Sensing Ambient Light for User Experience-Oriented ColorScheme Adaptation on Smartphone Displays

Jiadi Yu, Jiaming ZhaoDepartment of ComputerScience and Engineering

Shanghai Jiao Tong University{jiadiyu,

jmzhao}@sjtu.edu.cn

Yingying ChenDepartment of Electrical and

Computer EngineeringStevens Institute of

Technologyyingying.chen@stevens.edu

Jie YangDepartment of Computer

ScienceFlorida State Universityjie.yang@cs.fsu.edu

ABSTRACTWith the rapid development of information technology, mo-bile devices have exhibited increasing popularity in recentyears. To support the anytime-anywhere service model ofmobile devices, one important problem related to the screendisplay arises when using these devices (e.g., smartphoneand tablet) under various lighting conditions. On one hand,it is hard for users to see the display clearly under stronglighting conditions (e.g., sunlight). On the other hand, thescreen appears dazzling under weak lighting conditions. Thisproblem related to the mobile device display can signifi-cantly degrade user experience and undermine the successfuldeployment of the anytime-anywhere mobile service model.Existing solutions mainly focus on the automatic adjust-ment of brightness level under different light conditions. Weshow that merely utilizing brightness level to solve the dis-play problem is not enough to maintain the user experienceunder both strong and weak lighting scenarios through ex-perimenting with over 200 volunteers. In this work, we takea different approach by investigating automatic color schemeadjustment to improve user experience. We find that Read-ability, Comfort level and Similarity are major factors thatcontribute to user experience. In recognizing these prob-lems, we propose a system, ColorVert, which utilizes theDKL color space to adaptively transform color schemes bysensing ambient light to improve user experience under var-ious lighting scenarios. Our experimental evaluation withover 200 precipitants and various mobile devices demon-strates that ColorVert is more effective in both maintain-ing as well as improving user experience compared with theexisting automatic brightness adjustment system.

Categories and Subject DescriptorsH.5.2 [INFORMATION INTERFACES AND PRE-SENTATION]: User Interfaces—Input devices and strate-gies

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DOI: http://dx.doi.org/10.1145/2809695.2809709 .

General TermsDesign, Experimentation, Human Factors

KeywordsSmartphone display; color scheme; user experience; sensingambient light

1. INTRODUCTIONMobile devices such as smartphones and tablets have re-

ceived increasing popularity over the recent years and blendedinto our daily lives. In particular, equipped with high reso-lution screens and various web browsers, modern mobile de-vices have become one of the major sources for web surfing.According to the survey on the Internet, 250 million smart-phones were sold in the 3rd quarter of 2013 alone[1]. Com-pared with traditional desktops or laptops, one importantproblem related to the screen display is surfaced when usingthese devices under different lighting conditions for support-ing the anytime-anywhere mobile service model. That is,the user experience of screen reading is strongly associatedwith ambient environments, among them the most impor-tant factor is the lighting conditions. On one hand, understrong lighting conditions (e.g., sunlight), some text colorsare washed off by the strong light and become undistinguish-able from the background color, rendering unreadability. Onthe other hand, the dazzling screen often makes our eyesuncomfortable at night. Thus, to ensure the successful de-ployment of the anytime-anywhere mobile service model, itis highly desirable to develop a scheme that can adaptivelyadjust the way the screen displays the content based on thedynamic ambient environments.

Existing studies on improving smartphone user experienceunder different light conditions mostly focus on the adjust-ment of the brightness level. For example, Android and iOShave automatic brightness adjustment system, which canchange the brightness level of the screen according to thelight strength. And there are subsequent studies to improvesuch systems by capturing more precise light strength [2].However, these solutions are far from satisfactory as usersremain suffering under both strong and weak lighting con-ditions even if the highest brightness level is applied. Thus,merely adjusting the brightness level of the device screencannot solve the problem completely. In this paper, we takea different viewpoint by developing adaptive color schemeadjustment to maintain and improve user experience underdifferent light conditions. Although there exist some work

309

to improve user experience by adjusting color saturation ofimages and videos[3] or enhancing image contrast[4, 5] ac-cording to ambient light, it is hard for these solutions toachieve the high readability for texts under different lightconditions. Most of the studies related to color schemetransformation [6] [7], however, only focus on the energyconsumption of different color schemes rather than the userexperience.

Since 84% of users utilize their smartphones for web surf-ing[8], we focus our study on web browsers. Although theuser experience of mobile devices has gained much atten-tion, existing work has not concerned the impact of thewebpage color scheme to user experience. We first set outto investigate the main factors that contribute to user ex-perience when using smartphones. Through an extensivestudy of over 200 volunteers and the analysis of webpagecolor schemes, we find that the user experience is mainlydetermined by Readability, Comfort level and Similarity be-tween the transformed color scheme and the original one.We further propose ColorVert, which improves user experi-ence by adaptively adjusting the color scheme of web pagesbased on sensed ambient light under dynamic environments.Specifically, ColorVert applies a novel color transformationalgorithm to calculate the optimal color scheme under bothstrong (e.g., direct strong sunlight) as well as weak lightingconditions based on ambient light sensing. Our prototypeimplementation of ColorVert on Android-based smartphonesand tablets verifies the feasibility of using ColorVert in realenvironments. Based on the feedback from over 200 vol-unteers, our system could effectively improve user experi-ence compared with the existing brightness adjustment sys-tem. Although our work only focuses on dealing with webbrowsers, we believe the proposed color transform adap-tation approach could be easily extended to handle othersmartphone tasks using screen displays.

We highlight our main contributions as follows:

• We empirically investigate the impact factors on userexperience when using mobile device under differentlighting conditions with over 200 volunteers. The im-portant finding is that the user experience is mainlydetermined by the readability, comfort level and sim-ilarity between the transformed color scheme and theoriginal one.

• We develop a dynamic color scheme adaptation sys-tem for smartphone web browsers, namely ColorVert,which can adaptively transform color scheme of web-pages to maintain and improve user experience throughsensing ambient environments.

• We implement ColorVert on four types of Android de-vices including smartphones and tablets with differentscreen sizes. Our prototype of ColorVert shows thatour system is light weight and is feasible to use in real-world scenarios.

• We conduct extensive experiments with over 200 vol-unteers to evaluate the performance of ColorVert. Theresults of users’ feedback confirm that our ColorVertsystem is effective in improving user experience underboth strong and weak lighting conditions.

The rest of this paper is organized as follows. We give anin-depth motivation study in Section 2. Section 3 and Sec-tion 4 presents the design details and color transformation

algorithm of the ColorVert system respectively. In Section5, we evaluate the performance of ColorVert and analyze theresults. Related work is reviewed in Section 6. Finally, wegive conclusive remarks in Section 7.

