Developing the Federal 

Aviation Administration’s 

Requirements for Color Use in 

Air Traffic Control Displays

Jing Xing

Civil Aerospace Medical Institute

Federal Aviation Administration

Oklahoma City, OK 73125

May 2007

Final Report

DOT/FAA/AM-07/10

Office of Aerospace Medicine

Washington, DC 20591
NOTICE

This document is disseminated under the sponsorship 

of the U.S. Department of Transportation in the interest 

of information exchange. The United States Government 

assumes no liability for the contents thereof.

___________

This publication and all Office of Aerospace Medicine 

technical reports are available in full-text from the Civil 

Aerospace Medical Institute’s publications Web site: 

www.faa.gov/library/reports/medical/oamtechreports/index.cfm
i

Technical Report Documentation Page 

1. Report No. 2. Government Accession No. 3. Recipient's Catalog No. 

DOT/FAA/AM-07/10 

4. Title and Subtitle 5. Report Date 

Developing the Federal Aviation Administration’s Requirements for Color May 2007 

Use in Air Traffic Control Displays 6. Performing Organization Code 

 

7. Author(s) 8. Performing Organization Report No. 

Xing J 

9. Performing Organization Name and Address 10. Work Unit No. (TRAIS) 

FAA Civil Aerospace Medical Institute 

P.O. Box 25082 11. Contract or Grant No. 

Oklahoma City, OK 73125 

12. Sponsoring Agency name and Address 13. Type of Report and Period Covered 

Office of Aerospace Medicine 

Federal Aviation Administration 

800 Independence Ave., S.W. 

Washington, DC 20591 14. Sponsoring Agency Code 

15. Supplemental Notes 

Work was accomplished under approved task AM- BHRR522 

16. Abstract 

This report describes the materials we developed for the Federal Aviation Administration’s requirements for 

color use in Air Traffic Control (ATC) displays. While many color use guidelines and the Federal Aviation 

Administration’s Human Factors Design Standard (HF-STD-001) provide general information about how to 

choose color schemes in visual displays, the purpose of this document is for developers of ATC technologies 

and human factors practitioners to evaluate the use of color from the perspective of ATC operations. This 

document provides two checklists. The first is a “To-do” checklist to assess whether the use of a color is 

effective for its intended purpose of assisting ATC task performance. The second is a “Do-not do” checklist to 

assess whether the use of color introduces potential negative effects on performance. While the two checklists 

may not cover all color use issues, they are pertinent to performance and can serve as a baseline to 

qualify/disqualify color schemes in ATC displays. Developers and human factors practitioners are encouraged 

to reference these checklists for interface design and acquisition evaluation of ATC technologies.

 

17. Key Words 18. Distribution Statement 

 

 

Color, ATC Display, Checklist, Requirement, Human 

Factors

Document is available to the public through the 

Defense Technical Information Center, Ft. Belvior, 

VA 22060; and the National Technical Information 

Service, Springfield, VA 22161 

19. Security Classif. (of this report) 20. Security Classif. (of this page) 21. No. of Pages 22. Price 

Unclassified Unclassified 19 

Form DOT F 1700.7 (8-72) Reproduction of completed page authorized 

iii

ACKNOWLEDGMENTS

We sincerely thank Dino Piccione, Glen Hewitt, Allen Poston, Steve Cooley, and Carol Manning for 

their invaluable suggestions through this study. This project could not be done without their help and initial 

efforts. We thank Drs. Michael McAnulty, Vicki Ahlstrom, and Kenneth Allendoerfer for their comments 

on an earlier draft of this report. These comments substantially improved the quality and reliability of the 

report. Kenneth Allendoerfer also thoroughly tested the color use requirements presented in this report and 

provided lots of feedback on modifying the requirements and improving their usability. We also express 

our appreciation for the support from the following ATC facilities: ZDC, ZNY, ZNC, PHL tower, DCA 

tower, SFO tower, New York TRACON, Potomac TRACON, and PHL TRACON. We appreciate all the 

FAA reviewers and editors whose efforts greatly improved this report.

v

Contents

I. INTRODUCTION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

II. HOW TO USE THIS REPORT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

III. HOW TO USE COLOR IN ATC DISPLAYS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

IV. HOW TO USE COLOR EFFECTIVELY TO AID TASK PERFORMANCE . . . . . . . . . . . . . . . . . . . . 3

Task Purpose: Attention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Task Purpose: Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Task Purpose: Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

V. HOW TO AVOID POTENTIAL DISADVANTAGES OF COLOR USE . . . . . . . . . . . . . . . . . . . . . . . . 5

VI. CONCLUSIONS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

BIBLIOGRAPHY. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Appendix A. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

“To-Do” Checklist for Ensuring the Effectiveness of Color Use. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A-1

Appendix B. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . B-1

“Do-Not Do” Checklist for Avoiding Potential Disadvantages of Color Use. . . . . . . . . . . . . . . . . . . . . B-1

Appendix C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1

Measuring/Computing the CIE (International Committee of Illumination) . . . . . . . . . . . . . . . . . . . . . C-1

Chromaticity Coordinates of a Color. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C-1

Appendix D. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-1

Estimating Text Readability on Visual Displays. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-1

1

Developing the Federal Aviation Administration’s Requirements

for Color Use in Air Traffic Control Displays

I. INTRODUCTION

During the past decade, new technologies and automation tools in the air traffic control (ATC) environment 

have led to the increasing use of color displays. However, 

the Federal Aviation Administration (FAA) has not established any formal requirements for the use of color 

in displays or systematic methods to evaluate the benefits or disadvantages of color use. While many existing 

guidelines for the use of color provide some principles 

of how to apply colors in visual displays, most of them 

are largely concerned with color perception rather than 

the effects of color on task performance in a complex, 

dynamic environment like ATC. 

The intent of this report is to provide a systematic 

method to assess the benefits and potential disadvantages 

of color use from an ATC operational perspective. The 

method helps to ensure that the use of color in ATC displays enhances task performance and does not introduce 

undesirable safety risks. In 2004, site visits were conducted 

at nine ATC facilities (three Air Traffic Control Towers, 

three Terminal Radar Approach Control facilities, and 

three En Route Traffic Control Centers). At each of the 

nine facilities, two researchers 1) observed how controllers 

used color information to perform tasks, 2) identified 

color uses and their relevance to ATC tasks, and 3) determined potential problems and factors associated with 

color use. In addition, the advantages and disadvantages of 

using colors in their displays were discussed with facility 

representatives (controllers, supervisors, training managers, and quality assurance specialists). The discussions 

significantly contributed to the task of identifying color 

and visual factors that might aid or negatively affect ATC 

task performance (Xing & Schroeder, 2006a). 

