perception of sparkle in anti-glare display screens · concluding that sparkle has the same cause...

Perception of sparkle in anti-glare display screens

James A. Ferwerda (SID Member) Abstract — In this p

Alicia Stillwell (SID Student Member)

Howard Hovagimian (SID Member)Ellen M. Kosik Williams (SID Member)

aper, we describe a series of psychophysical experiments to quantify therelationships between anti-glare (AG) glass treatments and perceived sparkle in emissive displays. Theexperiments show the following: (1) that a new measure, pixel power deviation, correlates well with per-ceived sparkle; (2) that for a given AG treatment, sparkle is worse on high-pitch displays; (3) that tests ofsparkle using small samples provide a conservative bound on perceived sparkle in display-sized sam-

Received 8/21/13; accepted 10/21/13.J. A. Ferwerda and A. Stillwell are with Munsell C14623, USA; e-mail: [email protected]. Hovagimian and E. M. Kosik Williams are with C© Copyright 2014 Society for Information Display 1

ples; and (3) that sparkle visibility is affected by the content of displayed images. The goal of these effortsis to enable the development of AG glass treatments for emissive displays that effectively reduce frontsurface reflections while preserving high image quality.

Keywords — display systems, sparkle.

DOI # 10.1002/jsid.223

1 Introduction

Modern electronic displays are typically composed ofpixelated emissive elements (LCD/backlight, organic light-emitting diode, etc.) faced with a cover sheet of plastic orglass. This cover sheet can be designed to have a range oftransmissive and reflective properties, and one common pur-pose is to reduce the visibility of reflections (glare) from lightsources and the environment. Anti-glare (AG) treatments canbe applied to the cover sheet that diffuse the reflections andthereby reduce their visibility. While these treatments can beeffective in reducing the impact of surface reflections, theycan sometimes produce a transmission artifact known as“display sparkle” where the displayed image appears to be cov-ered by small colored highlights that scintillate with movementof the display and observer. Figure 1 shows a photograph of thesparkle phenomenon and Fig. 2 illustrates its cause. Sparkleartifacts can be disturbing and can severely reduce perceivedimage/display quality. Therefore, display designers have beentrying to understand sparkle and to develop methods forminimizing sparkle in displays with AG treatments.

2 Prior work

Recently, a number of researchers have attempted to quantifysparkle and to relate it to the physical properties of AGsurface treatments.

Cairns and Evans1 observed that sparkle is similar inappearance to the speckle patterns seen when coherent laserlight is transmitted by diffusing surfaces. They further

olor Science Laboratory, Cen

orning Incorporated, Cornin071-0922/14/0000$1.00.

observed that sparkle depends on the relative scales of theemitting and diffusing elements and the emitter to diffuserdistance. Concluding that sparkle has the same cause as laserspeckle, they suggest that a useful measure for characterizingsparkle is the standard (laser) speckle contrast (SC) function

SC ¼ σi=Ið Þ

where I is the transmitted intensity and σi is the standard de-viation of the transmitted intensity over the measurement re-gion. In an experiment to verify this, they used a laser and aline-scan camera to measure the speckle contrast of twospray-produced AG surfaces with similar overall roughnessvalues but with features at different spatial scales. For thesurfaces they tested, they found that perceived sparkle wasdirectly related to the measured speckle contrast.

Subsequently, Huckaby and Cairns2 conducted a more sys-tematic study of the relationship between speckle contrastand perceived sparkle. First, they measured the speckle con-trast of five acid-etched AG surfaces having different rough-nesses (as characterized by 60° specular gloss, with valuesranging from 35 to 120). Then, they had 30 observers ratethe perceived sparkle of the surfaces on a scale of 1–10 byplacing them in front of a cathode-ray tube (CRT) and observ-ing the effects. While they found that both speckle contrastand perceived sparkle increase monotonically with surfacegloss (lower roughness), and suggest that this provides sup-port for speckle contrast as a metric for perceived sparkle,the correlation (as seen in their results graph reproduced inFig. 3) appears to be only modest.

