Analyzing the color image by examining its quantitative attributes eliminates the subjective judgment on color quality evaluation of printed colors or colors in nature. Advancements in science and engineering, however, have allowed print and graphic professionals to apply scientific research methods across printing workflow. Applying these methods heightens the importance of proper print production workflow. Workflow 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. 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]. Given each family of devices tends to create and produce color differently, the challenge is to manage color consistency across the entire workflow. Analysis of color image quality in the commercial printing workflow requires a Color Management System (CMS) to assist the color producer (printer operator or the designer) in delivering accurate output colors regardless of device color capacities with the use of proper color management techniques (see Figure 1).

Digital color print reproduction involves physical/mechanical interaction among the imaging cylinder, dry/liquid toner, and the substrate (Avramovic & Novakovic, 2012). The outcome of this interaction is the color print. Color can be viewed as a science where 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. A continuous-tone color image is composed of a full spectrum of shades and color from near white to dense black. In a traditional printing (offset, digital offset, gravure or flexography) workflow, the method by which continuous-tone photographic images are transformed to a printable image is called halftoning. In this method, varying percentages of the printed sheet are covered with halftone dots to represent the varying tones in the image. The ink (paste or liquid ink) printed by each dot, of course, has the same density. At normal viewing distance, the dots of a printed image create an optical illusion of a continuous tone image. In contrast, a simple digital image could be a binary picture, [h(x, y)], with each point being either completely black or completely white (Pnueli & Bruckstein, 1996). A digital halftone is a pixel map, with bit depth, that gives the impression of an image containing a range of gray shades or continuous tones. An 8-bit grayscale image contains 256 different levels of gray from white to black.

Gray balance is the proper percentage of combinations of cyan, magenta, and yellow inks that produce neutral shades of gray. Hue shifts will occur when there is any imbalance of one of the components. The imbalance is due in large part to ink impurities, chromaticity deviation of the substrates, and other attributes. Gray balance is a significant factor in determining overall color gamut. Gray balance can be determined by careful evaluation of a full set of tint charts printed with process inks. Colorimetric methods are used to determine if the hue of gray is desirable in order to make sure that the black ink scale is neutral. ISO 12647-7 document states that the gray balance can be printed and measured at the CMY overlap (overlap of C = 50%, M = 40%, and Y = 40%). The deviation can be determined from the calculation of ΔH* (deviation of hue, h*) or ΔC* (deviation of chroma, c*) and it requires the colorimetric data of CMY overlap printing from the L* a* b* model.

Regardless what experts (published reports) say, there is no single/fixed screening method that would work for every color reproduction. Since the introduction of digital workflow (computer to film, to plate, and to press) in the industry, there are many experts who have come up with technologies and tools for color images that would benefit from improved color reproduction technologies. Screening is only an option of such technologies. In the digital printing environment, screening software can create the digital version of the amplitude modulate (AM) and also of the frequency modulated (FM) halftone screen. Screening software in the raster image processor (RIP) of the color managed workflow (CMW) at a digital printing press front-end platform (DFE) applies a digital dot pattern to the color image during printing.

The quality of color image reproduction of any type of printing (digital or traditional) is largely influenced by the properties of paper. Paper is considered a commodity, but its properties are a long way from being standardized (Wales, 2009). In order to produce quality printed materials (a good halftone image), the press operator must carefully manage several print parameters, such as the source colors (a source profile of ISO/ANSI standard), press calibration, press characterization (device destination profile), and the screening option. Without assigning these parameters to a print job would result in a color mismatch.

The purpose of this experimental study is to determine if significant differences exist or not in the gray hue (overlap of CMY) deviation (ΔH) among the eight Color Output Sequences (COS). The experiment is conducted in a color managed digital printing workflow (CMDPW) to determine the effect (impact or influence) of print conditions on the printed gray hue based on the statistical evaluation among eight (groups, K = 8) different types of color print modifications. The following one-tailed non-directional hypothesis was established for this study because of the eight independent COS groups. A one-tailed non-directional hypothesis predicts the nature of the effect of the independent group on the dependent variable (or the color attribute as the outcome). A non-directional alternative hypothesis does not state the direction of the difference but indicates only that a difference exists. The one-tailed test provides more power to detect an effect in one direction by not testing the effect in the other direction.

H_o: There is no significant difference (or relationship) in the color printing attribute of Gray ΔH (CMY overlap) among the eight COS when the printed colorimetry is compared against the reference colorimetry.

H_a: There is a significant difference (or relationship) in the color printing attribute of Gray ΔH (CMY overlap) among the eight COS when the printed colorimetry is compared against the reference colorimetry.

The digital color printing device used in this experiment was a Konica-Minolta bizHub C6000 Digital Color Press. It uses a Creo IC-307 raster image process (RIP) application (front-end system). A two-page custom test target image (12” × 18” size) was created for proofing and printing use for the experiment (See Figures 3 & 3A). The test target contained the following elements: an ISO 300 and generic images for subjective evaluation of color, an ISO 12647-7 Control Strip (2013, three-tier), and a TC1617x target for gamut/profile creation. Table 1 presents the variables, materials, conditions, and equipment associated with this experiment. 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 statistical analysis to determine the significant differences that exist among the eight COS groups. Print/Color attributes (primary colors and gray hue) from each group were analyzed/compared with one another. For all the eight COS groups, a total of 800 samples of target color images were printed, 100 prints for each substrate group, noted by letter “N” (N = 100). Of 100 samples of each group, 80 samples were randomly selected from each COS group, and measured, noted by the letter “n” (n = 80). Glass, G.V. & Hopkins, K.D. (1996), provides an objective method to determine the sample size when the size of the total population is known. The following formula was used to determine the required sample size, which was 80 (n) printed sheets for each COS for this study: n=[χ2NP(1-P)]/[d2(N-1)+χ2P(1-P)]