Through the site observations, a good understanding was gained of how controllers use colors for their 

work. Colors were found to be used primarily for three 

purposes associated with ATC task performance: 1) to 

draw attention to urgent information, 2) to identify data 

categories, and 3) to segment complex scenes. Many color 

and visual factors pertinent to controllers’ task performance were also identified and then classified into two 

major categories: 1) factors that make the use of a color 

beneficial for a given purpose, and 2) factors that have 

the potential to negatively affect task performance. Based 

on these results and findings from cognitive and visual 

research, two checklists were developed to evaluate color 

schemes in ATC displays. The first checklist evaluates the 

effectiveness of a color in achieving the intended purpose; 

the second checklist evaluates the potential disadvantages 

of color use.

This report is based on information derived from 

ATC operations and is intended as a general resource for 

both developers of ATC technologies and human factors 

practitioners in the evaluation of color use. The FAA’s 

standard practices document, BASELINE REQUIREMENTS FOR COLOR USE IN AIR TRAFFIC CONTROL DISPLAYS, is currently being developed from 

the materials presented in this report. When completed, 

the standard practices document will serve as the official 

guide for vendors of ATC technologies.

Although we used the literature from the visual and 

color research domains to provide a rationale for the 

checklists, this report is not intended to serve as an introduction to color science. More information about how 

color works and how to use color in general visual displays 

can be found in many color use guidelines (Reynolds, 

1994; Cardosi & Hannon, 1999; Ahlstrom & Longo, 

2003; Narborough-Hall, 1985; Van Laar, 2001, NASA 

Color Usage Webpage, 2004). 

II. HOW TO USE THIS REPORT

Throughout this report, “shall” and “should” statements that are common in design specifications will be 

used. A “shall” statement refers to a described, testable 

condition that must be met. A “should” statement refers 

to a condition that is not completely testable but is recommended. Most “shall” and “should” statements in this 

report are accompanied by a paragraph of explanation 

that provides additional information about the statement 

and the related scientific literature. 

The main body of this report is presented in the following three sections. Section III requires that the use 

of a color in an ATC display shall be associated with 

a purpose that assists ATC task performance, and the 

section describes the primary purposes of attention, 

identification, and segmentation. Section IV requires 

that the use of a color shall be effective for its given 

purposes and describes the conditions under which the 

use of color is effective. Section V requires that the use 

of a color shall not introduce potential disadvantages to 

ATC performance and describes the conditions under 

which the use of color can limit performance and then 
2

pose safety risks. To meet the color use requirements for 

ATC displays, all colors used in a display shall comply 

with the statements specified in each of the three sections. In addition, short versions of Sections IV and V 

are provided in the formats of a “To-Do” and a “Do-Not 

Do” checklist in Appendices A and B.

iii. HOW TO USE COLOR IN ATC DISPLAYS

Color shall be used only when it is associated with 

a purpose that aids task performance. The use of color 

in ATC displays is typically associated with one of the 

following three purposes: attention, identification, or

segmentation.

Attention – A salient color is used to immediately 

direct users’ attention to alert or emergent information. 

In this application, a color generally means “urgent, critical, immediate attention is needed.” The effective use 

of color means that the colored target can automatically 

capture a user’s attention or “pop-out” from a complex 

display (Pop-out means that a visual target can be effortlessly detected irrespective of the amount of local visual 

materials). 

Controllers need to instantly detect the onset of critical 

information in ATC displays, so a target that represents 

critical information needs to immediately become obvious so as to capture attention. This phenomenon is called 

“pop-out’ in vision research. Pop-out of colored targets 

in complex scenes is extremely efficient and desirable 

(Treisman & Gelade, 1980). Pop-out is especially useful 

when targets need to be detected in large displays, because 

targets located in the peripheral visual field can be quickly 

brought to the fovea for detailed inspection. 

Identification – A set of colors is used to identify 

data categories when data are presented dynamically, 

intermingled, or distributed in an irregular way on a 

display. In this application, each color is associated with 

a specific meaning. The effective use of color for identification means that searching by color for an item of 

a given category among many other items can be done 

effortlessly, accurately, and reliably. 

While our sensory system can perceive a large amount 

of information, our cognitive system can only process 

a few pieces of information at a time. Therefore, an 

efficient way to handle a large amount of data is to classify data into categories and denote the categories with 

visual attributes that can be easily identified. Such tasks 

are called identification. Color is often the most effective 

search attribute for difficult visual identification tasks; 

searching for data of a given category by color can be 

effortless, accurate, and reliable (Christ, 1975). Effortless

often implies quick, as when the time required to find 

an item does not depend on the total number of items 

displayed. Reliable means that selection is consistent and 

dependable. Accurate means that, statistically, the correct item is always selected and the incorrect item is not 

selected. The superiority of color to achromatic cues is 

more evident as the visual scenes become more complex 

or the difficulty of identification tasks increases (Sachtler 

& Zaidi, 1992). 

Segmentation – Different colors are used to organize information by segmenting a complex scene into 

distinctive visual objects. In this application, a color is 

not necessarily associated with a meaning. It is merely 

used to integrate or differentiate data. The effective use 

of color for segmentation means that data displayed in 

the same color appear as a visual object separated from 

other data so that a user will know where to look for 

related information. Such visual objects can be either 

spatially continuous regions (such as an image) or spatially 

discontinuous patterns (such as a figure).

The human visual system organizes a complex scene into 

meaningful objects. To appreciate what and where particular 

information is present, the visual inputs are organized by a 

filtering procedure that has been termed segmentation (Julesz, 

1965; Pinker, 1984). Segmentation means that some visual 

inputs are integrated as one object by a common visual attribute, and that object is represented differently from other 

objects. Segmentation tasks include regional segmentation 

and pattern segmentation. Regional segmentation involves 

segmenting a spatially continuous region from its surrounding materials; pattern segmentation involves integrating 

some spatially discontinuous items into one object and 

segmenting them from other displayed items. Segmentation 

is crucial when using an automation system with a cluttered 

display and varying task demands. In complex displays like 

those used in ATC, color is more effective and is processed 

faster than achromatic cues for segmentation (Treisman & 

Gelade 1980; Nothdurft, 1993).