Becker and Neumeier3 took a different, image-processing-based, approach to characterizing sparkle. They started byarranging a monochrome digital camera in front of an liguid

ter for Imaging Science, Rochester Institute of Technology, Rochester, NY

g NY 14831, USA.

Journal of the SID, 2014

FIGURE 2 — Diagram illustrating the cause of sparkle. The rough sur-face of the anti-glare (AG) cover glass light rays emitted by the RGBsubpixels in the LCD panel. This produces the sparkle patterns observedby the viewer.

FIGURE 3 — Results of Huckaby and Cairns’2 experiment relating 60°specular gloss, speckle contrast, and perceived (subjective) sparkle. Whilethe speckle and sparkle functions are both monotonically increasing, thecorrelation only appears to be modest.

FIGURE 1 — Photograph showing the sparkle phenomenon. The lefthalf of the image is a bare LCD panel displaying a uniform green im-age. The right half shows the same image seen through an anti-glarecoating. Note the visible noise. In direct view, the dots appear toscintillate with movement.

Ferwerda et al. / Sparkle perception in anti-glare display screens

crystal display (LCD) display with an AG front surface. Thecamera and display distance were adjusted so that there were5–7 camera pixels per display sub-pixel (RGB) and showed auniform green image on the display. They then used twomethods to characterize sparkle. In the “difference” method,they took two images of the display with the AG surface indifferent positions. They then took the difference betweenthe image values, which eliminated constant intensity levelsand revealed the intensity variations produced by the AGsurface. In the second “spatial filtering” method, they took asingle image and computed the intensity variations by dividingthe measured image values by a low-pass filtered (averaged)version of the same image. To test the methods, they con-ducted an experiment in which they had expert observersplace 14 AG samples into eight categories. They found good,near-linear relations between the image-based measures andperceived sparkle ratings, although the relations found usingthe difference method was more consistent than the spatialfiltering method. They then investigated the relationships be-tween three important properties of AG surfaces (glare reduc-tion, distinctness of transmitted image, and perceived sparkle)and found that it is possible to design AG treatments that arehigh in the first two positive properties (glare reduction anddistinctness of image) and low in the negative property (spar-kle as measured by their methods).

Finally, in a project related to this one, Gollier et al.4 have de-veloped a newmeasurement device andmetric for characterizingAG surface properties and perceived sparkle called pixel powerdeviation (PPD). Themeasurement device is illustrated in Fig. 4.In the device, a test sample is placed a set distance (typically0.5mm) from an RGB LCD display (Lenovo U110 notebookcomputer display) with its protective cover removed. The displayis set to show a uniform green screen (only green subpixels illu-minated). The light transmitted by the sample is imaged on amonochrome CCD (charge-coupled device) camera through apair of lenses (L1 and L2) and is stopped by a diaphragm (D)with aperture set to 12mrad to mimic the acceptance angle ofthe human eye. The optics are designed so that each LCD pixelis sampled by approximately 20×20 CCD pixels.

FIGURE 4 — Diagram of pixel power deviation measurement device. The“source” is an LCD panel showing a uniform green screen. The “test sam-ple” is the anti-glare sample. “L1 and L2” are lenses, and “D” is diaphragmset to mimic the acceptance aperture of the human eye. The “CCD” is ascientific grade monochrome CCD camera. The optics are arranged soeach LCD pixel is sampled by approximately 20 ×20 CCD pixels.

To measure the PPD of an AG sample, two images are cap-tured, a test image of the screen seen through the sample anda reference image of the screen alone. To calculate the PPDof the AG sample, any dark noise or other background lightis first subtracted from both the test and reference images,the pixel areas are identified, and the total power within eachpixel is integrated. The pixel power distribution in the testimage is normalized by dividing by the pixel powers fromthe reference image. The standard deviation of the distribu-tion of these normalized pixel powers is then calculated togive the (referenced) PPD (PPDr) measure. Performing themeasurement using a reference is necessary to remove anysource non-uniformities and provide the sparkle level thatarises solely from the sample under test. Figure 5 shows twoimages taken with the device.

