spie proceedings [spie electronic imaging 2003 - santa clara, ca (monday 20 january 2003)] color...

Absolute and Relative Colorimetric Evaluation for Precise Color OnScreen

Franz Herberta, Jo Kirkenaera, Jack Ladsonb

aIntegrated Color Solutions, Inc., 609 S. Vulcan Ave, Suite 301, Encinitas, CA 92024bIntegrated Color Solutions, Inc., 1000 Plowshare Rd., Yardley, PA 19067

ABSTRACT

This paper deals with assessing and controlling the variables required to present accurate and precise color on screen.The objective is to generate a representation of an accurate, precise, soft copy of an object color with little difference intheir color and appearance. This opens new vistas in product design and quality control. We obtained duplicate sets of23 colors including two neutral chips that are distributed and widely spaced at different color centers throughout colorspace. We used these sets to evaluate color and appearance at different locations remote to one another. We obtainedCIE L* a* b* values for the color representations displayed on the screen under multiple illuminants, and comparedthose colorimetric values to the corresponding object color sample values with a Pearson Correlation coefficient greaterthan 0.95 for all illuminants. Multiple personnel in different locations performed psychometric evaluations of the colorand appearance presented by the display to that of the perceived color and appearance of the object under multipleilluminants. We quantitatively assessed and ranked the quality of the perceived color matches. We judged the precisecolor on screen to be accurate using our rating system and applying business statistics to evaluate and quantify theresults. The evaluation of the data validate that we achieved excellent colorimetric (measured) accuracy and quantifiableperceptual agreement of the soft copy color to the color and appearance of objects.Keywords: Display Calibration, Precise Color on Screen, Color Adaptation, Softcopy, Spectrophotometry

1. INTRODUCTION

In the graphic arts industry and related industries it is customary to view the accuracy and quality of color by judging theperceived color of very complex images1. This has led to a great deal of work in understanding the psychophysicsinvolved in assessing an observation. Additionally considerable time and resources have been devoted to understandingthe issue of gamut compression and gamut mapping. This enables colors to be represented that are not within the gamutof a device, and the ability to reproduce accurately an image in multi-media.

Our issue is somewhat different from that stated above. Our challenge is to achieve a high degree of color accuracyperceptually for a single color representation or a single stimulus on a display unit; sometimes this is referred to as “soft-copy”2. Specifically, we are comparing and judging the quality and accuracy of the color and appearance of an imagepresented on a display unit to the perceived color of an object (in this case a plastic chip). It was also an importantrequirement to view and to verify metamerism and color constancy of the object compared to the displayed image. Thismeans that we need to view accurately under different illuminants the object and the projected image. This also meansthat we need to maintain the relationship of color and appearance over those changing illuminants. This body of workexamines color and appearance results quantitatively under three popular industrial illuminants, D65 (Daylight), F2(Fluorescent), and Illuminant A, (Incandescent). Furthermore we extended the challenge and present the results ofperforming these tasks using different systems, in different locations, using different operators, and separated by 3,000miles.

The purpose of the experiment demonstrates that the color of an object can be accurately portrayed on screen using adisplay device; such that an experienced color expert would judge the comparison of the colors to be nearly identical orperfect. A wide selection of specimens allowed us to prove and validate the accuracy of the comparison over the gamutof color space. We termed accurate color representation on screen to be “precise color on screen.” After achievingexcellent colorimetric agreement between the measured color of an object and the displayed representation of that colorwe found that there were apparent lightness/hue/chroma shifts in the displayed representation. We determined that thebackground and the immediate surround had enormous effects on the perceived color of the display and the perceived

Color Imaging VIII: Processing, Hardcopy, and Applications, Reiner Eschbach, Gabriel G. Marcu,Editors, Proceedings of SPIE–IS&T Electronic Imaging, SPIE Vol. 5008 (2003)

© 2003 SPIE–IS&T · 0277-786X/03/$15.00

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color of the specimen3. Based on our observations and experiences, we found that we needed a white point reference inthe viewing booth as well as the display to assist in the process of chromatic adaptation4. We also observed that theappearance and color of the object in the viewing booth changed depending upon its physical placement in the booth andreflections incident upon its surface.