Other than the three primary task-related purposes of 

attention, identification, and segmentation, color is occasionally used to label data. This application is typically used in 

the situation where many categories of data are displayed 

irregularly and intermingled across a display, yet the available 

space is insufficient to label the data with text. Therefore, 

colors are used to label the data and a look-up table is 

provided to indicate the meaning of each color. The use of 

color for this purpose appears similar to that for identification. However, the colors in this application are only for 

labeling, not for controllers to efficiently and reliably search 

the data included in a given category. Thus, as long as the 

colors are visually distinguishable and a look-up table is 

provided, they can adequately serve the purpose. Because 

the application of color for data labeling is rare and easy 

to use, this kind of color use is not included in Section IV, 

“How to effectively use color to aid task performance.” 
3

IV. HOW TO USE COLOR EFFECTIVELY 

TO AID TASK PERFORMANCE – A 

“TO-DO” CHECKLIST

A color can be mathematically specified by its luminance and chromaticity parameters (see Appendix C for 

details). Visual research has demonstrated that the effectiveness of color for a given task depends on luminance, 

chromaticity, the number of colors used, and other factors. 

This checklist was developed by integrating visual research 

results with ATC color application requirements.

The checklist is organized with respect to the three 

primary task purposes of color use in ATC displays. 

For each purpose, shall and should statements are used 

to describe the conditions required for the color use to 

be effective. To utilize this checklist, first determine the 

purpose of a color usage and then evaluate the conditions for that purpose. Only when all the conditions for 

a given purpose are met can the use of color be effective. 

If a color is used for more than one purpose, then all the 

conditions under those purposes need to be met for the 

color being effective. A short version of this checklist is 

presented in Appendix A. 

Task Purpose: Attention

The following conditions shall be met for a color to 

be effective in capturing attention.

1) Luminance: The apparent luminance of the

target that requires attention shall be no less

than the luminance of other displayed data

(called distracters) in the view field. 

The luminance of the target that requires attention 

should be high enough so that the target remains salient 

even when controllers reduce the screen brightness. 

The pop-out effect depends on the salience of a visual 

target relative to the distracters. Brightness (i.e., luminance) contributes to salience more than color; thus, 

a brighter target among dimmer distracters can easily 

pop-out while a dimmer target among brighter distracters 

fails to pop-out, regardless of its chromaticity (Li, 1999; 

Treisman & Souther, 1985). In addition, high luminance 

is desirable for mission-critical targets in an ATC work 

environment because the targets need to be visible and 

salient even when controllers reduce the brightness of 

their computer screens. 

The apparent luminance is the luminance of a visual 

object perceived by the eyes. It can be estimated as the 

area of the object multiplying the physical luminance. 

For instance, the apparent luminance of a text box filled 

with a color is about 5 times higher than the apparent 

luminance of the text of the same color because text only 

takes about 20% of the area. 

2) Luminance difference or chromaticity difference: The apparent luminance of the target

shall be at least 20 cd/m2 greater than that

of distracters. Alternatively, the absolute

chromaticity difference between the target

and distracters shall be greater than 0.24 in

InternationalCommittee of Illumination (CIE) 

uniform chromaticity coordinates (seeAppendix

C for details). 

Either the luminance difference between a target and 

distracters or the chromaticity difference between them 

can make the target pop-out from distracters. Nagy and 

Sanchez (1992) found that the minimal luminance difference for a target to capture attention was about 20 cd/m2 

for small targets (0.5~1.5 degree view angle) presented in 

large displays. Alternatively, the chromaticity difference 

between the target and distracters has to be greater than 

40~60 times the color discrimination threshold. Notice 

that the chromaticity difference can produce pop-out 

only when the luminance of the target is no less than 

that of the distracters. 

3) Number of distracter colors: The number of

distracter colors shall be fewer than five.

For the best effect, the number of distracter colors 

should be minimized to no more than two or three.

Color is effective in inducing pop-out when other 

materials of comparable or larger sizes in the view field 

are composed of no more than two or three colors. 

With increasing variation of distracters, target salience 

decreases and the pop-out effect diminishes (Treisman 

& Gelade, 1980).

Task Purpose: Identification

The following conditions shall be met for a set of 

colors to be effective in identifying data categories.

1) Color naming: The colors used to identify

datacategories shall bereliably and consistently

named.

Basic colors should be chosen for the purpose of 

identification. 

In the ATC environment, identification of data categories is usually performed at different spatial locations and 

times. Therefore, the task of using color for identification 

is essentially the task of color naming in which controllers 
4

associate the data with specific color names and search 

for the color of the given name from memory. Color 

research has found that 11 basic colors can be reliably 

and consistently named across populations of different 

geographic regions and different cultures. Those are red, 

green, yellow, blue, pink, brown, purple, orange, and 

three achromatic terms, black, white, and gray (Boynton 

& Olson, 1990). These colors are maximally separated in 

the color space. In addition, magenta and cyan are also 

among the consistently namable colors. Visual experiments have demonstrated that basic colors work better 

than non-basic colors when identifying data categories 

in complex scenes and for complicated tasks (Smallman 

& Boynton, 1990; Guest & Van Laar, 2002). 

2) Chromaticity difference: If non-basic colors

are used for identification, the chromaticity

differences between the colors shall be greater

than 0.04 in CIE uniform coordinates. 

The chromaticity difference between the colors should

be significant enough for each color to be distinctively 

named.

The minimal chromaticity difference for colors to 

be distinctively named is about 10 times the color discrimination threshold (Boynton, MacLaury, & Uchikawa, 

1989; Poirson & Wandell, 1990). If the chromaticity of 

two colors is too close, the similarity in the two colors 

interferes, making it difficult to use the color name as 

the criterion to search the data in a category. 

3) Number of colors: The number of colors used

to identify data categories shall be fewer than

seven.

Visual experiments have demonstrated that color has 

no advantage over achromatic cues for identification when 

the number of colors used is greater than 6~7 (Carter, 

1982). Furthermore, using more than six colors for identification can result in an increase in errors of omission 

when users perform multiple tasks (Cummings, Tsonis, 

& Xing, 2007). 