To test the usefulness of the PPDr measure, in an unpub-lished experiment, Gollier et al.4 asked observers to rate theperceived sparkle of 10 textured glass AG samples using a cat-egorical rating procedure. They found excellent linear corre-lation between PPDr and perceived sparkle and in additionwere able to estimate the absolute threshold for perceivedsparkle at 1–3% PPDr.

While this work represents some promising first steps to-ward characterizing perceived sparkle and relating it to theoptical properties of AG surfaces, perceived sparkle is not afixed property and is known to vary with a number of display,environmental, and user-related factors. In this paper, we con-duct a series of psychophysical experiments that investigatesome of these factors to gain a fuller understanding of thesparkle phenomenon and to provide guidelines for the designof effective, high-quality, AG display treatments.

3 Experiments

In the previous section, a number of methods were describedthat attempt to characterize the potential sparkle of AG dis-play surfaces. These methods use a variety of physical instru-ments and image processing techniques in laboratory settings

FIGURE 5 — False color images of anti-glare samplemeasurement device. The sample imaged in Fig. 5a dimages that are regular and consistent, yielding lowimage in Fig. 5b shows higher variation in the pixelhigher visible sparkle.

to quantify the relationships between the optical properties ofthe surfaces and potential sparkle. While the methods repre-sent a step forward in quantifying sparkle, the assessmentsmade under highly controlled laboratory conditions may notfully predict the effects under typical use conditions wheredifferent kinds of displays (desktop, mobile, and television),with different resolutions (typically 75–300 ppi), will be show-ing different kinds of content (text, graphics, photos, andvideo) to users with different experiences, purposes, andpreferences. To develop a better understanding of how thesedifferent factors affect perceived sparkle and the visual qualityof displays with AG treatments, we have conducted a series ofpsychophysical experiments.

In experiment 1, we investigate the interactions of displayresolution (pixel pitch) with perceived sparkle and quantifythe relationships between perceived sparkle and the PPDrsparkle measure for displays with different pixel pitches. Be-cause sparkle measurements are typically carried out on AGsamples that are much smaller than the AG treated surfacesfinally applied to the displays, in experiment 2, we study theeffects of AG sample size on perceived sparkle and againrelate the visual assessments to the PPDr sparkle measure.Finally, in experiment 3 we look at the effects of image con-tent on perceived sparkle for range of representative contentincluding photographic landscapes and portraits, and text, aswell as standard test images. The methods, procedures, and re-sults of the experiments are described in the following sections.

3.1 Methods

Modern emissive displays have a wide variety of pixel geome-tries and sizes. These factors are known to affect the sparklephenomenon, with sparkle generally understood to be moreproblematic as display resolution increases or rather as pixelpitch decreases.1 To investigate this issue and to provide datarelevant to modern systems, we studied two displays. The firstwas an LCD panel from a Lenovo U110 laptop computer withits protective cover removed. The LCD panel had squarepixels, RGB subpixels, and a pixel pitch of 141 pixels/in.

s acquired with the pixel power deviation (PPD)oes not exhibit any visible sparkle and has pixelreferenced PPD (PPDr). In contrast, the samplepower distributions yielding higher PPDr and


FIGURE 6 — Setup used in the sparkle scaling experiments. LCD panel

(180μm/pixel). The other display was a “Retina” LCD panelfrom an Apple iPad 3 tablet computer. This display also hadsquare pixels and RGB subpixels but had a finer pixel pitchof 264 pixels/in. (95.2μm/pixel). We tested this display withits glossy cover glass/touch screen in place.