2. MOTIVATION STUDYWe first provide the background on readability and com-

fort level of the smartphone screen. Then through the in-vestigation of the existing automatic brightness adjustmentsystem on smartphones and the color scheme of webpages,we find the reason of user experience degradation under bothstrong and weak lighting conditions. Finally, from the re-sults of user preference survey, we discover main factors thataffect user experience.

2.1 PreliminariesWe first introduce the background of the smartphone screen

readability and comfort level.

2.1.1 The measurement of ReadabilityPhotometry is the science about the measurement of visi-

ble light in terms of its perceived brightness to human vision.The most important physical quantity given in photometryis luminance[9]. Luminance(cd/m2) describes the amount oflight passes through a particular area with a given angle.

Given the smartphone is equipped with OLED displays,there is an effective way to calculate the luminance of screen.Since OLED consists of three kinds of light emitting diode,the total spectral power distribution is the addition of thoselight sources, specified by an (R,G,B) tuple in the RGB colorspace. We can first calculate the relative luminance [9][10] ofa color, defined as Y = 0.2126R+0.7152G+0.0722B, whereY is the relative luminance, with its value normalized to 1for a given white point (when R=G=B=1). We can observefrom the formula that green light contributes the most tothe luminance, while blue light contributes the least.

Further, we are not only concerning about the luminanceof a certain object, but also its luminance difference com-pared with another object, i.e. the Luminance Contrast:

Contrast% =|L1 − L2|

max(L1, L2)· 100%, (1)

where L1 and L2 are the respective luminance of the objects.Based on the above information, Relative Visual Perfor-

mance (RVP) model is introduced to measure the readabil-ity[11][12]. It is measured by the speed and accuracy withwhich a visual task is performed. Specifically, RVP is mea-sured using Reaction Time (RT) which defines as the inter-val between the onset of the stimulus and the response byhumans. Human can reach different RT values given a setof different characteristics of the stimulus. RVP is formallydefined by:

RV P% =RT0

RT· 100%, (2)

where RT0 is the smallest reaction time a person can achieve,given the best lighting condition and stimulus characteris-tics. RT is the reaction time of a certain condition. A biggerRVP value indicates we spend less time to get the informa-tion of the stimulus and results in a better readability con-dition, whereas a smaller RVP value means less readability.Usually, a RVP value greater than 80% implies good read-ability [13][14][11].

310

Figure 1: Influence of automatic brightness controlon RVP and UGR.

2.1.2 The measurement of Comfort LevelWhen the difference of luminance level between the tar-

get and the environment becomes too large, our eyes feeluncomfortable when looking at a target. To evaluate thecomfort level, we use the standard metric provided by In-ternational Commission on Illumination, the Unified GlareRating (UGR)[15][16].

UGR = 8log0.25

Lb

∑

n

(L2

nωn

p2n), (3)

where Lb is the ambient luminance, Ln is the luminance ofeach light source n, ωn is the solid angle of the light sourceseen from the observer and pn is the Guth position index,which depends on the distance from the line of sight of theviewer. By using the UGR, we could analyze the impact ofthe light emitted by the screen of the smartphone on oureyes. Normally, the standard office lighting has UGR ≤ 19.A UGR value larger than 25 may make our eyes uncomfort-able[15].

2.2 Investigating Existing Automatic Bright-ness Adjustment System

Since we cannot see the screen clearly under strong light-ing conditions and feel the screen is too dazzling at night,many smartphone operating systems (Android and iOS forexample) perform automatic brightness control, which dy-namically adjusts the smartphone brightness level based onthe ambient light strength. In this section, we show thatthe automatic brightness adjustment system has limited im-

Table 1: The details about the volunteers.

Sex Age UnderSunlight

UnderNight

Total

Male 20-30 32 21 9930-50 25 12over 50 6 3

Female 20-30 40 21 10730-50 18 17over 50 7 4

Total 128 78 206

Figure 2: Bright color ratio, RVP and UGR valueof different webpages.

provement on user experience under strong and weak light-ing conditions.

2.2.1 User Experience under Strong Lighting Con-dition

Since the strong light reflected by the smartphone screencan decrease the luminance contrast and wash off the colorof texts, it is often hard for us to see the words clearly.As existing work employs automatic brightness adjustmentsystem to improve user experience, we investigate its effec-tiveness under direct strong light (sunlight) of 20000cd/m2

with four kinds of mobile devices: Motorola XT928 with a4.5’ screen, Samsung Galaxy Note with a 5.3’ screen, Nexus7with a 7’ screen and Galaxy Note with a 10.1’ screen. Weimplement the tool Psychopy[17] on smartphones to set upexperiments to measure the RVP and UGR value of web-pages. For Samsung Galaxy Note smartphones, besides au-tomatic brightness adjustment system, we also apply theadapt display mode [3] on our experiments, which is able toautomatically adjust color saturation according to ambientlight.

To measure RVP value via the Equation 2, the smallestreaction time (RT0) can be calculated from the formula in[11][12]. For Reaction Time (RT), we do experiments thesimilar as [11][12]. We recruit 206 volunteers of differentages randomly on college campus. For 128 of them, smart-phones are placed under direct sunlight with ambient lumi-nance around 20000cd/m2 measured by light sensor. Forthe other people, smartphones are placed at night whereambient luminance is less than 2cd/m2. The details aboutthe volunteers is shown on Table.1. During each trial in theexperiment, a webpage is displayed on the screen of smart-phones, after a brief, random time delay, and each volunteeris asked to touch the screen as soon as they detect the newlydisplayed webpage. The time between webpage onset andresponse is recorded as the RT for that trial. The webpagesare selected from the sites most frequently visited by users.After the experiment, each volunteer has a block of RTs forall displayed webpages, and then we are able to calculatetheir RVP values for the webpages. Moreover, the UGRvalue of each displayed webpage can be calculated from theEquation 3 according to [15][16].

311

Figure 3: DKL Color Space.

The results are shown in the upper half of Fig.1. Fromthe figure, we can see that, under strong lighting condition,although the UGR value is low enough to keep a good com-fort level, the automatic brightness adjustment system onlyslightly improve the RVP value, which is still too low toachieve a satisfactory readability level (i.e., the RVP valueis much less than 80%). The adapt display mode of SamsungGalaxy Note do not much contribute to improve readabilitylevel for webpages.