4) Luminance difference: The luminance difference between the colors shall be less than 20 

cd/m2.

The luminance difference between the colors should

be minimized so that none of the colors is much brighter 

than the others.

When the luminance difference between two colors is 

greater than 20 cd/m2, the data displayed in the brighter 

color can pop-out to capture users’ attention (Nagy & 

Sanchez, 1992). Therefore, a bright color becomes a 

distraction when users search for the data displayed in 

the dimmer color. Consequently, users tend to miss the 

data included in that category, and the dimmer color 

becomes ineffective for identification. When the levels 

of importance of data categories need to be emphasized, 

it is better to manipulate color hues rather than luminance. For instance, when one of the data categories 

has a higher priority than other categories and needs to 

be pointed out to controllers, yellow or red-orange are 

often assigned to the category because those colors have 

a hue close to red.

5) Luminance: The luminanceofthecolors should

be high enough so that the colored data can be

reliably detected even when controllers reduce

their screen brightness. 

Controllers can adjust the brightness of their displays. 

As a result, some low-luminance colors may become invisible. It is important for the developers of ATC technologies 

to be prepared for such circumstances. At present, the 

FAA has no requirement for the lower-limit of controllers’ 

brightness adjustments (Ahlstrom & Arend, 2005). 

Task Purpose: Segmentation

The following conditions shall be met for a color to 

be effective in segmentation.

1) Luminance ratio or chromaticity difference: For

regional object segmentation, the chromaticity

difference between the object and its surrounds

shall be greater than 0.004. Alternatively, 

the luminance ratio, defined as the absolute

luminance difference between the object and

surrounds divided by the luminance of the

object, shall be greater than 5%. For pattern

segmentation, the color difference between the

pattern and its surrounds shall be greater than

0.012. Alternatively, the luminance ratio shall

be greater than 15~20%.

Both chromaticity and luminance differences between 

two visual regions or patterns can result in segmentation; 

however, chromaticity is more effective than luminance. 

Theoretically, segmentation can be achieved when the 

chromaticity difference is greater than the color discrimination threshold, which is estimated as 0.004 in 

CIE uniform chromaticity coordinates and at which 

two filled areas placed side-by-side can be reliably discriminated (Wyszecki & Fielder, 1971). However, it takes 

about three times the discrimination threshold for two 

spatially discontinuous patterns to be reliably segmented. 
5

The effect of luminance in segmentation is determined 

by the ratio of the luminance difference to the baseline 

luminance of the object to be segmented. The threshold 

ratio is about 5% for regional segmentation and 15~20% 

for pattern segmentations (McIlhagga, Hine, Cole, & 

Snyder, 1990). 

2) Luminance difference: When the color difference is sufficientforsegmentation, the luminance

difference shall remain the same for data that

are of equal importance. 

The luminance difference between data of equal importance should be minimized so they appear to have 

the same visual salience. 

Because the salience of a visual object increases with its 

luminance, having the luminance levels of the displayed 

data consistent with the levels of data importance can 

help to organize data priorities. On the other hand, less 

important data (such as background information) may 

be integrated and segmented by less brighter colors. 

3) Number of colors: The object to be segmented

shall consist of no more than two colors unless

the object is composed of high-density textures

of various colors. 

A region consisting of two or more colors is likely to 

be confused with other regions made up of many colors 

(Julesz, 1965); thus, it may not be reliably segmented 

from others.

V. HOW TO AVOID POTENTIAL 

DISADVANTAGES OF COLOR USE – 

A “DO-NOT DO” CHECKLIST

The visual and cognitive factors underlying the negative instances of color use in ATC displays were analyzed 

and classified. Eight factors, with which the use of color 

has potential disadvantages for task performance, were 

identified. Those are: low text readability, lack of an effective redundant cue, color complexity, excessive coding

sets, experience interference, uncorrelated coding, loss of

integration, and distraction. The conditions for how to 

prevent the factors from occurring in ATC displays were 

identified by integrating the findings in the literature with 

ATC color applications. These conditions are included 

in this “Do-not do” checklist of color use. A shall or 

should statement is used to describe the condition for 

each factor. The potential consequences of the presence 

of a factor are also described. To avoid safety risks, the 

use of a color in an ATC display shall be checked for 

the presence of each factor and meet all the statements 

provided. See Appendix B for a short version of the “Donot do” checklist.

1) Low text readability: The luminance contrast between the text and background shall

be greater than the threshold contrast (30%) 

for error-free reading.

Potential consequence: Low text readability increases 

reading difficulty and may cause reading errors. 

Readability is the property that permits a user to easily 

read text on a screen irrespective of meaning (Legge, Rubin, 

& Luebker, 1987). It is primarily determined by the luminance contrast between the text and its background colors. 

Luminance contrast can be calculated as the difference 

between the text and background luminance divided by 

the sum of the luminance. Therefore, zero contrast means 

that the text and background have the same luminance, 

and 100% contrast describes the maximum luminance 

contrast such as the contrast produced by the combination of white and black. The text readability is trimmed 

to zero when both the text and background luminance is 

very low (typically less than 10 cd/m2 for many computer 

monitors), regardless of the luminance contrast (Krebs, 

Xing, & Ahumada, 2002). Several experimental studies 

have demonstrated that readability decreases linearly with 

luminance contrast. In particular, readability deteriorates 

significantly below 20~30% contrast (Legge et al., 1987; 

Scharff & Ahumada, 2002). Furthermore, Krebs et al. 

(2002) found that for a given monitor the minimum text 

size for error-free reading varied with text contrast when 

the contrast was less than 20%; while the minimum size 

was a constant within the contrast range of 20-100%. 

Based on these and many other studies, the threshold 

contrast for error-free reading is set at 30%. Appendix 

D describes how to calculate the text contrast from the 

color specifications of a text and its background. 

2) Lack of effective redundant cue: The use of

color for attention or identification shall be

accompanied with effective redundant cues. 

Potential consequence: Lack of effective redundant 

cues means that color is the only cue to draw attention 

or identify data categories. Lack of effective redundant 

cues may result in loss of color-coded information when 

colors are viewed by color-vision-deficient users, from 

off-axis angles for flat panel displays, or under strong 

ambient light. 