To study the effects of different AG treatments, we createda set of AG glass samples by roughening the front surfaces of0.7-mm-thick alkali aluminosilicate glass through various etch-ing processes. Different surface roughnesses were producedby varying the treatment times. Separate reference and small(2 in. × 2 in.) and large (6 in. × 6 in.) sample sets were created.We measured the light-scattering properties of the samplesusing the PPDr technique described earlier. The PPDr mea-surements were referenced to the Lenovo LCD panel. Themeasured PPDr values for the sets of AG glass samples usedin the experiments are given in Tables 1 and 2.

showing a uniform green screen is behind a black cardboard mask. Refer-ence samples glued to 35mm slide mounts form a scale that is along thebottom (low to high referenced pixel power deviation, left to right). The testsample, with placement handle, is on the top. Although not evident fromthe photograph, the level of sparkle varies significantly across the scale.
3.2 Procedure
We performed a series of psychophysical scaling experimentsto understand the relationships between the properties of AGdisplay glass and perceived sparkle. All the experiments usedthe same graphical rating procedure.5

The experimental setup is shown in Fig. 6. The “small”glass samples were glued to the backs of 35mm slide mounts,which provided a standard viewing aperture (one 3/8 in. ×7/8 in.) and allowed for easy handling of the samples without

TABLE 1 — Referenced pixel power deviation values of thereference samples and the “small” test samples used inexperiments 1 and 3.

“Small” samples PPDr value

Reference 1 0.6Reference 2 4.2Reference 3 7.7Reference 4 12.1Test 1 0.7Test 2 1.0Test 3 2.5Test 4 3.1Test 5 4.0Test 6 5.2Test 7 5.2Test 8 6.4Test 9 7.1Test 10 8.8

PPDr, referenced pixel power deviation.

TABLE 2 — Referenced pixel power deviation values of the“large” test samples used in experiment 2.

“Large” samples PPDr value

Test 1 0.4Test 2 2.2Test 3 4.4Test 4 5.8Test 5 8.0

PPDr, referenced pixel power deviation.


smudging. The samples were placed directly against thedisplay being tested with the AG surface closest to the viewer.Four “reference” samples with PPDr values 0.6, 4.2, 7.7, and12.1, were placed in a row along the bottom of the displayfrom low to high (left to right). The “test” sample beingevaluated was placed above the row of reference samples.Observers viewed the samples from approximately 18 in.under normal office lighting conditions; however, care wastaken to avoid front surface reflections from the glass samples.

On each trial of an experiment, observers were given asheet of paper with a 6-in. line printed on it (Fig. 7). Crossmarks made at four intervals across the line were used to rep-resent the magnitudes of the reference samples. For each testsample, observers were asked to make a mark on the line toindicate how the sparkle of the test sample compared withthe sparkle of the reference samples. Note that while the ref-erence and test samples were characterized using the PPDrmeasure, the measured values and choice of reference stimuliplaced no necessary constraints on the observers’ judgmentsof perceived sparkle for a given sample. Observers were freeto rate the test samples as they saw fit, and thus, an infinityof potential psychophysical relations were possible.

To calculate the observer’s rating (s), the distance (d) fromthe left edge of the line to the mark was divided by the totalline length (6 in.), and then, this value was multiplied by thesum of the lowest and highest reference PPDr values [s=d/6*(0.6+ 12.1)]. These ratings were then averaged across the

FIGURE 7 — Response figure used by the observers in the scaling exper-iments. Cross points indicate the magnitudes of the reference samples. Ob-servers were instructed to make a mark at the scale location correspondingto the perceived sparkle of the test sample. Sparkle ratings were calculatedfrom normalized mark/scale distances.

observers tested, to calculate an overall perceived sparkle valuefor each test sample.We applied this linear scaling to the data sothe PPDr and perceived sparkle scales would have the samenumerical range; however, because the rating method producesinterval scales, all linear scalings of the data are equivalent, andwhile the range of the scale and stimulus scale values maychange, the relationships (distances) between the stimuli mea-sured with respect to the scale are preserved.

