an investigation into color preference for color palette ...natural color palettes rank...

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An Investigation into Color Preference for Color Palette Selection in Multivariate Visualization Patrick Coleman Saunders {[email protected]} Victoria Interrante {[email protected]} DEPARTMENT OF COMPUTER SCIENCE AND DIGITAL TECHNOLOGY CENTER UNIVERSITY OF MINNESOTA MINNEAPOLIS, MINNESOTA

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Page 1: An Investigation into Color Preference for Color Palette ...natural color palettes rank intrinsically higher (i.e. more aesthetically pleasing) in some significant way and potentially

An Investigation into Color Preference for Color Palette Selection in Multivariate Visualization

Patrick Coleman Saunders {[email protected]}Victoria Interrante {[email protected]}

DEPARTMENT OF COMPUTER SCIENCE AND DIGITAL TECHNOLOGY CENTER

UNIVERSITY OF MINNESOTA

MINNEAPOLIS, MINNESOTA

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ABSTRACT

In this report we offer an experimental study and results on color palette preference. This experiment aimsto evaluate the relationship in preference of natural color schemes, being color palettes drawn from imagesof natural scenes, versus selected colors, being in this case those color palettes chosen for use in a set ofmultivariate information visualizations. Findings in this comparison of color preference may allow us toextend the results and make a comment on color choice in multivariate visualization in general.

1. INTRODUCTION AND RELATED WORK

Selection of appropriate color schemes for use in multivariate visualization has long been a contentioussubject. A great many works exist documenting various colorscales and color selection methods for variousrelevant applications spanning articles and software packages through conference papers and bookchapters. These approaches hinge on perceptual characteristics of the colors and/or data space to inform thecolor choice. For example, Bergman et al. present Pravda, a tool for choosing visualization colors based onperceptual rules. These rules span many forms, including spatial frequency of the data, the type of the dataitself, etc. [BRT95]. Another approach is offered by Kindleman, et al. in their face-based color luminancematching software. This software assists in the selection of perceptually equilumininant colors to mostaccurately convey relative data values across multiple color dimensions [KRC02] via optimization ofdiscriminability across the colorscale, a technique also employed by this research group [Sau05, HIH*05].Healey et al. exploit perceptually-derived colorspaces such as CIE-LUV for use in methods for ensuringthe most mutually distinguishable color palettes [Hea96]. Various others have authored attempts atcolorscale optimization for use in multivariate visualization [e.g. LH92]. Despite this work, the variety ofcolor schemes represented in the corpus of scientific visualization attests to the fact that the method ofselecting colorscales is often governed by the convention of a given discipline (e.g. mechanical engineersmake frequent use of the heated-object colorscale) or simply approached in an ad hoc manner.

This work aims to approach the color selection process from a methodological context. We seek toinvestigate aesthetically-pleasing qualities in color palettes versus those that are visually jarring orunpleasant. A fundamental assumption is made that using more aesthetically-pleasing color palettes formultivariate visualization will engender a greater interest in contemplating the visualization and thuspotentially greater efficacy, whereas a poor color scheme may produce negative reactions in the viewer thatmay interfere with a full understanding of the visualization. In this sense, simply improving the aestheticqualities of the color palettes in use may yield general improvement in the quality of any givenvisualization.

Natural images and their unique perceptual qualities are well-researched among psychologists,vision scientists and color professionals. Many studies have found unique first-order characteristics innatural images [e.g. JKB05]. For example, the range of colors present in natural world is quite restrictedversus the gamut of all possible printable colors or visible colors [e.g. HH]. Our own examination of thenatural images used in this study confirms a relatively lower standard deviation in hue and saturation whencompared with other non-naturally derived images. Given the unique relationship between natural imagesand the human perceptual system, [e.g. Kap92] it is not unreasonable to investigate the possibility of acorrelation between features of natural images and preference. In fact, several similar studies exist;however, these concentrate on the preference of natural versus non-natural scenes as a whole [VB00,VB02, VR05]. Here we seek to examine a possible correlation between solely the color palettes fromnatural images and aesthetic preference.