About 8~10% of all males have various types of color 

vision deficiencies, yet the FAA’s current color vision 
6

standard for air traffic control applicants allows individuals with some degree of color vision deficiencies to enter 

the workforce (based on a demonstrated ability to pass a 

job-related practical test; Mertens, Milburn, & Collins, 

2000). In addition, displayed colors tend to be distorted 

or washed out when viewed from off-axis angles for flat 

panel displays or under strong ambient light. Therefore, 

when a color is used for attention or identification, achromatic cues should be available, in addition to color, to 

encode the information as a redundancy. It is important 

that redundant cues are effective for the given purpose.

Table 1 lists the effectiveness of some frequently used 

redundant cues for different task purposes, where “E” 

means that the redundant cue is as effective as color, “L” 

means that the cue is less effective compared to color, 

“NE” means that the cue is not effective for the task, 

and “NA” means that the cue is not applicable for the 

task. For attention, typical effective cues include flashing 

/ blinking and luminance; effective cues for identification

include spatial location, distinctive shape, and text (Xing 

& Schroeder, 2006b).

3) Color complexity: The number of colors used

to identify data categories in a single display

mode shall be fewer than seven. 

Potential consequence: Using colors for identification

places additional demands on working memory. Moreover, 

exceeding the working memory capacity (about seven 

items) can cause users to make more omission errors 

when performing multiple tasks.

Using colors to identify data categories can reduce 

the time needed to search for the data and increase 

performance accuracy. However, to take the advantages 

of color, color names must be related to their assigned 

meanings, which burdens the working memory. Since 

the number of items that can be maintained in working 

memory is limited (about 7 ± 2, Miller, 1965), it is not 

surprising that color has no advantages over achromatic 

visual cues when more than six color categories are used 

(Carter, 1982). Furthermore, in situations where color is 

used to identify data categories while users also perform 

other tasks such as planning or monitoring, using more 

than six colors results in an increase of omission errors 

in the other tasks (Cummings et al., 2007).

4) Excessive coding sets: A display shall use no

more than three sets of colors to identify data

categories. 

Potential consequence: Multiple sets of color-coding 

can increase cognitive workload and cause misinterpretation of information. 

An ATC display typically contains multiple data 

groups. For example, a radar display may have datablocks 

as one data group and weather information as another 

group. Each data group can contain several categories 

of data. While identifying the categories of one or two 

data groups by color may help to search the data of a 

given category, there is no clear advantage of using color 

when multiple sets of color-coding are used (Yuditsky, 

Sollenberger, Della Rocco, Friedman-Berg, & Manning, 

2002) because remembering the color meanings and 

switching between the sets increase cognitive workload; 

and because users tend to mistake the meanings of the 

colors in the less-frequently-used color sets. In addition, 

using multiple coding sets is often accompanied with 

another negative color factor, uncorrelated coding: one 

color for multiple meanings. 

5) Experience interference: The use of a color

shall not conflict with its reserved meanings, 

according to the color use conventions in ATC

displays. 

Potential consequence: Interference occurs when the 

meaning of a color conflicts with controllers’ previous 

experiences and may result in misinterpreting information and increasing the cognitive workload required to 

comprehend color meanings. 

Table 1. Effectiveness of Redundant Cues Relative to Color for Various Tasks 

Task Flashing Location Luminance Shape Size Text 

 

Attention Ea Lb E NEc NE NE 

 

Identification NAd E NE L NE L 

 

Segmentation NA L L L L NE 

a

E = redundant cue is more effective than or at least as effective as color b

L = cue is less effective compared to color 

c

NE = cue is not effective for the task 

d

NA = cue is not applicable 
7

There are some conventions about color use in ATC 

displays, and controllers have acquired reserved color 

meanings associated with those conventions through 

experience. If a color use conflicts with its reserved 

meaning, what is in the experience and the perceived 

information may interfere with each other, and the perceived information can be biased by experience (Stroop, 

1935). The following is a list of conventions for color 

usage in ATC displays:

Red is reserved to draw attention to emergency or 

alert messages.

Yellow is reserved to identify a target or data category 

that needs caution.

Orange, purple (or magenta), and cyan (or turquoise) 

are typically used to identify data categories; green, 

white, and black are often used to identify the category 

of the normal status.

Non-basic colors, especially those in the green-blue 

domain (e.g., green-blue, gray-blue, and yellowishgreen) are the typical choices for segmentation. 

6) Uncorrelated coding: When used for attention

oridentification, acolorshallbe fullycorrelated

with a specific meaning in task performance; 

and information of the same category shall be

represented by the same color. 

Potential consequence: Uncorrelated color-coding 

causes coding uncertainty, which may decrease the accuracy of identifying data in a category and cause misinterpretation of color-coded information. 

Color has significant advantages in identification 

tasks only when it is fully correlated with the information it denotes. If a color is only partially correlated 

with the content of information, then a user cannot 

use the color as the unique selection criterion for decision-making; therefore, the color has no advantage over 

achromatic attributes (Christ, 1975). Moreover, such 

color-coding may cause performance errors because 

it introduces uncertainty to the signal processing. By 

the theory of signal detection, increasing uncertainty 

of the selection criterion leads to a higher probability 

of decision errors. 

7) Loss of integration: Data that need to be

momentarily related for successful task performance should be displayed in the same or similar

colors. 

•

•

•

•

Potential consequence: Displaying two sets of data 

in different colors reduces instant information integration between the data sets because a user is less likely to 

recognize the relationships between pieces of information 

when they are depicted in different colors. 

The brain processes color information with two 

mechanisms: integrating pieces of information depicted 

in the same color and segregating information depicted 

in different colors. Segregation and integration are like 

the opposite ends along an axis in that a higher level of 

segregation is always accompanied with a lower level of 

integration, and vice versa. The brain processes different 

colors separately, and it has to intentionally make an extra 

effort to integrate information represented by different 

colors. Thus, the brain is less likely to automatically associate pieces of information displayed in different colors 

compared to those displayed in the same color, especially 

when the data are displayed dynamically. This lack of 

association poses a risk in tasks where data in different 

categories need to be simultaneously related.

8) Distraction: Multiple salientcolorsthat need

attention shall not be onset simultaneously in

a single display mode.