Further investigation and experimentation minimized the variables such that we obtained excellent agreement bothcolorimetrically and perceptually. All references in this body of work are for color representations that are within thevisual gamut of the display device. This work will expand on the color and appearance modeling adopted by the CIE in1997.

The specimens used in this study are plastic chips used in the routine evaluation of colored plastic products in theindustry. Because of the optical properties of the specimens it is obvious that other materials could be evaluated whosecolors are critical. This includes opaque, translucent, and transparent materials. The experience gained through thisstudy can be extrapolated to improved ‘soft proofing’ in the graphic arts, eventually making the need for viewing boothsa thing of the past.

2. EXPERIMENTAL

2.1 Specimen Selection

The specimens are a subset of a larger group of samples that had the following properties. First, the samples wererepresentative of the typical products of colors produced in manufacturing. Second, the specimens must be widelyspaced in hues so when combined with the property of varying lightness they provided a representative sampling ofcolor space under three illuminants. Third, all the samples must be within or very close to a gamut defined by ourdisplay device. We selected 23 specimens that met these criteria. We added two additional specimens, a near white andgray, to validate photometric linearity and accuracy near the neutral, L*, axis.

2.2 Specimen Physical Parameters

The specimens consist of plastic samples that vary in physical size and thickness. However, most of the samples were1” x 2” and nominally 0.060” thick. The samples subtend less than 4 degrees of arc, actually ~3.5 degrees average,when viewed in the environment we designed for color comparison, assessment, and judgment. The specimens weremanufactured from various plastic compounds; including, PE, HDPE, PET, NYLON, and HPPA.

2.3 Specimen Optical Properties

Most specimens are opaque and some are moderately translucent. Translucent specimens have a contrast ratio, the ratioof the measured CIE Y values for Illuminant D65 under the 2-degree Observer function of the specimen over black,divided by similar values except that the backing material is white in the denominator5. Therefore, it follows that opaquespecimens have a contrast ratio of ~1.0 because there would be no difference in the values when white or black backs thesample; hence the fraction reduces to unity. All specimens are glossy to moderate glossy having approximate initial 60-degree gloss values between 50 and 90 Gloss Units6.

2.4 Specimen Color Coordinate Values

Table 2.4-1 defines the color coordinate values for the “25” chipset in terms of D65 illuminant under the 2-degreeeobserver. Data are reported in terms of CIE L*a*b*7.

Proc. of SPIE Vol. 5008 295


Table 2.4-1 Color Values of Specimens (D65)

Color L* a* b*

1 Beige 74.67 1.91 20.462 Blue 2995C 61.11 -19.28 -38.923 Blue 43.47 -12.65 -44.104 Brown 45.62 7.25 18.825 Brown Acc 19.31 12.54 12.266 Citrus 87.33 -7.08 95.377 Green - Dark 26.97 -0.28 3.338 Green 43.34 -53.47 -3.429 Green - Light 43.68 -47.56 11.6910 GreyHound Gray 60.55 -1.06 1.3811 Maroon 18.55 32.25 15.9412 Mauve 41.19 32.42 5.9813 Navy Blue 15.20 8.05 -22.1014 Pink 81.28 19.82 2.7915 Red PMS# 485 46.11 56.74 34.9816 Royal Blue 41.74 14.31 -58.7017 Sapphire Blue 12.67 20.98 -36.2718 Slate Gray 27.97 -0.76 -3.0119 Butterscotch Light 68.19 26.04 58.3220 Butterscotch (brownish) 47.49 16.85 34.7621 Dark Chocolate 10.90 8.93 5.2122 White 94.27 -0.20 0.9923 White 201 93.72 0.95 -2.1024 Yellow 86.28 -8.00 66.8425 Yellow -Dark 67.10 11.81 76.77