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 (.50)

d = the degree of accuracy expresses as a proportion (.05)

The descriptive and inferential statistics used along with the results are presented in tables and on the pages that follow. Subjective judgment on color difference/deviation was not used in this study. The subjective judgment of color difference 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 & 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 & Wilhelm, 2003). Instruments, such as colorimeters and spectrophotometers, could eliminate the subjective errors of color evaluation perceived by human beings. In comparing the color differences between two colors, a higher (ΔE or ΔH) is an indication that there is more color difference and a lesser (ΔE or ΔH) is an indication of less color difference.

Statistical Package for Social Sciences (SPSS) was used to analyze the collected data to determine the colorimetric variation (COLVA) among the eight groups. Since the K = 8, a one-way Analysis of Variance (ANOVA) with equal n method (at α = 0.05) was used to determine the significant differences that exist among the (K = 8, n_i = 80, n_t = 640) group means (averages) color deviations of the various COS (Glass & Hopkins, 1996). The F-test can be calculated by using the following equation (Glass & Hopkins, 1996). F=σb2σw2=MSbMSw=SSb/VbSSw/Vw=∑nk(X¯k−X¯)2/K−1∑(Xik−X¯k)2/N−K

The F distribution and a probability value p, which is derived from the F, were used to determine if significant differences exist in the output color attributes of COS. F is a ratio of two independent estimates of the variance of the sample, namely between the groups and within the groups (K = 8, n_i = 80, n_t = 640). A low p value (or higher F value) is an indication that one should reject the stated null hypothesis (H_o) in favor of the stated alternative hypotheses (H_a). This is interpreted as one of the COS means (average) is significantly different. It suggests that there is a strong support that at least one pair of the COS group means (averages) is not equal. The higher the p value (or lower F value) indicates that the means of various color attributes of the COS are not statistically different. When statistically significant effects were detected among the eight groups, the Tukey method - Post hoc ANOVA analysis was used to determine which group (K) means were significantly different. The Tukey method is also known as the honest significant difference (HSD) test between two sample means (averages) which can be determined by using the following equation (Glass & Hopkins, 1996). q1=X¯1−X¯KSX¯

The value of q is the difference between the larger and smaller means of the two samples. Differences among the means at p ≤ 0.05 were considered to be statistically significant among all the eight COS groups. The main effect of the print parameters on the digital color output in a CMDPW was determined by using the above stated methods (F and q). The HSD multiple comparison test (with α = 0.05) in the experiment enabled the researcher to identify the significant difference between one COS group to others. In other words, which COS differs significantly from another?

Colorimetric, densitometric, and spectrophotometric computations were used to determine the gray color (overlap of C = 50%, M = 40%, and Y = 40%) hue variation (ΔH) among the eight COS. The data for each group were generated from the printed colors of the target (ISO 12647-7, 2013 Control Wedge/Strip) image (see Figure 7) by using an Eye-One-Pro spectrophotometer with interface application, such as the CGS-ORIS CertifiedWEB against the GRACoL2013_CRPC6 reference data. Measured colorimetric data (CIE L* a* b*) from ISO 12647-7 control strip were used to determine the means (averages) of: Gray ΔH (CMY overlap) between the printed colors and its reference data (IT8.7/4). Data derived from ISO 12647-7 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 3 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 nominal CIE L* a* b* characterization data values for the GRACoL2013-CRPC6 reference.

This research demonstrated the influence of applied Color Output Sequences (COS) in determining the colorimetric variations (COLVA) on the gray balance (GB) hue in a Color Managed Digital Printing Workflow (CMDPW). Correct COS approach would be assigning the correct print parameters during the printing. Print parameters play an important role in a CMDPW. This is as per the color science of a Color Managed Workflow (CMW), which in turn relies on the 4-Cs (Calibration, Characterization, Conversion, and the Control). The findings of this study represent 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 CMDPW. However, other graphic arts educators, industry professionals, and researchers may find this study meaningful and useful. For example, educators can implement similar or the presented model (or method) to teach color management concepts.

For each COS, colorimetric variation (COLVA) in the gray balance was determined on the basis of GRACoL2013-CRPC 6 reference. The data from the ANOVA test revealed that there were significant differences in the color reproduction among multiple COS. The printed gray average color (overlap of CMY) ΔH of the multiple COS groups (N = 8, Average ΔH = 1.47) was found to be within the GRACoL2013-CRPC reference (ΔH = 1.50). It suggests that a mismatch of print parameters within the COS groups did not affect the gray hue deviation. However, the ANOVA test revealed that significant differences exist in the gray hue among the multiple COS. As a result of this experiment, it is evident that color management works and it offers more flexibility and control over color reproduction images. 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 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. Future study is needed to determine the gray hue of color management workflow vs. non-color management workflow. Future studies can be conducted by using other printing processes. The colorimetric data of this experiment led to the conclusion that the application of a correct print parameters set-up is an important step in a CMDPW in order to output accurate colors or gray balance of choice for a desired use/purpose. Mismatch of print parameters could result in a color management discrepancy.