Twenty male and female observers aged 18–35 years partic-ipated in the experiments. All were university students or em-ployees. Some had professional interest in imaging systems,but all were naïve to the specific topics and purposes of theexperiments. All had normal or corrected-to-normal vision.

3.3 Experiment 1: effects of display characteristics

In experiment 1 we wanted to investigate the effects of dis-play pixel pitch on perceived sparkle. To do this, we had ob-servers scale the sparkle of the small AG samples on thelow-pitch and high-pitch displays described earlier, using auniform green screen as a background image. The results ofthe experiment are summarized in Fig. 8, which plots per-ceived sparkle versus measured PPDr. The two sets of glyphsshow the results for the two displays tested. The error bars in-dicate the standard deviations of the ratings, and the lines andequations are regression fits to the data.

Two main findings can be seen in the graph. First, on eachdisplay, there is a strong linear relationship (R2 = 0.97) be-tween sample PPDr and perceived sparkle. This suggests thatPPDr is potentially a good predictor of sparkle for AG treat-ments. Also, it is important to note that although PPDr ismeasured on the 141 ppi display, the perceived sparkle onthe higher resolution display still scales linearly with the

FIGURE 8 — Results of experiment 1—note the linear relationships be-tween measured referenced pixel power deviation (PPDr) and perceivedsparkle and that for a given anti-glare sample, perceived sparkle is higheron the high-pitch (264 ppi) display.

measured values. This suggests that PPDr values can bescaled using a multiplier related to the pixel pitch, or othersystems parameter, to provide a relative magnitude of sparklefor other displays. Second, an analysis of variance showed thatthe slopes of the PPDr versus sparkle lines are significantlydifferent for the two displays (df= 1, F= 83.98, p< 0.001).The data show that over most of the PPDr ranges, the sparkleratings for samples test on the high-pitch display are greaterthan the ratings for samples tested on the lower-pitch display.This result confirms informal observations that for a given AGtreatment, perceived sparkle increases with display pitch andshows that the effect is very systematic.

In addition to understanding the relationship betweenPPDr and perceived sparkle across the range of AG treat-ments on different displays, it is also useful to understandthe relationships between measured differences in PPDrand differences in perceived sparkle. This information is valu-able for establishing tolerances on manufacturing processesand for the optimization of sparkle with respect to other treat-ment properties. To investigate this issue, we analyzed the var-iance of the perceived sparkle ratings for each of the samplesused in experiment 1 and estimated the just-noticeable differ-ence in PPDr for the treatments we tested. To do this, weaveraged the standard deviations of the sparkle ratings mea-sured for each sample in experiment 1 and then multipliedthis result by a factor of 0.67449 to reflect the standard 75%detection threshold used in visual psychophysics.5 This calcu-lation resulted in an estimated sparkle just-noticeable differ-ence of 0.84 PPDr units. In practical terms, this means thata measured PPDr difference of this amount should be detect-able on three out of four observations. While this finding is inline with our informal observations and the fact that samplesrepresented significant ranges of both AG properties and per-ceived sparkle, it must be emphasized that this is only a roughestimate, and formal sparkle discrimination studies should beperformed to establish more reliable measures.

3.4 Experiment 2: effect of sample size

In experiment 2, we wanted to understand if sparkle mea-sured using small samples would be a good predictor of spar-kle in larger mobile display-sized (smart phones and tablets)treatments. This is an important question because in-lab test-ing is often used to provide specifications and tolerances forcommercial production and manufacturing. The methodsand procedures used in experiment 2 were the same as thoseof experiment 1, but the “large” AG samples were tested. Inthis case, to approximate the slide mounts used for the sam-ples in experiment 1, the larger samples were affixed to thebacks of square, black cardboard panels with 3.5 in. × 5 in. ap-ertures. The reference samples and apparatus were the sameones used in experiment 1. Only the high-pitch 264 ppi dis-play was used in testing.