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2. AN EXPERIMENTAL APPROACH

Our experiment particularly addresses the relationship between aesthetic values in natural imagesversus created images in which color schemes were chosen by an arbitrary, potentially unknown methodrepresenting the status quo. By testing these two groups against each other, we hope to determine whethernatural color palettes rank intrinsically higher (i.e. more aesthetically pleasing) in some significant way andpotentially what statistical features of the color palette may factor in this ranking. In order to do this, wemust extract color palettes from various sources and present them for rating in some fashion by experimentparticipants.

2.1. SOURCE MATERIAL

We have chosen to compare color palettes from natural images with the status quo in informationvisualization. Several considerations are important in the selection of source material for natural images.First, in order to minimize the influence of camera calibration effects, we utilize a set of natural imagestaken by precisely and consistently calibrated digital cameras. These images are commonly in use forcomputer vision studies and other experiments [Mcg05]. Second, these images must be freely available forusage such that the results of this experiment can be confirmed and/or compared to other such experiments.The Mcgill University online color image database fulfilled these needs [ibid]. The complete database is ofsubstantial size and contains some photographs not suitable for use in this study (e.g. photographs ofcityscapes); therefore, we utilize a randomly chosen subset of images from the library and then cull outthose not qualifying as “natural” in the sense that they contain man-made objects. The resultant images canbe a considered a relatively random sampling of natural images, though by no means an exhaustiverepresentation of natural image characteristics.

Status quo images from the information visualization cannon provide a control group in thisexperiment. The Proceedings of IEEE Infovis 2005 [SW05] represent the worldwide state-of-the-art in thefield of information visualization and thus the color palettes of the graphics contained therein are sensibleand respectable comparators to natural image palettes. In order to gather these images, we employ digitalscanning to read the image from the printed page at high-resolution and store it as in a lossless imageformat. There is some risk of calibration differences in these images- inks can change color significantlydepending on edition, age, and other factors; therefore, we use a single printed source for all status quoimages and these differences are minimized. Example images from the natural image set and the Infovisscan set can be found in figure A.

2.2. CONTEXT AND NORMALIZATION IN TEST IMAGES

The influence of context on color perception is a well-researched phenomenon. Colors in closephysical proximity on any media play a strong role in each others' perceived value, an effect know assimultaneous contrast [e.g. JH61]. For example, a gray color in the presence of a strong red color mayappear more blueish than the same gray color in proximity to a strong green color. Context also plays arole at the cognitive and cultural levels [e.g. War04]. Prior knowledge may influence the perception andperceived aesthetic qualities of a given color when it is presented in a certain context. A given shade ofblue may inspire a given emotion when presented in the context of a photograph of a swimming pool butan entirely different sentiment when presented as a blue sky, and these emotional qualities may influencethe perceived aesthetic quality of the image and colors therein. Furthermore, natural images possesscharacteristic spatial qualities (e.g. spatial frequency, spectral power content) that could possibly influenceor inform the viewer about the origin of the image in question [BM87] and thus affect perceived aestheticvalue. It has been shown that the color detection apparatus of the visual system is specifically oriented todetect said patterns in spatial orientation and frequency [BSD88]. Thomson and Foster performed image-matching experiments that show other second-order characteristics (e.g. phase spectrum) of natural imagescarry recognizable information and could potentially be used to discern between natural and artificialimages. [Tho99, TF97].

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In order to minimize these influences and evaluate solely the color palettes, it is necessary toremove as many contextual characteristics of the original image as possible prior to performing theexperiment. To do this we have produced noise images possessing first-order color characteristicsequivalent to those of the source image while removing any recognizable context. Our method forproducing these images obfuscates spatial frequency and gradient effects as well as normalizing geometricfeatures such as image size across the set of images. The method for generating these is relativelystraightforward and provides a good set of images for use in the experiment.

The original natural or Infovis image is first converted in a L*a*b* colorspace representationconsisting of a three-dimensional space bound by lightness and “a, b” color axes. L*a*b* is device-independent color space and has the advantage of being based on perceptual-difference metrics; that is, anygiven color coordinate in L*a*b* space should appear as different from its neighbors and any other colorand that second color's neighbors. Because of this quality, the euclidean distance between two colors in thisspace is (ostensibly) an accurate measurement of their difference. This color space is an appropriate middleground in which to examine and manipulate the Infovis images (scanned, high-quality CMYK media), andthe natural images (direct digital RGB). In the conversion to L*a*b* space, we have assumed a standardwhite-point for calibrated monitors, though we anticipate more rigorous calibration efforts in ourforthcoming work. Once an image has been reduced to this three-dimensional colorspace statisticalrepresentation by populating the space with hit counts of pixels of each color in the image, it is a simplematter to process these counts versus total pixels per image to obtain the percentages of total image spacethat a given color occupies, thus providing normalization.