Potential consequence: The onset of a salient target 

causes distraction, which may decrease the detection of 

other significant events and result in loss of information 

even if the events are visually salient and are in the focus 

of view. 

Attention to a target implies withdrawal from other 

data to effectively deal with the target. Since the ability 

to attend to visual inputs is limited, attending to a salient 

target in a complex scene acts as a distraction for other 

inputs in the scene. Therefore, the perception of other 

inputs is reduced. Hence, what facilitates attention is 

also the source of distraction. In extreme circumstances, 

the pop-out of a salient target can induce a phenomenon 

known as “inattentional blindness,” which means when 

focusing attention on a salient object, one often fails to 

notice other salient objects or events occurring simultaneously (Simons, 2000; DiVita, Obermayer, Nugent, & 

Linville, 2004). Visual experiments have demonstrated 

that salient targets capture attention and enter into the 

awareness one at a time; other targets may be ignored 

(Schmidt, Vogel, Woodman, & Luck, 2002). Therefore, 

the use of color for the purpose of attention should be 

limited and applied with caution. 
8

VI. CONCLUSIONS

This report presents the materials for the development 

of the FAA’s requirements for color use in ATC displays. 

The report describes two checklists. A “To-do” checklist 

assesses whether the use of a color is effective for its intended purpose so that it aids ATC task performance; a 

“Do-not do” checklist assesses whether the use of color 

introduces potential negative effects on ATC performance. These two checklists were based on the observation and analysis of color use in current ATC displays 

to ensure that they are task-oriented and are specifically 

applicable to ATC. One advantage of the checklists is 

their easy-to-use format. A human factors practitioner 

can quickly go through the checklists for each color in 

a display and qualify or disqualify the color use. Finally, 

recall that the materials in this report were intended to 

serve as a reference to ensure the proper use of color in 

future ATC technologies. Therefore, this report does not 

provide detailed information about how to choose a color 

palette for a display and how to optimize the effects of 

color. Such information can be found in other color use 

guidelines ((Reynolds, 1994; Cardosi & Hannon, 1999; 

HF-STD-001, 2003; Narborough-Hall, 1985; Van Laar, 

2001, NASA Color Usage Webpage, 2004).

VII. BIBLIOGRAPHY

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March, 2007).

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control displays. Proceedings of Human Factors and

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Boynton RM, MacLaury R, Uchikawa K (1989). Centroids of color categories compared by two methods. 

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Boynton RM, Olson CX (1990). Salience of chromatic 

basic color terms confirmed by three measures. 

Vision Res; 30(9): 1311-7. 

Cardosi K, Hannon D (1999). Guidelines for the use 

of color in ATC displays. Washington, DC: Federal Aviation Administration; No: DOT/FAA/AR99/52.

Carter RC (1982). Visual search with color. J Exp Psychol;

8: 127-36.

Christ RE (1975). Review and analysis of color coding research for visual displays. Hum Factors; 7: 

542-70.

CIE (1931). Proceedings of the 8th Session, International

Commission on Illumination, pp: 19-29. Bureau 

Central de la CIE, Paris. 

CIE No. 15.2 (1986). Colorimetry, 2nd ed., Commission 

Internationale de 1’Eclairage, Vienna, Austria.

Cummings ML, Tsonis C, Xing J. Investigating the use 

of color in timeline displays. Washington, DC: 

FAA Office of Aerospace Medicine; FAA Technical 

Report (In press). 

DiVita J, Obermayer R, Nugent W, Linville JM (2004). 

Verification of the change blindness phenomenon 

while managing critical events on a combat information display. Hum Factors; 46(2): 205-18. 

Guest S, Van Laar D (2002). The effect of name category 

and discriminability on the search characteristics of 

colour sets. Perception; 31(4): 445-61.

Julesz B (1965). Texture and visual perception. Sci Am;

212(2): 38-48.

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predicting the readability of a monitor. Proceedings

of the 46th annual meeting of Human Factors and

Ergonomics Society; 46: 1659-63.

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of reading--V. The role of contrast in normal vision. 

Vision Res; 27(7): 1165-77. 

Li Z (1999). Contextual influences in V1 as a basis for 

pop out and asymmetry in visual search. Proc Natl

Acad Sci USA; 96: 10530-5.

McIlhagga W, Hine T, Cole GR, Snyder AW (1990). 

Texture segregation with luminance and chromatic 

contrast. Vision Res; 30(3): 489-95. 

Mertens HW, Milburn NJ, Collins WE (2000). Practical 

color vision tests for air traffic control applicants: 

en route center and terminal facilities. Aviat Space

Environ Med; 71(12):1210-7.

Miller, G. A. (1965). The magical number seven, plus or 

minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81-97. 

Nagy AL, Sanchez RR (1992). Chromaticity and luminance as coding dimensions in visual search. Hum

Factors; 34(5): 601-14. 
9

Narborough-Hall CS (1985). Recommendations for applying colour coding to air traffic control displays. 

Displays; 131-7.

NASA Color Usage Webpage (2004). http://colorusage.arc.nasa.gov (Internet address as of March, 

2006).

Nothdurft HC (1993). The role of features in preattentive 

vision: Comparison of orientation, motion, and 

color cues. Vision Res; 33(14): 1937-58. 

Pinker S (1984). Visual cognition: An introduction. 

Cognition; 18: 1-63. 

Poirson, AB, Wandell BA (1990). Task-dependent color 

discrimination. J Opt Soc Am A; 7(4): 776-82.

Reynolds L (1994). Colour for air traffic control displays. 

Displays; 15: 215-25.

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signals in visual memory. J Opt Soc Am A; 9(6): 

877-94. 

Scharff LF, Ahumada AJ Jr (2002). Predicting the readability of transparent text. J Vision; 2(9): 653-66. 

Schmidt BK, Vogel EK, Woodman GF, Luck SJ (2002). 

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visual working memory. Percept & Psychophysics;

64: 754-63. 

Simons, DJ (2000). Attentional capture and inattentional 

blindness. Trends Cognit Sci; 4: 147-55.

Smallman HS, Boynton RM (1990). Segregation of basic 

colors in an information display. J Opt Soc Am A;

7(10): 1985-94. 

Stroop JR (1935). Studies of interference in serial verbal 

reactions. J Exp Psychol; 18: 643-62.

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diagnostic for preattentive processing of separable 

features. J Exp Psychol; 114(3): 285-310. 