The results of experiment 2 are plotted in Fig. 9. The datafrom experiment 1 for the small samples on the high-pitch


FIGURE 9 — Results of experiment 2—note that perceived sparkle mea-sured using the small samples is a conservative predictor of sparkle forthe larger display-sized samples.

display are plotted for comparison. As in experiment 1, thePPDr versus sparkle ratings for the large samples show astrong linear relationship (R2 = 0.99), suggesting that PPDris a good metric for perceived sparkle in AG display treat-ments. The graph also shows that for a given PPDr value, per-ceived sparkle for the large samples is lower than for the smallsamples, but an analysis of variance showed that the differencesare not statistically significant (df=1, F=0.29, p=0.59). Theresults suggest that in-lab sparkle testing using small sampleswill likely be a conservative predictor of perceived sparkle inmobile display-sized AG treatments and that therefore thePPDr metric may be useful for establishing specifications andtolerances in commercial production and manufacturing.

FIGURE 11 — Results of experiment 3—note that with the exception ofthe “landscape” image at high referenced pixel power deviation values,samples tested with real images show lower perceived sparkle than thosetested with a green screen. This suggests that green screen testing is conser-vative but may be excessive for typical use contexts. In particular, per-ceived sparkle for samples tested with the busy “crowd image” imagewas near negligible across the full sample range, suggesting that visualmasking effects may significantly reduce the visibility of sparkle undersome conditions.

3.5 Experiment 3: effects of image content

In experiment 3, we studied the effects of image content onperceived sparkle. This is an important question. While AGdisplay treatments are typically evaluated in-lab using abstracttest patterns such as green screens, these patterns are notnecessarily representative of the kinds of images end userswill show on the displays. Furthermore, testing may overesti-mate or underestimate perceived sparkle under typical useconditions, leading to unnecessary constraints and expenses

FIGURE 10 — Images used in experiment 3. (Left to right) te


for manufacturers and/or dissatisfied end users. To investigatethis issue, we had observers scale the perceived sparkle of AGsamples while viewing real images. To represent the kinds ofimage content end users might display, we created an imageset that included typical photographic images (landscape, por-trait, and crowd) as well as text. The images used in the exper-iment are shown in Fig. 10. The small samples and the high-pitch display were used. All the images were rendered at thenative resolution of the display. Otherwise, the experimentalmethods, procedures, and observers were the same as theones used in the two previous experiments including thereference samples backed by uniform green image.

The results of experiment 3 are shown in Fig. 11. As in ex-periments 1 and 2, linear trends were seen between measuredPPDr and perceived sparkle across the range of AG samples;however, the data also show that image content can have asignificant impact on perceived sparkle. First, note that withone exception, the sparkle ratings obtained with the greenpatch are the highest at each PPDr level. Second, note that

xt block sample, landscape, portrait, and crowd image.

0 0.5 1 1.5 2

crowd

portrait

text

landscape

green

Comparison of regression slopes

3 groups have slopes significantly different from green

FIGURE 12 — Analysis of experiment 3—results of a multiple compari-son test on the slopes of the regression lines for the data shown in Fig. 11.Note that the slopes of “perceived sparkle” functions for the text and por-trait images are significantly lower than those for the green and landscapeimages (p<0.001) and that the slope of the function for the crowd image issignificantly lower than all of the others (p<0.001).

James A. Ferwerda is an Associate Professor andthe Xerox Chair in the Chester F. Carlson Centerfor Imaging Science at the Rochester Institute ofTechnology. He received a BA in Psychology, MSin Computer Graphics, and a PhD in ExperimentalPsychology, all from Cornell University. The focusof his research is on building computational

testing with the text block sample, portrait, and crowd imageimages showed significantly lower sparkle ratings than testingwith the green patch [p< 0.001 in each case as measured bydifferences in their regression slopes with respect to the greenpatch data (Fig. 12)]. Third, note that in particular, the sparkleratings for samples tested with the visually busy crowd imagewere near negligible across the full PPDr range, likely due tovisual masking effects.6 Finally, note that the landscape im-age showed some nonlinearity, with low sparkle ratings forlow-to-moderate PPDr values and high sparkle ratings athigh PPDr values. Taken together, these results suggest thatwhile the current practice of evaluating AG display treat-ments with green screens is likely to be a conservative pre-dictor of perceived sparkle, in many cases, it may lead tooverly stringent requirements that may negatively impactother aspects of product production (such as cost andrejected units) and end user performance (such as imagecontrast and sharpness).

models of human vision from psychophysicalexperiments and developing advanced imagingsystems based on these models.