2.5 Monitor Calibration

The calibration of the monitor was one of the most critical procedures in achieving success with our visual comparisons.We tested CRT’s as well as LCD’s and found that we could only get reasonable results for Illuminant A with LCD’s. Inaddition it turned out that the luminance of the display was a crucial parameter. We experimented with several differentdisplays and ended up using the Apple 17” Studio Display. The luminance of the uncalibrated state was approximately330 cd/m2, which surpassed all other displays we tested. See table 2.5-1 below for the luminance after calibration foreach illuminant.

Table 2.5-1 Display LuminanceIlluminant LuminanceDaylight (D65) 292.4 cd/m2

Fluorescent (F2) 261.8 cd/m2

Incandescent (A) 193.1 cd/m2

The calibration procedure consisted of several steps. This allowed the system Video Lookup Tables (LUTs) to bemodified, until the error approached zero.

Apple 17” Studio Display is a registered trademark of Apple Computer, Inc.

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1. Set Video LUTs to identity values (curves)2. Compute desired XYZ values from standard spectrum of desired illuminant3. Measure white patch (for standardization)4. Use white patch as scalar for XYZ calculations5. Modify end points of Video LUTs until desired white point is within 0.2 ∆E unit.6. Measure 33 step grayscale7. Compute correction function which turns display response into perfect Gamma function of 2.28. Add correction function into Video LUTs9. Measure 25%, 50% and 75% gray patch, iterating to ensure correct white chromaticity values and correct

gamma value10. Measure RGB grid11. Compute ICC profile

The ICC profile was set as the system profile using ColorSync, with the Video LUTs being stored as a tag in the profile.The measured spectral values of the chips were converted to the respective illuminant relative L* a* b* values. Thesewere passed into the L*a*b* input of the display profile and converted to monitor RGB using ColorSync.

An additional parameter was the display target luminance. If the display was not set to maximum luminance, wereduced the end points of the Video LUTS until the desired luminance was achieved. This procedure presented the onlyway to achieve equal luminance after calibration for the three desired illuminants. Since the resulting luminance forIlluminant A was low, we chose not to force the other illuminants down to that level, but compensated by adjusting thebrightness of the viewing booth to match the luminance of the display. See Figure 2.5-1 below for the spectra of theindividual white measurements after calibration:

Figure 2.5-1 Spectra of Standardized White Curves

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The software provided the mechanism to adjust the chromaticity of the white point of the video display device to that ofthe viewed illuminant. Table 2.5-2 specifies the correlated color temperature of the display and the correspondingchromaticity coordinates.

Table 2.5-2 Chromatic Coordinates and Correlated Color Temperature of monitor

Selected Illuminant Correlated Color Temperature CIE ChromaticityCoordinate, x

CIE Chromaticity Coordinate, y

D65 (Daylight) 6500 K 0.3127 0.3293F2 (Fluorescent) 4158 K 0.3721 0.3751A (Incandescent) 2856 K 0.4473 0.4078



We adjusted the luminance level of the white displayed on the display device to be greater than 100 cd/m2 in accordancewith the ISO specifications for display.

2.6 Viewing Conditions and Environmental Considerations

The current configuration of the experiment did not place the object (target) in conformance with current InternationalStandards Organization, ISO, recommendations8 for illuminating and viewing objects as shown in Figure 2.6-1.However, we found that the recommendations were not ideal when attempting to simultaneously view and compare anobject and the corresponding soft copy on the display unit. We found that it is necessary to be normal and perpendicularto the display device and the object when performing critical color comparisons. This setup is shown in Figure 2.6-2.