2.2.2 User Experience under Weak Lighting Condi-tion

When ambient light strength is weak, as the ambient lumi-nance Lb in Equation 3 decreases, the UGR value increasessharply. A large UGR value would degrade the comfort levelfor reading. Although the automatic brightness adjustmentsystem could reduce the luminance level of the screen, theresults are still far from satisfactory. We perform similar ex-periments when the ambient light strength is low. From theexperiment results shown in the bottom half of Fig.1, theUGR value is still very large (i.e., UGR >19), which resultsin low comfort level. Moreover, under weak lighting condi-tion, the RVP value is high enough for good readability.

The reason of the large UGR value is that most of thewebpages use white or other bright colors as background.Even though the overall luminance level of the screen is re-duced, a bright background color still keeps Ln at a highlevel, which results in low comfort level.

2.3 Studying the Relationship between ColorSchemes and User Experience

Since existing brightness adjustment system is not goodenough to satisfy user, we study another aspect, color scheme,which has significant impact on user experience. In orderto understand the relationship between color schemes anduser experience, we conduct a survey on the color schemesof some popular webpages. We randomly select 1000 web-pages from popular websites such as Google, CNN, etc. It isnoticed that most webpages use bright colors in many placesin order to attract the attention of user. According to oursurvey results in Fig.2, 84% of the words on the CNN arelight grey and blue, 54% of the words on Google search re-sults page are sky blue and red. Because a lot of webpagesutilize bright color scheme, their readability or comfort levelare seriously undermined under both strong or weak lightingconditions.

On one hand, under strong lighting conditions, the brightcolor on the webpages results in the low RVP value. As isshown in the middle part of Fig.2, for most webpages understudy, RVP values are under 40%, whereas good readabilityhas RVP value larger than 80%. To fully understand theimpact of color on user experience, we introduce the DKL(Derrington, Krauskopf and Lennie) color space[18], which isbased on the Macleod and Boynton chromaticity diagram.The DKL color space is shown in Fig.3. DKL uses threeaxis to describe the color space: S-cone axis, L-M axis, andachromatic axis. For any given plane that is perpendicu-lar to the achromatic axis, all colors on the plane have thesame luminance value, i.e., luminance contrast is zero. Thebiggest plane which intersect the achromatic axis at center iscalled Isoluminant Plane. Every color is represented in the 3-dimensional space, i.e., (α,h,r), using spherical coordinatesthat specify the elevation(α) from the isoluminant plane, theazimuth(h) that stands for the hue, and the radius(r) that isthe fraction of the maximal modulations along the cardinalaxes of the space. By using the DKL color space, we caneasily distinguish the luminance effect from the color effect.It is thus convenient for us to analyze the effect of colors onuser experience. For a bright color scheme, both foregroundand background colors are located in the upper half of theDKL sphere, whose contrast is low, resulting in a low RVPvalue (i.e., poor readability).

On the other hand, under weak lighting conditions, thebright colors on the webpages cause the high value of UGR,which degrade the comfort level when reading. Our exper-iment about the bright colors’ influence on comfort level isshown in the bottom part of Fig.2. The UGR value is mea-sured when the ambient luminance is less than 2cd/m2. Itcan be seen that for most of the bright colors, UGR value isaround 30, which is larger than level of comfortable reading.The reason of the large UGR value is that bright colors keepLn in Equation 3 at a high level.

2.4 User Preference InvestigationFrom our above analysis, the existing automatic bright-

ness adjustment system is far from satisfaction of user ex-perience under both strong and weak lighting conditions.Meanwhile, we find user experience has a strong relation-ship with color schemes. We thus can transform the colorscheme of webpages to achieve a better user experience. Inthe study, the 206 volunteers mentioned in Section 2.2 areshown the same static webpages on smartphones with a se-ries of color schemes. For 128 of them, smartphones areplaced under direct sunlight with ambient luminance around20000cd/m2 measured by light sensor. For the other people,smartphones are placed at night where ambient luminanceis less than 2cd/m2. The volunteers are asked to score eachweb page by 1 to 10 with our grading system, in which thelevel one indicates that the color scheme results in the low-est user experience and the level increases as the user ex-perience improves. We select webpages from the sites mostfrequently viewed by user, and for each webpage we present3 color schemes:

• Original, remain the original color scheme of web-pages with existing automatic brightness adjustmentsystem.

• Inversion, replace all colors with their inversion color.Since most of the webpages use bright colors for back-

312

(a) strong light (b) weak light

Figure 4: User preference of different color schemes.

ground and foregrounds, use inversion can increaseRVP value and decrease UGR value.

• Black-White, for Black-White color scheme. Understrong lighting condition, the RVP value can reach themaximum when we use white for background and blackfor foreground. Under weak lighting condition, theUGR value can be significantly decreased when we useblack for background and white for words.

All transformations are applied to foregrounds and back-ground. The average scoring results and the RVP value un-der strong ambient light are shown in Fig.4(a), whereas theaverage scoring results and UGR value under weak ambientlight are shown in Fig.4(b). From the scores given by users,we obtain important user feedbacks. Under strong lightingconditions, the Original and Inversion color schemes can-not meet users’ requirement on readability, i.e., RVP valuesare low. Although the Black-White scheme can achieve thehighest RVP value among those transformation methods,the scores given by users are very low. This is because aBlack-White scheme is dull to users. Therefore, a high RVPvalue alone can not satisfy users. Moreover, although RVPvalues are low for the original color scheme, users give a rel-ative high preference score for it. The reason is that usersare used to the original color scheme of a webpage and if thecolor scheme is not similar to the original one, users mayfeel the webpage is strange and unsatisfactory. Thus, usersprefer the webpage to be similar to the original one. Un-der weak lighting conditions, users prefer the color schemewith a lower UGR value, like Black-White or Inversion colorscheme. This is because a lower UGR value means a bettercomfort level, which contributes to user experience improve-ment.

According to the analysis above, we find that the auto-matic brightness adjustment system is not satisfactory andcolor schemes have significant impact on user experience.User experience thus can be improved by transforming colorschemes. In particular, we notice that user prefer colorschemes with a high RVP value under strong lighting con-dition and a low UGR value under weak lighting condition.

In addition, users prefer color schemes which are similar tothe original one.

3. COLORVERT DESIGNIn order to build an efficient color scheme adaptation sys-

tem, we devise a new system, ColorVert, that improves userexperience by dynamically adjusting the color scheme ofwebpages based on the light strength sensed at the light sen-sor on smartphones. The workflow of ColorVert is shown inFig.5. There are three major units in our system: DynamicSensing Unit, Transforming Unit, Execution Unit. Firstly,a Sensing Unit senses the changes of ambient light strength.When ColorVert captures the change of light strength, itwill trigger the Transforming Unit where the optimal colorscheme for the present webpage will be calculated. Sec-ondly, Transforming Unit gathers useful information aboutthe webpage such as font size and colors from the web browserengine. Based on ambient light strength and the webpage in-formation, the Color Transformation Algorithm is employedto generate the best substitute color for both the backgroundand foregrounds of the webpage. Finally, the Executing Unitreceives the output of the algorithm and applies the newcolor scheme to display on the smartphone screen.

