow was observed and monitored for 100 days. It was aimed at determining the ACD consistency over a period of 100 days. The conclusions of this study are based upon an analysis of colorimetric and densitometric data, visual assessment, and associated findings. The guiding objectives of this study allowed testing of an accepted color management practice to gain a better understanding of the presumptions associated with the application of SPC in a digital printing environment. The experiment examined the importance of calibration, characterization, and the color evaluation processes of the digital press which was capable of printing colors to match or be in proximity of GRACoL 2013 standards. Printed samples from the experiment were measured against the GRACoL 2013 (CGATS21-2-CRPC6) standards in CIELAB space using CGS-ORIS CertifiedWEB application interface with an X-Rite Eye-One Spectrophotometer. The data collected were run through multiple software applications (Microsoft Excel/SPSS/Minitab) to apply various statistical methods. Analyzed data from the experiment revealed that the printed colorimetric values were in match (aligned) with the GRACoL 2013 (reference/target). Since the SID values of CMYK colors were in control throughout the process, this enabled the CMDPW to produce consistent acceptable color deviation (ΔE₍₂₀₀₀₎).

It is evident that integration of device profiles is important in a CMW, and it also enables/allows the workflow process to meet specific industry standards of ICC-based CMW. This study represented specific printing or testing conditions. The images, printer, instrument, software, and paper that were utilized are important factors to consider when evaluating the results. The findings of the study cannot be generalized to other digital printing workflows. However, the result of this research may be of interest to others when exploring similar methodologies to other printing workflows. The findings determined that only the optical aspects of color are quantitatively analyzable and measurable because humans perceive color subjectively. It will be hard to document and measure the color values we see or detect. Additionally, the implementation of a CMDPW is costly, time consuming, and a tedious process. It does, however, benefit those who implement this workflow to get consistent color from device to device. Applied statistical methods and the outcome of the analyzed data enabled the determination of the process consistency. It is important to reiterate the fact that having a CMW will not replace the SPC. Employing quality improvement techniques or strategies must be part of any manufacturing process or digital printing. The colorimetric data of this experiment also led to the conclusion that the application of a correct print parameters setup is an important step in a CMDPW in order to output accurate colors of choice for a desired use/purpose. Mismatch of print parameters could result in a color management discrepancy or inconsistency.