Figure 2.6-1 Environmental Conditions for Simultaneously Observing Critical Color Comparisons

Figure 2.6-2 Viewing Conditions for Observing Critical Color Comparisons

• Lighting tracks should be positioned directly aboveor slightly in front of the Observer

• Light should be projected on the adjacent walls tominimize glare

• Light pattern from both illuminants should overlap(illuminated area on the wall should contain bothilluminants)

• D65 Illumination + A Illumination = F2 Illumination

D65 (Daylight)

A (Incandescent)



Color Monitors are increasingly being used to display and view digital “soft-copy” images. It is extremely importantthat the viewing conditions in which the Monitors are placed are adequately specified and controlled. However,adherence to these recommendations does not ensure that the color representation on the Monitor will match theperceived color of the object. In practice, with high quality color management, and an accurate display of a colorrepresentation, a visual perceptual match is difficult to achieve for many reasons.

We positioned the display device so that there were no colored areas in the field of view. This ensured that there wereno reflections on the screen of the display device. All walls, floors and furniture are neutral grey, which minimized anyeffect disrupting the vision of the viewer. Monitor Luminance, ambient illumination and other environmentalconsiderations for viewing adhered to the ISO recommendations.

We also provided a white reference in the immediate field of view of the observer to allow them to chromatically adaptto a white point under the given illuminant9. The software would also allow us to adjust the width and brightness of thewhite reference on the display.

2.7 Observers and Visual Evaluation

There were 3 observers in two different locations that participated in the color comparison of the object color to the colorrepresentation on the display device. The 6 observers made over 450 color observations. We verified that eachobserver’s color vision was normal. We tested this using an Ishihara’s Test for Colour Deficiency10 as a validation tool.The age range of the observers was from 45 to 60 years old and one female participated in the observations, as shown inTable 2.7-1, below.

Table 2.7-1 Age and Gender Distribution of Observers.

No Color Normal Gender Location Age1 √ M CA 602 √ M CA 523 √ F GA 454 √ M GA 525 √ M GA 47

All the observers consider themselves capable of and are experienced in making critical color industrial observations.One of the authors designed and performed psychometric scaling experiments. None of the observers worked as aprofessional color matcher; however, each observer made thousands of color matches and/or judgments over their career.

We compared each specimen to the representation on the display unit and rated its color and appearance using a threepoint scaling system. Where 1 is perfect, 1.5 is nearly perfect, 2 is acceptable, 2.5 is marginal, and a rating of 3 is notacceptable. There were two observers in the CA location, and three observers in the GA location. From time to timevarious observers participated in the experiment in both locations.

2.8 Software and Colorimetric Analysis

We developed custom software to address the multiplicity of needs that this challenge demanded. The software wasvery flexible in that we could adjust the visual parameters to more accurately test color and appearance models. Thisalso provided us with the flexibility needed to compensate for the multiplicity of factors affecting human colorperception. This software product “locked-down” the critical adjustment parameters so that an operator could notchange the configuration during visual experiments.

We designed and wrote another software application to measure the representation of the single colors on the displayunit. We measured these in CIE L*a*b* units using illuminant D65 under the 2 degree observer. We also measured thespectral reflectance factor of each of the specimens and computed the object color for those specimens using D65illuminant under the 2-degree observer. These computations were performed in accordance with ASTM E-308. For this



test and evaluation we included those specimens out of the display’s gamut. We then calculated the total colordifference, CIE ∆E*, as well as the individual component differences in ∆L*, ∆a*, and ∆b*. The measurements showedexcellent correlation and agreement between the spectrophotometric object color values and those measured displayedvalues; hence, we proceeded with a visual evaluation.

3. Results

The effect of calibration on the Apple Studio Display can be seen in Figure 3-1. The top curve shows the display withlinear Video LUTs, and the lower curve, representing an almost perfect gamma curve is after calibration to a gamma of2.2 (D65). The graph shows luminance as a function of digital output value (0-255) of an arbitrary Apple Studio Display.