Dynamic Sensing: ColorVert can sense ambient envi-ronments and calculate the optimal color transformationscheme accordingly. Light sensor in smartphones can senselight events, which happens when ambient light strength hassignificant change. When ColorVert captures the change oflight strength, it will trigger the Transforming Unit wherethe optimal color scheme for the present webpage will be cal-culated. Since the light strength change leads to the changeof RVP and UGR value, it is possible that readability andcomfort level are degraded. ColorVert thus needs to cal-culate the optimal color scheme once the change of lightstrength happens.

Transforming: After ColorVert senses ambient environ-ments, the Transforming Unit first collects information fromboth the light sensor and the web browser, such as the am-bient light strength, original color scheme and font size ofthe webpage. Based on the collected information, ColorVert

313

Sensing Transforming Execution

AdjustingBackground

AdjustingForeground

Single ColorTransforming

Multiple ColorsTransforming

DOM Tree

Render Tree

Display

Painting

Font Size, Color

Am

bient Light

Execu

tion

Dyn

amic

Sen

sing

Op

timal

Co

lor S

chem

e

Stronglight

WeakLight

Web BrowserEngine

Figure 5: ColorVert design model.

employs Color Transforming Algorithm to calculate the op-timal color scheme for foreground and background respec-tively. Considering the contrast between foreground andbackground color of webpages, the algorithm calculates theoptimal color scheme of foreground and background respec-tively. Specifically, the color scheme transforming algorithmconsiders user readability, the comfort level and the simi-larity between transformed color scheme and original colorscheme. The calculated optimal color scheme thus can sig-nificantly improve user experience. The detailed algorithmis introduced in Section 4.

Execution: The final step is to apply the optimal colorscheme to Web Browser. Considering the working flow ofthe Web Browser Engine, after a webpage is successfullydownloaded, the Web Browser Engine first begin to parsethe file and construct a DOM tree. DOM tree contains thestyling information together with visual instructions in theHTML and CSS. Then, the DOM tree is used to construct aRender Tree. Finally, a Painting stage traverses the RenderTree and paints each node by calling a series of Libraryfunctions.

ColorVert is executed at the painting stage of the WebBrowser, after the Render Tree of a webpage is constructed.During the painting stage, ColorVert changes the interfacebetween Web Browser Engine and Library functions to sub-stitute the original color scheme with our optimal color scheme.Thus the painting functions paint the render tree nodes withthe specified colors.

4. COLOR SCHEME TRANSFORMING AL-GORITHM

In this section, we present the color transformation algo-rithms, which are used to calculate the optimal color schemein Transforming Unit. The optimal color scheme can signif-icantly improve user experience.

4.1 Criteria ConstructionAccording to our analysis in Section 2, user experience is

mainly determined by the following factors: RVP value (i.e.,readability) for strong lighting conditions, UGR value (i.e.,comfort level) for weak lighting conditions and Similaritybetween the original and transformed webpage.

1)RVP value: The mechanism of deciding RVP value iscomplex. In our experiment, we find that there are mainlythree factors affect RVP value: Luminance Contrast, ColorHue Difference and Font Size.

Luminance Contrast: In Section 2, we illustrate howto calculate the actual luminance contrast and RVP value.All colors on a plane of the DKL color space have the sameluminance value. The luminance contrast is the distanceof two planes. In order to exclude the effect of color hue,we select a pair of foreground and background color fromthe achromatic axis in Fig.3 (i.e., (900, 0, r)) to study therelationship between the luminance contrast and RVP value.The font size of words is fixed at 13px. We observe the wordsand record RVP values under different luminance contrastlevels between the foreground and background color (i.e.,different the r value). The results are shown in Fig.6 thatRVP is up to 80% when luminance contrast is equal to θ.It can be seen that when luminance contrast is bigger thanθ, there is no significant change in RVP value as luminancecontrast increases, while when the luminance contrast is lessthan θ, the RVP value decreases dramatically as luminancecontrast decreases. Therefore, when luminance contrast isbigger than a certain value θ, the RVP value can satisfy therequirement of readability.

Color Hue Difference: Besides luminance contrast, thecolor hue can also affect RVP value. In order to exclude theeffect of the luminance contrast, we select a pair of fore-ground and background colors from the isoluminant plane(i.e., (0, h, r) in the DKL color space, where h is the colorhue and r specifies the radius). The color hue difference ofa color pair is defined as |h1 − h2|, where h1 is the colorhue of the foreground color and h2 is the background colorhue. First we discuss the impact of r on RVP value. In thisexperiment, we fix h1 at 0 degree and h2 varies from 0o to360o. As the experiment results shown on the upper half ofFig.7, RVP value is the highest when the color hue differenceis equal to 180o, i.e. |h1 −h2| = 180o. The results also showthat the bigger the r value, the higher the RVP value, wherer = 1 indicates that the color is on the surface of the DKLcolor sphere. Next, we discuss the impact of h1 on RVPvalue. We set h1 as a series of values (0o, 60o, . . . , 300o).|h1 − h2| varies from 0o to 360o and r is fixed at 1. The re-sults are presented on the bottom half of Fig.7. The resultsshow that as long as the color hue difference of the colorpair remains the same, RVP value is similar among differentcolor hue h1. This indicates the color hue itself does not af-fect RVP value, what matters is only the color hue differencebetween the foreground and background.

Font Size: A bigger font size can make the words moreclear and improve user experience in reading. Font size isusually specified in CSS, with the unit px. Most words inthe ordinary webpages are within the range of 10-25px. Wefix the color hue difference at 1800 to discuss the relation-ship between the font size and RVP value. The experimentresults are shown in Fig.8. We can see that the change infont size results in the change of θ, i.e., the bigger the fontsize, the smaller the luminance contrast constraint θ.

2) UGR value: Under weak lighting conditions, we canimprove user experience by decreasing UGR value. Fromthe Equation 3, UGR value can be decreased by reducingthe total luminance of the screen Ln.3) Similarity: Similarity is another important factor

that contributes to user experience. In order to evaluatethe Similarity of a transformed color with the original one,we define the distance of two colors, color a and b, as thespherical distance of two points in the DKL space, repre-

314

Figure 6: Relationship betweenthe luminance contrast and RVPvalue.

Figure 7: Relationship between thecolor hue and RVP value.

Figure 8: Relationship betweenthe font size and RVP value.

sented as distance(a, b). The smaller distance(a, b) impliesa higher similarity.