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vision standards. Part I: Status of color use in ATC 

displays and demography of color-deficit controllers. Washington, DC: FAA Office of Aerospace 

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AM-06/02. 

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A-1

Appendix A

“To-Do” Checklist for Ensuring the Effectiveness of Color Use

The checklist is organized with respect to task purposes. The elements in Table A-1, from left to right, are task 

purpose, color and visual factors contributing to the effectiveness of color use, conditions with which color use is 

effective for the intended purpose, and checkboxes that would be filled with “Yes” if a condition is met or “No” otherwise. To use this checklist, first determine the task purpose of a color. For each purpose, several factors contribute 

to the effectiveness of color use. When all the conditions for a given purpose are met, the use of color is effective for 

that purpose. 

Table A-1. A “To-do” checklist for ensuring the effectiveness of color use

Task

purpose 

Visual factors Conditions for color-use being effective Yes

/ No 

Luminance The luminance of the color-coded target shall be equal to 

or greater than that of distracters (i.e., other materials 

displayed in the visual field). 

Luminance or 

chromaticity 

difference 

The luminance difference between the color-coded target 

and distracters shall be greater than 20 cd/m2

.

Alternatively, the chromaticity difference between the 

color-coded target and distracters shall be greater than 

0.24 in CIE uniform chromaticity coordinates. 

Attention

Number of 

distracter 

colors 

The number of distracter colors shall be fewer than five. 

Color naming The colors used for identification shall be reliably and 

consistently named. 

Chromaticity 

difference 

The chromaticity differences between non-basic colors 

shall be greater than 0.04 in CIE uniform coordinates. 

Number of 

colors 

The number of colors used to identify data categories 

shall be fewer than seven. 

Luminance 

difference 

The luminance differences between colors shall be less 

than 20 cd/cm2

.

Identification 

Luminance The luminance of colors should be high enough to be 

reliably detected when screen brightness is reduced. 

Luminance 

ratio or 

chromaticity 

difference 

For regional object segmentation, the chromaticity 

difference between the object and its surrounds shall be 

greater than 0.004. Alternatively, the luminance ratio

(defined as the absolute luminance difference between the 

object and surrounds divided by the luminance of the 

object), shall be greater than ~5%. For pattern 

segmentation, the color difference between the pattern 

and its surrounds shall be greater than 0.012. 

Alternatively, the luminance ratio shall be greater than 

15~20%.

Luminance 

difference 

When the color difference is sufficient for segmentation, 

the luminance difference shall be kept roughly the same 

for data that are of equal-importance. 

Segmentation 

Number of 

object colors 

The number of colors of the object to be segmented shall

be no more than two unless the object is composed of 

high-density textures of different colors. 

B-1

Appendix B

“Do-Not Do” Checklist for Avoiding Potential Disadvantages of Color Use

The first column lists the negative factors or potential disadvantages associated with the use of color in ATC 

displays. The second column lists some potential consequences of the presence of each negative factor. The third 

column lists the conditions to prevent the negative factors from occurring. The last column consists of checkboxes 

that can be filled with “Yes” if the condition is met and “No” otherwise. A “No” indicates that the use of color has 

potential negative effects on task performance.

Table B-1. A “Do-No Do” checklist for avoiding potential disadvantages of color use

Negative 

factors 

Potential disadvantages Conditions to avoid the 

disadvantages 

Yes

/No

Low text 

readability

Reducing reading speed; 

increasing reading errors. 

The luminance contrast between the 

text and background shall be greater 

than the threshold contrast (20~30%) 

for error-free reading. 

Lack of 

effective 

redundant 

cue

Increasing chances of losing 

or misinterpreting information. 

The use of color for attention or

identification shall be accompanied 

with effective redundant cues. 

Color 

complexity 

Increasing cognitive workload; 

reducing speed of information 

interpretation; increasing 

chances of misinterpreting 

information.

The colors used to identify data 

categories in a single display mode 

shall be fewer than seven. 

Excessive 

coding sets 

Increasing chances of missing 

information; users tend to 

ignore color-coding. 

A display shall use no more than 

three sets of colors to identify data 

categories. 

Experience 

interference 

Increasing cognitive workload; 

increasing chances of 

misinterpreting information. 

The use of color shall not conflict 

with the reserved meanings of color 

use conventions in ATC displays. 

Uncorrelated 

coding 

Slower and less accurate in 

identification compared to 

using text or symbols alone. 

Data identified by a color shall be 

completely correlated with a specific 

meaning in task performance. In 

particular, one color shall not be 

used for multiple purposes, and 

multiple colors shall not be used to 

represent the same information. 

Loss of 

integration 

Less likely to associate pieces 

of information that are color 

segmented. 

Dynamic data that need to be 

simultaneously bonded for successful 

task performance should be 

displayed in the same or similar 

colors. 

Distraction Decreased detection of other 

significant events; loss of 

information

Multiple salient colors that need 

attention shall not be onset 

simultaneously in a single display 

mode.

C-1

Appendix C

Measuring/Computing the CIE (International Committee of Illumination) 

Chromaticity Coordinates of a Color

A computer monitor generates a color through three phosphor channels: red, green, and blue. The amount of 

phosphors emitted from a channel is specified with 8-bit digital values of the channels: r, g, and b, each for red, green, 

and blue phosphors. Computer programmers use these three numbers to specify a color on displays. For example, 

rgb values of 255, 0, 0 represent red, and 255, 255, 0 are yellow. While rgb values specify the physical attributes of 

a color on a monitor, they do not tell how viewers perceive the color. 

The International Committee of Illumination (CIE) defined color chromaticity coordinates to describe human 

color perception (CIE, 1931). In this definition, a color can be specified by three variables: L, x, and y, where L is 

the luminance of a color while x and y determine the hue; x and y vary between 0 and 1. For example, the typical 

values for white in a computer monitor are x=0.3300 and y=0.3515; the values for red are x=0.6340 and y=0.3337. 

The xyL values of a color surface can be measured with a colorimeter. 