Figure 3-1 Video Display Response Before and After Calibration

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One important aspect of our monitor matching was that standard methodology using the Bradford transform to calculatethe appearance shift from e.g. D65 to the ICC colorimetry of D50 did not yield acceptable results. Only when weskipped the Bradform transform and calculated the monitor profile to expect D65 L*a*b* values did the resulting colorssatisfy the matching criteria.

3.1 On-Screen Colors

The L* a* b* values were calculated directly from the sample spectral data for the given illuminant and observer7. At thesame time, we verified that all sample values are within the gamut of the display for the given illuminant. For illuminantD65, 7 of the 25 samples were out of gamut, for illuminant F2, 4 samples were out of gamut, and for illuminant A 14samples were out of gamut. Samples that were out of gamut were not included within the specific illuminant sample setfor this study.

Using the GretagMacbeth Eye-One♦ instrument, we measured the displayed representation of colors on the screen. TheL* a* b* for the given illuminant were processed through the corresponding profile to obtain the required R G B values.We obtained spectral values from the displayed representation and calculated corresponding L* a* b* values. Thecorrelations between sample L* a* b* values and the display measured L* a* b* values are shown in Table 3.1-1.

♦ Eye-One is a registered trademark of GretagMacbeth Company, New Windsor, NY 12553-6148



Table 3.1-1: Pearson Correlation Coefficients for L* a* b*11

L* a* b*

D65 0.9997 0.9977 0.9991F2 0.9996 0.9976 0.9964A 0.9993 0.9964 0.9511

The quantity of the sample set is reduced for illuminant A because many samples were out of gamut; however, thesample set is still large enough to be significant. This relationship is also shown in the following figures 3.1-1 through3.1-3 for illuminant D65. The other illuminants showed similar results.

Figure 3.1-1: Correlation on L*

Display L* as a function of Sample L*

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Figure 3.1-3: Correlation on b*Display b* as a function of Sample b*

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From these results we conclude that the color representation displayed on screen very closely matches the original colorfor in-gamut colors. The corresponding CIE ∆E calculations show 2.57 for D65, 3.19 for F2 and 3.85 for illuminant A.Again confirming that the color on display is very close to the actual specimen color for in-gamut colors.

3.2 Color Matching

Initial setup assumed approximately equal brightness level in the display and the viewing booth. However, most of thedisplayed representations appeared much too bright compared to the specimen in the viewing booth. We adjusted thebrightness level in the viewing booth until a subset of the sample set showed a brightness match. We measured thisbrightness with a spotmeter, and compared to the same spotmeter measurement off the display.

Under illuminant A more samples were out of gamut than for the other illuminants. Since the blue component of theilluminant is reduced, see Figure 2.5-1, this is to be expected. However, on screen measurements of samples in gamutshowed excellent agreement.

We found that it was necessary to provide a white reference in the immediate field of view of the observer. This allowedthe observer to adapt chromatically to their “white point” so that there was an appropriate visual reference. Weaccomplished this by adding a white frame to the display around the projected color of the specimen. The width of theframe is adjustable as is the relative intensity of the border. This provides the correct white reference for our humanvisual system that allows correct visual observations. We also added a white frame to the sample holder in the viewingbooth. Implementation in the booth consisted of a platform to hold the specimens surrounded by the white reference.Unlike on the display, the size or relative brightness of this white is not adjustable. We positioned the sample by raisingand tilting it relative to the bottom of the viewing booth to minimize reflections from its floor.

In assessing the visual match, we compared each sample represented on the display to its object and assigned a valuefrom 1 being perfect to 3 being not an acceptable commercial match. Later, we added values of 1.5 for near perfect, and2.5 for not acceptable. Observers 1 and 2 viewed specimens on a different display unit than observers 3, 4, & 5. Alldisplays are the same model, produced by the same manufacturer.