4.2 Color Transformation under Strong Light-ing Condition

In this section, we discuss the color transformation al-gorithm under strong lighting conditions based on the cri-teria above. Given a webpage include foreground colors(x1, x2, . . . , xn) and a background color y, we aim to findan optimal color scheme that consists of foreground color(x′

1, x′2, . . . , x

′n) and an uniform background color y′.

4.2.1 Foreground Color TransformationThe foreground of a webpage is its various words that

contain different information. Firstly, we design an optimalsubstitution for a single color words. Since a webpage is nor-mally composed of words with different colors, we need tofurther deal with the problem of multiple foreground colors.With the criterion constructed, we determine the availablecolor pairs for foreground and background. Based on the cri-teria discussed in Section 4.1, when performing color trans-formation, we need to consider the following constraints:

• We should keep the luminance contrast of the fore-ground and background above θ.

• Color pairs of foreground and background should belocated on the surface of the sphere (r = 1), and theircolor hue difference should be 180o.

Since the bright color texts are easier to be washed offby the strong light, we select the foreground colors from thebottom half of the sphere (i.e., dark colors) in the DKL colorspace. In order to meet the the luminance contrast and colorhue constraints of the foreground and background, the upperhalf of the sphere is selected for background. We then cancalculate the Available Area, i.e., the colors in those areascan meet the constraints of the foreground and background.It is shown in shaded areas (surface spherical crowns) inFig.9. Also, the font size results in the change of the shadingarea. When the font size becomes bigger, the available areaalso becomes bigger, and vice versa.

Single Color: Considering the available area, our trans-formation method for a single color x is quite straightfor-ward. We just need to find out a color x′

1 in the availablearea which has the highest similarity with the original color.Fig.9 shows our transformation method. In order to keep

the similarity, the h value of the original color x1 is fixed.Firstly, we increase the r value of the x1 to x∗

1. Then, we de-crease the α value of x∗

1 until it goes into the available area.Finally, x′

1 is selected as new word color, and the other halfof the color pair y1 as background color. So for a singlecolor, we could effectively find a color pair that is optimal.

Multiple Colors: Most of popular webpages usuallyhave multiple bright colors on a single webpage. If eachcolor is transformed using the method above, it is possiblethat color collision could occur. Color collision means two ormore different colors in the original webpage are transformedto the same color. To illustrate this, we refer to Fig.9, whichshows a bright color x1 is transformed to x′

1 on a webpage.Suppose that the original color scheme of the webpage alsoinclude the color x′

1, or there is another color x2 which isalso transformed to x′

1 by the method above, a color colli-sion occurs. Usually, in webpages, different word colors areused to represent different kinds of information. If the colorcollision occurs, it is hard for users to distinguish them andunderstand what kind of information they are representing.Therefore, it is necessary to eliminate the color collision.

To avoid color collision, we first extend our definition ofSimilarity: similarity is not only associated with the distancebetween two colors, but also associated with their relativeweight that is the percentage of a given foreground color.The color with a higher percentage, i.e., bigger weight, hasa larger impact on the similarity. So we use the weighteddistance to evaluate the similarity of two color schemes.We assume that the original color scheme includes color(x1, x2, . . . , xn), and the new color scheme includes color(x′

1, x′2, . . . , x

′n) after transformation. wi in (w1, w2, . . . , wn)

represents the weight of the corresponding color. If xi isalready located in the available area before transformation,then we do not change xi and let x′

i = xi. The similaritycan be defined as:

1

WeightedDistance=

1∑ni=1 wi × distance(xi, x′

i). (4)

A smaller weighted distance indicates the color scheme ismore similar to the original one.

Further, we introduce a constrain to our transformationmethod, that is

{∀x′i, x

′j |distance(x′

i, x′j) > β, i �= j}, (5)

where β is a parameter that describes the color distance thatcan distinguish two kinds of colors. This constraint guaran-

315

Figure 9: Transformation method for single fore-ground color under strong lighting conditions.

tees that any two colors in the new color scheme must beseparated for a certain distance, so that any two word col-ors could be easily distinguished. In order to guarantee theconstraint, we set collision areas, which represent the sets ofpoints that have distances less than β with any already se-lected color. We select a color from remaining available area(that is, available area − collision area) with the largestsimilarity (i.e., min(WeightedDistance)). Apparently, inorder to maintain the largest similarity, the color selectedshould be on the boundary of the remaining available area.

With the two equations above, we could calculate the op-timal color scheme for multiple foreground colors. The al-gorithm is described in Algorithm 1. Since a color with abigger weight should have a higher transformation priorityaccording to the Equation 4, we first sort all the colors ac-cording to their weights. Then the first for loop can find outall the colors that do not need to transform and then updatethe remaining available area accordingly. In the second forloop, we deal with colors that need to change, i.e., select acolor with the largest similarity from the remaining avail-able area. Suppose that we totally maintain k points on theboundary of the remaining available area, the complexity ofthe loop is O(nk) . However, considering that there are usu-ally only a few kinds of colors in a webpage, k is much largerthan n, so the total complexity of the algorithm is O(k).

4.2.2 Background Color TransformationAbove work allows us to find an optimal color scheme for a

given webpage with many foreground colors. We can alwaysfind an optimal background color for a foreground color, e.g.,if x′

1 in Fig.9 is selected as foreground color, the other halfof the color pair y1 is selected as background color. This isbecause it can guarantee Color Hue constraints, i.e.,

|hforeground − hbackground| = 180o. (6)

Thus, many background colors will be selected for multipleforeground colors. However, users are used to a uniformbackground in a webpage for all foreground colors. So wepresent a background calculation algorithm to find an opti-mal uniform background color as follows.

We assume that (y′1, y

′2, . . . , y

′n) are the background colors

for multiple foreground colors. (x′i, y

′i) is a color pair that

meet our all constraints. Assume the color y′ is the optimalbackground color for the whole webpage, we should select a

Algorithm 1 Foreground Color Transformation

Require: A set of foreground colors (x1, x2, . . . , xn) in awebpage and their respective weight (w1, w2, . . . , wn)and font size (f1, f2, . . . , fn).