Alicia Stillwell is an MS candidate in the ColorScience Program at the Rochester Institute of Tech-nology. She received a BS in Mathematics from theUniversity of Nevada, Reno in 2010. Since 2012,she has been employed as an Operations Analystat Dish Network.

4 Discussion and conclusions

In this paper, we have described a series of psychophysical ex-periments that explore the relationships between the proper-ties of AG glass treatments and perceived sparkle in emissivedisplays. The experimental results show the following: (1) thata new measure of sparkle, PPDr, correlates well with per-ceived sparkle; (2) that sparkle is affected by interactionsbetween AG treatments and display pitch, with sparkleappearing worse on high-pitch displays; (3) that tests of per-ceived sparkle made using small samples provide a conserva-tive bound on sparkle in display-sized samples; and (4) thatsparkle visibility is affected by the content of displayed im-ages, with solid green screens typically rated as showing themost sparkle, photographic and text images rated as showing

somewhat less, but with busy images rated as showing signifi-cantly less sparkle, probably due to visual spatial-frequencymasking effects.

While these results are promising and useful, there is stillmuch work to be done. First, the glare-reducing propertiesof glass samples should be evaluated and compared with spar-kle measures to understand trade-offs in these factors. Sec-ond, the effects of image content should be studied moresystematically to allow sparkle to be predicted under typicaluse conditions. Finally, the effects of motion on perceivedsparkle (whether from dynamic content or from observer/de-vice movement) should be studied. Together, these effortsshould allow the development of psychophysical models ofthe effects of AG glass treatments that should enable the pro-duction of emissive display systems that provide high imagequality under widely varying viewing conditions.

References1 D. R. Cairns and P. Evans, “Laser speckle of textured surfaces: towardshigh performance anti-glare surfaces,” SID Symposium Digest of TechnicalPapers 38, No. 1, 407–409 (2007).

2 D. K. P. Huckaby and D. R. Cairns, “Quantifying sparkle of anti-glare sur-faces,” SID Symposium of Technical Papers 40, No. 1, 511–513 (2009).

3 M. E. Becker and J. Neumeier, “Optical characterization of scattering anti-glare layers,” SID Symposium of Technical Papers 42, No. 1, 1038–1041(2011).

4 J. Gollier et al., “Display sparkle measurement and human response,” SIDSymposium of Technical Papers 44, No. 1, 295–297 (2013).

5 P. G. Engeldrum, “Psychometric Scaling: A Toolkit for Imaging SystemsDevelopment,” Winchester MA: Imcotek Press (2000).

6 J. A. Ferwerda et al., “A model of visual masking for computer graphics,”ACM Transactions on Graphics (SIGGRAPH ‘97), 143–152 (1997).


Howard J. Hovagimian has been active in the tech-nology sector for over 20 years. He joined Corning,Inc. in 2010 as a Senior Research Statistician withthe Modeling and Simulation department. Hisresearch interests include problems in Corning’sDisplay, Environmental, and Specialty Materialsdivisions.

Ellen M. Kosik Williams is a Senior ResearchScientist in the Science and Technology Divisionat Corning Incorporated. She received her PhDfrom the Institute of Optics, University of Rochesterwhere she studied ultrashort laser pulse measure-ments. Dr. Kosik Williams’ current interests includeoptical coatings for displays and the characteriza-tion and minimization of display artifacts.


perception of sparkle in anti-glare display screens · concluding that sparkle has the same cause...

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