Table 3.2-1 presents the results obtained for 5 observers under illuminant D65. Samples shown in bold received a scoreof a poor match to a score of a not acceptable match. Even though the Yellow HPA is listed as out of gamut forobservers 1 and 2 it was in gamut on display 2 for observers 3, 4 and 5.

Observers 1 and 2 were located in CA and 3, 4 and 5 in GA, viewing the samples on a different but same model display,and similarly using a different but same model-viewing booth. This study is known as multiple operator precision.

The data shows that on the average there is very good agreement between observers. The general difference between theEast Coast, GA, and West Coast, CA, observers was later found to be caused by the East Coast viewing booth beingapproximately 300 degrees Kelvin warmer than the West Coast viewing booth, measuring a little over 6700 degreesKelvin. Desired correlated color temperature is 6500 degrees, nominal. The West Coast booth measured a little over6400 degrees Kelvin, well within the acceptable tolerance of +/- 100 degrees12. Hence a better match was alwaysobserved in CA, and the samples in the viewing booth in GA always looked bluer than the displayed samples at acalibrated color temperature of 6500 degrees Kelvin.

Similar observations were also made under F2, with strong deviations from the nominal correlated color temperatureshown in Table 2.5-2. Measured color temperature was close to 3900 degrees Kelvin, again more than a nominaltolerance of 100 degrees. Again, samples judged unacceptable are shown in bold.



Table 3.2-1: D65 Observations Observer

Out of 1 2 3 4 5Color: Gamut:Beige 1.5 1.5 1.5 2 3Blue 2995C xBlue xBrown 1.5 1.5 1.5 1.5 2Brown Accent 2 2 2 1Butterscotch (brownish) 2 2.5 1.5 1.5 2.5Citrus xGreen - No Warp xGreen - Dark 2 2 3 2.5 2.5Green xGreyhound Gray 2 2 2.5 2.5 3Maroon 1 1 1 1Mauve 2 2 2 1.5Navy Blue 1.5 1.5 2 1Pink 1.5 1.5 1.5 1.5Red PMS# 485 2 2 2.5 1.5 1Royal Blue 1.5 1.5 1.5 2Sapphire Blue xSlate Gray 2 2 2 1Butterscotch (light) 2 1.5 2 1Dark Chocolate 1 1.5 2 1White 201 1.5 1.5 1.5 1White 2 2 2 1Yellow 1 1.5 2 1.5Yellow - Dark x 1.5 1.5 2 2

Total Score: 31.5 32.5 36 11.5 30.5

Average Score: 1.66 1.71 1.89 1.92 1.61

Corresponding data for F2 illuminant are shown in Table 3.2-2.

In this case we see generally good agreement between observers, but a bigger difference between the GA and CAobservers caused by the poorer match of the white point to nominal white point.



Table 3.2-2: F2 Observations Out of Observers:

Color: Gamut: 1 2 3 4 5Beige 1 1 1 2.5 3Blue 2995C 1.5 1.5 1.5 2.5Blue 2 1.5 1.5 2Brown 1.5 1.5 2.5 3 2.5Brown Accent 1.5 1 2.5 1.5Butterscotch (brownish) 1.5 2 2.5 1 2Citrus xGreen - No Warp 1.5 1.5 1.5 3 2.5Green - Dark 1.5 1.5 2.5 2.5 1.5GreenGreyhound Gray 2 2 2 3 1.5Maroon 1 1 2 2Mauve 1 1 2 2Navy Blue 1 1 1.5 1Pink 2 2.5 1 1Red PMS# 485 1.5 1.5 2 2 1.5Royal Blue 1.5 2 1.5 2Sapphire Blue 1 1 2 1Slate Gray 2 1.5 1.5 1.5Butterscotch (light) xDark Chocolate 1 1 1.5 2White 201 1.5 1.5 2 1.5White 1.5 1.5 1.5 1