Ensure: The approximate optimal foreground colors(x′

1, x′2, . . . , x

′n) which can meet our constraint in Equa-

tion 5 and maximize the similarity in Equation 4.1: Sort (x1, x2, . . . , xn) according to their weight so that

their weight is in a decreasing order.2: Initialize the similarity sum of the colors as S3: Initialize the collision area as C4: for i = 1 → n do5: Calculate the Available Area Ai to the font size fi6: if Color xi is already in its available area then7: Let x′

i = xi

8: Let x′i as centre, β as radius, draw sphere SPi

9: Update the collision area as C = C ∪ SPi

10: end if11: end for12: for i = 1 → n do13: if Color xi is not in its available area Ai then14: Ai = Ai − C15: From the boundary of Ai select the color x′

i

which can minimize distance(xi, x′i)

16: Let x′i as centre, β as radius, draw sphere SPi

17: Update collision area as C = C ∪ SPi

18: Update S = S + wi × distance(xi, x′i)

19: end if20: end for

color that is the closest to all color (y′1, y

′2, . . . , y

′n) as y

′, i.e.,minimal weighted distance:

min(

n∑

i=1

wi × distance(y′, y′i)). (7)

Thus, from Equation 7, we can calculate a color y′ as theoptimal uniform background color.

4.3 Color Transformation under Weak Light-ing Conditions

In this section, we discuss the color transformation algo-rithm under weak lighting conditions. When we use oursmartphones at night, the luminance of ambient light Lb isvery low. A slightly increasing of Ln in Equation 3 thuscould rise the UGR value sharply, which makes our eye un-comfortable. In order to reduce the value of UGR, we shouldtry to reduce the luminance of our smartphone screen, i.e.,Ln.

4.3.1 Background Color TransformationSince the background color occupies most of the screen

and contributes to most of the luminance, it is importantto first find a background color with a low luminance level.Taking this fact into consideration, black is the best color touse as background at night, because its luminance is zero,which is in accordance with the luminance level of the am-bient environment. Black color can significantly reduce theUGR value and makes our eyes quickly adapt to the displayscreen, so user experience is improved.

316

Figure 10: Transformation method for single fore-ground color under weak lighting conditions.

4.3.2 Foreground Color TransformationFor foreground, we need to consider color transform algo-

rithm for single color and multiple colors. There are someconstraints that we need to take into consideration for theforeground color transformation under weak lighting condi-tions.

• Readability of a webpage: we should make sure thatthe RVP value is above a certain value χ.

• Similarity between the original color and transformedcolor: the new color should be selected from the samehue axis of the DKL color space, i.e., h = h′.

Single Color: Given the criteria above, we first focuson RVP requirement. RVP can be improved by increasingthe luminance contrast. Fig.10 shows our transformationmethod. If there is a foreground color x1 with RV Px1 <χ, we first move the color x1 along the line L. If RVPvalue requirement cannot be satisfied when it reach x′

1, wecontinue moving the color alone the arc L′ by increasing theα value of the color till we find a color x′′

1 with RV Px′′1≥ χ.

For any foreground color xi with RV Pxi ≥ χ, we do nottransform the color.

We further discuss the transformation method for an achro-matic color. Achromatic colors, especially black, usually oc-cupy a large percent of total words in a webpage. Sinceachromatic colors do not belong to a certain color hue, itcould be moved in any directions. Thus, we need to findout a certain direction, i.e., a certain color hue that couldmeet the requirement of RVP value as well as maintainingthe lowest luminance. An experiment is performed in or-der to find the optimal color hue. In our experiment, weselect black as background of the webpage and x(α, h, r) forforeground. According to the results presented in Fig.7, wefix r = 1 and let h equals to a series of values. For eachvalue of h, color set (α, h, 1) is a longitude on the surfaceof the sphere. Then from α = −90o, we gradually increasethe value of α, until the RVP value of the word exceeds therequirement χ. The results are shown in Fig.11. Firstly, thecolor we select meets the RVP requirement. So according toour experiment above, for each color hue hi, we guaranteethat α ≥ αimin (α is the minimum that can meet our RVPvalue requirement). Secondly, we should keep UGR valueas low as possible. Since α represents the luminance of acertain color, the bigger the α is, the higher luminance the

Figure 11: Color hue difference and their minimumα value.

color has. To keep the lowest possible UGR, we should selectthe color hue with a smallest αimin , which is green (h= 90o)according to the experiment results. Therefore, under weaklighting conditions, green can meet RVP value requirementwith the lowest α and is the best substitution for achromaticcolors.

Multiple Colors: For multiple colors, it is possible thatconflict can occur when performing color transformation.We already discuss the solution to color conflict under stronglighting conditions on Section 4.2. Similarly methods are ap-plied under weak lighting conditions.

5. EVALUATIONIn this section, we first present the prototype implemen-

tation of ColorVert and then we evaluate ColorVert underboth strong and weak lighting conditions respectively. Afterthat, we discuss the transformation time and overhead ofColorVert.

5.1 Prototype ImplementationWe implement ColorVert as an Android App and install

it on four mobile devices with different CPU frequency andscreen size, i.e., Motorola XT928 with a 4.5’ screen and a1.2GHz dual core CPU, Samsung Galaxy Note with 5.3’screen and a 1.4GHz dual core CPU, Nexus 7 with a 7’screen and a 1.3GHz quad core CPU, Galaxy Note 10.1tablet with a 10.1’ screen and a 1.4GHz quad core. Col-orVert senses the light strength condition by using the lightsensor embedded on the mobile devices. According to thelight sensor’s reading, ColorVert calculates the optimal colorscheme and executes it on the mobile devices. Fig.12 andFig.13 show two examples of the original and transformedwebpages performed by ColorVert under strong lighting con-ditions with various lighting strengths. We observe that inthe transformed webpages bright colors become darker asthe strength of the sunlight increases, while the similarityof the two webpages is preserved before and after applyingColorVert. We then present two examples of transformedwebpages performed by the inversion color scheme and Col-orVert respectively in Fig.14 and Fig.15 under weak lightingconditions. We find that although the background color ofboth schemes is adapted to black so that UGR value is re-duced, the foreground colors from ColorVert are more com-fortable to users than those from the inversion scheme.

317

(a) original (b) 20000cd/m2 (c) 30000cd/m2 (d) 40000cd/m2

Figure 12: Example 1 of applying the ColorVert prototype under strong lighting conditions.

(a) original (b) 20000cd/m2 (c) 30000cd/m2 (d) 40000cd/m2

Figure 13: Example 2 of applying the ColorVert prototype under strong lighting conditions.

(a) inversion (b) colorvert

Figure 14: Example 1 of applying Inversion and Col-orVert schemes under weak lighting conditions.

(a) inversion (b) colorvert

Figure 15: Example 2 of applying Inversion and Col-orVert schemes under weak lighting conditions.