The relationship between rgb and xyL values can be specified with a nonlinear transformation and a linear matrix 

transformation, as described by the following equations:

R=(r/255)gamma

G=(g/255)gamma

B=(b/255)gamma 

Where the parameter gamma describes the nonlinearity of luminance response for a given monitor. It usually varies 

in a range of 1.8-2.5 for CRT monitors, with a typical default value of 2.2. The transformation between RGB values 

and CIE chromaticity coordinates (xyL) are determined by the following equations: 

X=40.9568*R + 35.5041*G + 17.9167*B;

Y=21.3389*R + 70.6743*G + 7.98680*B;

Z=1.86297*R + 11.4620*G + 91.2367*B;

And, 

x=X/(X+Y+Z)

y=Y/(X+Y+Z)

L=Y

Notice that the parameters in these transformations vary from monitor to monitor. The numbers in the equations 

above are typical default values for CRT displays. The process of determining the parameters of the transformations 

for a given monitor is called color calibration. 

One of the greatest disadvantages of the CIE chromaticity systems is that visually the coordinates are not equally 

spaced. Thus, distortions occur in attempting to relate perceived colors to locations of the CIE chromaticity diagram. Based on the xyL systems, the CIE adopted the Lu ́v ́ uniform chromaticity coordinates that were more nearly 

uniformly spaced with respect to color perception (CIE, 1986). Therefore, the chromaticity difference between two 

colors can be computed as ((∆u ́) 2 + (∆v ́) 2)

1⁄2. The values of u ́ and v ́ can be computed from x and y through two 

non-linear equations: 

 u ́ = 4x / (-2x+12y+3)

 v ́ = 9y / (-2x+12y+3)

D-1

Appendix D 

Estimating Text Readability on Visual Displays

Text readability is determined by the luminance contrast of the text and background colors. There are several definitions of luminance contrast. The one that is most commonly associated with text readability is Michelson contrast, 

defined as (Lt- Lb) / (Lt+ Lb), where Lt is the text luminance and Lb is the background luminance. The contrast varies 

between 0 and 1, or 0 to 100%. The contrast zero means that the background color is the same as the text color; 

thus, the text is completely invisible. The 100% contrast corresponds to the maximum luminance contrast that can 

be achieved in an ideal display. The contrast threshold for error-free reading is typically taken as 20~30%. Below we 

provide step-by-step procedures to measure or estimate text readability.

Step 1: Obtain the text luminance Lt and background luminance Lb

Three procedures are provided to comply with different situations. Users may choose the one that is most suitable 

to their needs.

Procedure 1A: This procedure works for the situation where users 1) know the rgb values of the colors, 2) have 

a colorimeter or luminance meter to measure luminance, 3) are capable of generating a color patch on the given 

computer monitor, and 4) intend to get the accurate text readability for a given text.

Step 1A.1: Set up the room lighting similar to that of the work environment;

Step 1A.2: Generate a square patch uniformly filled with the text color (specified by the rgb values) on the center 

of the screen while keeping the rest of the screen black (specified by r=g=b=0).

Step 1A.3: Hold the colorimeter at users’ typical view distance and measure the luminance of the patch. That is 

the Lt.

Step 1B.4: Repeat 1A.2 and 1A.3 with the text background color to obtain Lb.

Procedure 1B: This procedure works for the situation where users 1) know the rgb values of the text and background 

colors, 2) have a colorimeter or luminance meter to measure luminance, 3) have done color calibration of the monitor 

and so as to obtain the gamma value of the monitor, and 4) do not want to spend time measuring luminance values 

for every individual text / background color.

Step 1B.1: Set up the room light similar to that of the work environment;

Step 1B. 2: Generate a patch uniformly filled with the color (255, 0, 0) on the center of the screen while keeping 

the rest of the screen black (specified by r=g=b=0).

Step 1B. 3: Hold the colorimeter at users’ typical view distance and measure the luminance of the patch. That is 

the value for Lr.

Step 1B.4: Repeat 1B.2 and 1B.3 with the color (0, 255, 0) to obtain the value Lg;

Step 1B.5: Repeat 1B.2 and 1B.3 with the color (0, 0, 255) to obtain the value Lblue;

Step 1B.6: Set the full screen at r=g=b=0 and measure the luminance as the value L0

Step 1B.7: Using Lr, Lg, Lblue, L0, and gamma values to compute the luminance for any given text color specified 

with rgb values by the following equation: 

Lt= Lr *(r/255) gamma + Lg *(g/255) gamma + Lblue * (b/255) gamma + L0

The same equation can be used to calculate the background luminance Lbwith the rgb values of the background 

color.

Procedure 1C: This procedure works for the situation where users don’t have any measurement equipment so they 

can only estimate the luminance of the colors.

Step 1C.1: Try to find the color parameters (gamma, Lr, Lg, Lblue, and L0 ) for the monitor, where Lr, Lg, Lblue, and

L0 are the screen luminance measured at the rgb values: (255, 0, 0), (0, 255, 0), (0, 0, 255), and (0, 0, 0) respectively. 

Gamma is the non-linearity parameter of a monitor. These parameters vary from monitor to monitor.

Step 1C.2: Using Lr, Lg, Lblue, L0 and the gamma value to compute the luminance for any given color specified 

with rgb by the following equation: 

Lt= Lr *(r/255) gamma + Lg *(g/255) gamma + Lblue * (b/255) gamma + L0
D-2

This equation allows you to calculate the text luminance Ltwith the rgb values of a text color and the background 

luminance Lbwith the rgb values of the background color. 

In the worst case where none of the color parameters is available, here are some default values used for typical 

CRT displays:

Lr = 21.3389, Lg =70.6743, Lb =7.98680, and gamma= 2.2, while L0may vary from 3 cd/m2 to 12 cd/m2 depending on the ambient light.

Step 2: calculate the text contrast 

Text contrast (the Michelson contrast) is defined as (Lt- Lb) / (Lt+ Lb), where Lt is the text luminance and Lb is the 

background luminance. The contrast varies between 0 and 100%. 

When both background and text colors are very dark, the text readability is determined by the luminance difference between the text and background, rather than the contrast. Therefore, we use the following equations to 

approximate the text contrast C:

C= Lt- Lb) / (Lt+ Lb), if Lt > 10 cd/m2 or Lt >10 cd/m2

Else

C=0 

Step 3: Compare the text contrast with the threshold error-free-reading contrast (recommended as 30% in this 

report). 

If C >= 30% (or 0.3), the text readability meets the requirement;

If C < 30%, the text readability does not meet the requirement.