Yellow xYellow - Dark x

Total Score: 29 29 36 17 35.5

Average Score: 1.45 1.45 1.80 2.43 1.78

4. CONCLUSIONS

Through accurate calibration and profiling of the display, we have shown that we can achieve very high correlationbetween the color of specimen observed in a viewing booth and the same specimen color displayed on a computerscreen. There is excellent colorimetric agreement between the single stimulus object color and the projected image(color) on-screen. The Pearson Correlation Coefficient is greater than 0.95 for all three illuminants. The judgment ofthe visual perceptual quality of the color and appearance comparison is excellent. All the specimens rated a perfect,score of 1, to nearly perfect, a score of 1.5, under all illuminants. There is excellent quantitative agreement in theobservations between the two groups of observers in CA and GA; although there is a distinct bias between the twogroups. The CA observers are slightly more liberal giving higher scores, while the GA observers have a tendency to bemore strict giving lower scores, most likely attributed to the deviations of their viewing booth from the nominal whitepoints. The combined evaluation by our observers stated that 85% of the observations were judged as perfect or nearlyperfect, i.e., having no perceived color difference between the object and the softcopy, and the remaining 15% of theobservations were judged as being an acceptable match.



There is still some work to be done. We continue to investigate methods and methodologies that will bring the viewingconditions fully in accordance with CIE recommendations. Second, the colors outside the gamut of the display deviceand their color representation on screen need further development. Finally, as expected, some color comparisons showslight appearance differences under different illuminants. Small colorimetric differences in the background and the colorof the immediate surround affect the color and appearance of the projected image. It is our assertion that thesedifferences are the primary contributors to the remaining observed color and appearance differences. We are completingthe evaluation of a chipset comprised of 100 chips that are distributed more tightly and uniformly throughout colorspace. These data will provide a more quantitative and exhaustive analysis enabling “perfection” of the comparison.

5. ACKNOWLEDGEMENTS

The authors would like to thank PolyOne Corporation in Suwanee, GA for their help generating samples and withobservations, and who truly made this project possible.

REFERENCES 1 D.L. MacAdam (1951) Quality of Color Reproduction. Proceedings of the Institute of Radio Engineers, 36, pages 468-485

2 Softcopy definition – Digital image processed by computers for high-resolution displays on color m monitors. SapceElectronic Warfare Lexicon, www. Sew-lexicon.com

3 “Recent Progress in Color Science” Compiled and Edited by Reiner Eschbach and Karen Braun, M. M. Hayhoe, TheEffects of Backgrounds on Sensitivity and Brightness, pp 6-8

4 Barlow & Mallon, 1982, from Color Appearance Models, Mark Fairchild, Pg 177-180, Addison Wesley Longman, Inc.

5 ASTM E- 2805, Test Method for the Hiding Power of Paints, ASTM International, 100 Barr Harbor Drive, WestConshohocken, PA 19428-2959

6 ASTM D-523, ASTM International, 100 Barr Harbor Drive, West Conshohocken, PA 19428-2959

7 ASTM E-308, Practice for Computing Colors of Objects by Using the CIE System, ASTM International, 100 BarrHarbor Drive, West Conshohocken, PA 19428-2959

8 International Standard ISO 3664, ISO Control Secretariat; 1, Rue de Varembe’, Case Postale 56, CH-1211, Geneva 20;Switzerland

9 Color Appearance Models, Mark Fairchild, Pg 177-180, Addison Wesley Longman, Inc.

10 Ishihara’s Test for Colour Deficiency, Kanehara & Co. Tokyo, Japan distributed by Graham Field, Inc. Hauppauge,NY 11788

11 Pearson Correlation Coefficient, E.S. Pearson & H.O. Hartley, 1966, Biometric Tables for Statisticians, CambridgeUniversity Press

12 ASTM D 1729 Standard Practice for Visual Evaluation of Color Difference of Opaque Materials, ASTM International,100 Barr Harbor Drive, West Conshohocken, PA 19428-2959



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