5.2 Impact on User Experience of ColorVertIn order to evaluate the effectiveness of ColorVert, we first

request those 206 volunteers mentioned in Section 2 to gradethe user experience for some popular webpages which aretransformed by ColorVert on Motorola XT928, with an am-bient light strength of around 20000cd/m2 for strong light-ing conditions (sunlight) and around 2cd/m2 for weak light-ing conditions (at night). With each webpage, users areshown with both the original with auto-brightness adjust-ment(Original) and the transformed webpage(ColorVert)simultaneously on two Motorola XT928 phones and they arenot aware of which one is the transformed one. The resultsof the survey are shown in Fig.16. For each webpage, twobars on the left show the RVP value and user experiencescore of the strong sunlight respectively while the two barson the right show the UGR value and user experience scoreof the weak light respectively. From Fig.16 we can observethat with ColorVert, RVP values are improved to around70% under strong lighting conditions, which is sufficient forsatisfying reading experience. User experience scores arealso significantly improved under strong lighting conditions.Moreover, under weak lighting conditions, although UGRvalues are decreased with a small decline compared with theinversion color scheme, the better comfort level and similar-ity help ColorVert a lot to achieve a higher score of users’feedback. From our study, we observe that the preservedsimilarity and the improved readability and comfort levelcontribute to the user experience improvement.

Next, we evaluate the performance of our system underdifferent light strengths by using different smartphones. Werandomly select 100 webpages and use four different mo-bile devices to display these webpages. Fig.17(a) shows our

318

Figure 16: RVP, UGR and user experience score ofColorVert.

results under strong lighting conditions, in which sunlightstrength varies between 15000 − 30000cd/m2. RV P Ratiois the ratio between RVP value of the optimal color schemeand original one, i.e.,

RV P Ratio =ColorV ert RV P

Original RV P. (8)

It can be seen from the figure that as sunlight strength in-creases, the RVP Ratio rises, which indicates that ColorVerteffectively prevents RVP value from sharp decreasing un-der strong lighting conditions. Also, we set up another ex-periment to evaluate the impact of different screen sizes onColorVert. ColorVert is applied to the four devices statedabove and we require volunteers to score the color scheme.We present the average user experience score in the bottomhalf of Fig.17(a). From the figure, ColorVert improves userexperience on all four kinds of devices. And there are littledifference of the user feedback for different screen sizes.

For weak lighting conditions, we perform similar exper-iments which restrict the ambient light luminance in therange of 0− 100cd/m2 and calculate the UGR Ratio, whichis defined as

UGR Ratio =ColorV ert UGR

Inversion UGR. (9)

Fig.17(b) shows the results. It can be seen that the UGR Ra-tio decreases as lighting strength goes down, which indicatesthat ColorVert can reduce the UGR value under weak light-ing conditions. User experience survey under weak lightingconditions shows similar results as those of strong lightingconditions.

Therefore, our color transformation model can achieve agood user experience across different webpages and mobiledevices for both sunlight and weak lighting conditions.

5.3 Transformation TimeTransformation time is another critical issue that concerns

our system. User experience would be severely degraded ifthe transformation time cannot keep up with the occurrenceof different kinds of events. We set up our system on fourmobile devices, and further set their CPU frequency to cer-tain levels. Then we randomly select 100 webpages and openthem on four devices with different CPU frequencies under

(a) strong light

(b) weak light

Figure 17: UGR, RVP ratio and user experiencescore of different devices.

both strong and weak lighting conditions. In order to ex-tract the time of color transformation, webpages are firstsaved on our mobile devices to avoid the interference fromnetwork connection. We record both the time consumed toload a webpage with and without our system and calculatetheir average time difference. Based on the trace collected,Fig.18 plots CDF of the color transformation time of Col-orVert. It can be seen from the Fig.18 that for MotorolaXT928 which uses 1GHz CPU frequency, about 90% of theColorVert transformation time are within 25ms. Other de-vices show similar results. These results are very encour-aging as they demonstrate the transformation time is veryshort, which is not noticeable by users. Also, we could seefrom the Fig.18 that CPU frequency almost has little ef-fect on the performance of our system. Therefore, the delaycaused by our system is acceptable in daily use and will notcause user experience degradation.

5.4 Overhead AnalysisAlthough Sensing Unit monitors the user and light event

in real time and recalculate the optimal color scheme accord-ing to the events, the overhead of our system is negligible.Firstly, when there is no event occurs, our system does notperform any calculation. Secondly, we have taken various of

319

(a) Motorola XT928 (b) Samsung Galaxy Note (c) Nexus 7 (d) Samsung Galaxy Tab

Figure 18: CDF of average transform speed of ColorVert on different devices.

methods to decrease the occurrence frequency of both sens-ing light and human-screen interactions to further lessen theoverhead. Also, total recalculation has a complexity of O(k)and Fast Transformation only performs a linear transforma-tion. So that only a few computation is need to calculate ouroptimal color scheme. As a result, our system is lightweightand the overhead could be negligible.

6. RELATED WORKIn this section, we review the existing work that related

to our research. Modern mobile devices have introduced theautomatic brightness adjustment system[19], which couldchange screen brightness level with ambient light. There aresubsequent studies aimed at improving the performance ofautomatic brightness adjustment system[2] to improve userexperience. However, they only focus on the luminance levelof the screen without taking the color scheme into consid-eration, which is insufficient under very strong and weaklightning conditions.

With the explosive growth of smartphone usage, there aresome researches about color scheme for smartphone displaysto improve user experience under strong and weak lightningconditions. [20] present a theoretical basis to adjust use-preferred color temperature for smart-phone displays undervarious illuminant condition, but [20] do not consider read-ability and similarity that contribute to user experience, anddo not propose any adaptive color scheme solution. Al-though there exist some work to improve user experienceby adjusting color saturation[3] or contrast[4, 5] accordingto ambient light, they focus on images and videos displaywhich is hard to achieve higher readability for texts. More-over, [21] shows long-wavelength light affects visual perfor-mance under weak lightning conditions, but they do not dis-cuss relationship between foreground and background color.

Moreover, researchers are interested in smartphone screencolor scheme adaptation. Focused on saving the energy, [6]can adjust the color scheme of webpages to reduce the powerconsumption of smartphones. [7] constructs a predictivemodel for mobile systems and [22] uses a multi-objectiveoptimization to find the most energy efficient color scheme.However, the purpose of these work is to improve energy ef-ficiency while sacrifices user experience. The work[23] pro-poses a frame rate adaptation system, which considers bothenergy efficiency and user experience. However, it does notrefer to the adaptive color adjustment for improving userexperience. Also, there are some existing studies that try toadjust webpages for smartphone screens[24][25]. However,they aim at fitting the webpage into the small smartphone

screen without considering the effect of ambient light on userexperience.

Although the user experience of mobile devices has gainedmuch attention, existing work has not concerned the impactof the webpage color scheme to user experience. Our workis different from the previous studies in that we explore therelationship between color and user experience, then designColorVert, which can adaptively adjust the color scheme toimprove user experience.

7. CONCLUSIONIn this paper, we address the problem on the readabil-

ity of mobile device screen display under dynamic lightingconditions. Through analyzing the color schemes on mobiledevice displays, we find that merely applying bright colorsresults in user experience degradation under both strongand weak lighting conditions. Furthermore, after runningexperiments with over 200 volunteers, we find that read-ability, comfort level and similarity are major factors thatcontribute to user experience when reading mobile devicescreens. We develop an adaptive color scheme transforma-tion system, ColorVert, which senses the ambient light con-ditions and utilizes our proposed color transformation algo-rithm to adaptively calculate the optimal color scheme un-der both strong and weak lighting conditions. We prototypeColorVert on several types of smartphones and tablets. Ex-tensive experiment results with over 200 participants demon-strate the effectiveness of the ColorVert design comparedwith the existing automatic brightness adjustment system.The prototype implementation shows that ColorVert is fea-sible to be applied in real-world scenarios for maintainingand improving user experience under various lighting condi-tions.

Although our work only focuses on dealing with plaintext of web browsers, we believe the proposed color trans-form adaptation approach could be easily extended to han-dle other smartphone tasks with plain text using screen dis-plays. In the future, we will extend our user experience-oriented color scheme on display of images, video and em-bedded text.

AcknowledgementResearch was sponsored by National Science FoundationCNS1217387, SES1450091, NSFC 61170238, NSFC 61373157,973 Program 2014CB340303, Changjiang Scholar and In-novative Research Team in University (IRT1158, PCSIRT)China.

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8. REFERENCES

[1] venturebeat.com, “Smartphone sales pass 250m in q3.”[Online]. Available:http://venturebeat.com/2013/10/28/smartphone-sales-pass-250m-in-q3-and-samsung-grew/

[2] T.-Y. Ma, C.-Y. Lin, S.-W. Hsu, C.-W. Hu, and T.-W.Hou, “Automatic brightness control of the handhelddevice display with low illumination,” in ComputerScience and Automation Engineering (CSAE), 2012IEEE International Conference on, vol. 2.

[3] The best smartphone display (it’s not who you think).[Online]. Available: http://gizmodo.com/the-best-smartphone-display-its-not-who-you-think/

[4] Y. Monobe, H. Yamashita, T. Kurosawa, andH. Kotera, “Fadeless image projection preserving localcontrast under ambient light,”Color and ImagingConference, vol. 2004, no. 1, pp. 130–135(6), 2004.

[5] T. O. Aydin, K. Myszkowski, and H.-P. Seidel,“Predicting display visibility under dynamicallychanging lighting conditions,”Computer GraphicsForum, vol. 28, pp. 173–182, 2009.

[6] M. Dong and L. Zhong, “Chameleon: a color-adaptiveweb browser for mobile oled displays,”MobileComputing, IEEE Transactions on, vol. 11, no. 5.

[7] Y. Wen, Z. Liu, W. Shi, Y. Jiang, A. M. Cheng, andK. Le, “Energy efficient hybrid display and predictivemodels for embedded and mobile systems,” inProceedings of the 2012 international conference onCompilers, architectures and synthesis for embeddedsystems.

[8] anhosting.com, “How many people use smartphones tobrowse websites?” [Online]. Available:http://www.anhosting.com/blog/2012/11/how-many-people-use-smartphones-to-browse-websites/

[9] H. L. M. H. K. Hammond, “The metric system inilluminating engineering,” in Radiometry andPhotometry.

[10] M. Stokes, M. Anderson, S. Chandrasekar, andR. Motta, “A standard default color space for theinternet-srgb,”Microsoft and Hewlett-Packard JointReport, 1996.

[11] M. S. Rea and M. J. Ouellette, “Visual performanceusing reaction times,” Lighting Research andTechnology, vol. 20, no. 4, pp. 139–153, 1988.

[12] M. S. Rea and M. J. Ouellette, “Relative visualperformance: A basis for application,” LightingResearch and Technology, vol. 23, no. 3, pp. 135–144,1991.

[13] S. Plainis and I. Murray, “Neurophysiologicalinterpretation of human visual reaction times: effect ofcontrast, spatial frequency and luminance,”Neuropsychologia, vol. 38, no. 12.

[14] B. M. O.DONELL, J. F. Barraza, and E. M. Colombo,“The effect of chromatic and luminance information onreaction times,”Visual neuroscience, vol. 27, no. 3, p.119, 2010.

[15] CIE, “Discomfort glare in interior lighting,” inDiscomfort Glare in Interior Lighting.

[16] CIE, “Calculation and presentation of unified glarerating tables for indoor lighting luminaires.”

[17] J. W. Peirce, “Psychopy psychophysics software inpython,” Journal of neuroscience methods, vol. 162,no. 1.

[18] D. I. MacLeod and R. M. Boynton, “Chromaticitydiagram showing cone excitation by stimuli of equalluminance,” JOSA, vol. 69, no. 8, pp. 1183–1186, 1979.

[19] C. S. Bell, “Automatic brightness control for displays,”Apr. 19 2011, uS Patent 7,928,955.

[20] K. Choi and H.-J. Suk, “User-preferred colortemperature adjustment for smartphone display undervarying illuminants,”Optical Engineering, vol. 53, pp.061 708–061 708, 2014.

[21] R. Mantiuk, A. G. Rempel, and W. Heidrich, “Displayconsiderations for night and low-illumination viewing,”in Proceedings of the 6th Symposium on AppliedPerception in Graphics and Visualization.

[22] S. Singh, D. Katiyar, M. Theodorou, S. Prasad,U. Mishra, R. Bhaskar, K. Pandey, O. Verma,A. Mishra et al., “Nutritional and morphologicalattributes of stay green vis-a-vis go-brown sorghumcultivars at different stages of maturity.” IndianJournal of Animal Sciences, vol. 79, no. 4.

[23] H. Han, J. Yu, H. Zhu, Y. Chen, J. Yang, G. Xue,Y. Zhu, and M. Li, “E 3: energy-efficient engine forframe rate adaptation on smartphones,” in Proceedingsof the 11th ACM Conference on Embedded NetworkedSensor Systems, 2013, p. 15.

[24] M. Jones, G. Marsden, N. Mohd-Nasir, K. Boone, andG. Buchanan, “Improving web interaction on smalldisplays,”Computer Networks, vol. 31, no. 11.

[25] N. Bila, T. Ronda, I. Mohomed, K. N. Truong, andE. de Lara, “Pagetailor: reusable end-usercustomization for the mobile web,” in Proceedings ofthe 5th international conference on Mobile systems,applications and services.

321