Using these percentages, we set to the task of generating the noise images. Each noise image is a24-bit, 4200 by 3000 pixel image printed at 600 dots per inch, full-bleed on 5” by 7” glossy photo paper.The images consist of multitudes of colored circles drawn over each other in random fashion covering theentire image space. To choose the color of each circle, we employ a simple random choice algorithm thatsearches the palette of colors taking into account their relative concentrations. The resultant images (figureA) possess equivalent first-order color distribution characteristics to the original source images whileremoving all recognizable features, contextual clues, and other such spatial components.

2.3. EXPERIMENTAL PROCESS

Twenty-two subjects participated in the color evaluation study. Each subject is presented with theseries of printed 5” by 7” color plates consisting of noise texture images drawn from natural image colorpalettes and Infovis palettes, shuffled anew prior to each experiment and stacked in the center of a table72" in width. The experimental participants are asked to examine the color plates and arrange them in orderof aesthetic preference from high to low on the table (figure B). Participants are instructed that only thehorizontal coordinate placement of the plates is relevant- the participant may overlap color plates in anyway deemed necessary to convey the appropriate aesthetic ranking. Some subjects requested additionalclarification on the criteria for ranking and were told, "Consider the colors in the image and place them onthe table according to how pleasing the colors are." Once the subjects had finished arranging the cards theexperimenter asked them to take a final look over the arrangement and make any necessary corrections.Once finalized, aesthetic values were taken from the arrangement by measuring the distance from the edgeof the table with a standard tape measure.

Some participants did not use the entire table space for their arrangement. It is unclear if this isdue to simply not following instructions to use the entire space, implying that we might normalize the dataratings. The other possibility is that or the participant explicitly meant that no color plates had aestheticsvalues at the extrema of the scale and thus their underutilization of the entire ranking space was intentional.Normalization is irrelevant for the purposes of correlation estimations, so we leave the ratingsunnormalized. Unfortunately one participant misunderstand the instructions and data for that trial had to bethrown out.

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Figure A. Representative source and noise images for status quo (left, [SHM05]) and natural palette (right, [Mcg*] ) color plates.

2.4. PRELIMINARY STATISTICS OF NATURAL AND ARTIFICIAL TEST IMAGES

Statistics of the natural and Infovis images differ substantially. When we generate histograms for acomposite of all natural or all Infovis images (figure C, D), the most striking difference is in the luminancechannel. The natural image distribution demonstrates pseudonormal qualities, whereas the the Infovisimages are highly white-dominated. Of slightly lesser difference is the trend in the natural compositeimage's a* towards yellower rather than bluer hues and a slight tendency towards redder rather thangreener hues. In the Infovis composite, the distribution of hue appears somewhat normal; however, wemust recall that all pure grayscale values lie along the central {a*,b*} axis in L*a*b* space. Because theInfovis images are so strongly white-dominated (with black a close second) these histograms do not reflecta realistic distribution of the non-grayscale hues.

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Figure B. Experimental Setup for a Single Participant.

Our Infovis scans contained substantial amounts of white due to the white background of theprinted images. While much of the white surrounding the visualizations was removed via close-fittingbounding boxes in the scanning process, a great deal of white remains interior to the visualizations.Because the decision to include white paper background in creating Infovis images is arguable, wecompare another histogram for a composite of Infovis images having had the white extracted in post-processing (figure E). Surprisingly, these histograms remain dominated by very light values and give uslittle additional information about the distribution of hues in the Infovis images. The luminance channelappears quite similar- we must keep in mind that the images still possess the rest of the grayscalecomponents (e.g. black captions or gray lines in a visualization) and these features continue to dominatethe a* and b* histograms.

These images do not inform us as to characteristics of an individual natural or Infovis noise image,they are histograms of composite images of all Infovis or all natural images. In an effort to understand thecharacteristics of the color palettes on per-image basis we have investigated representative images (thehighest and lowest ranked) from each set (figure I), discussed with the results of the experiment.

3. EXPERIMENTAL RESULTS

Before an evaluation of the relation between similarity to naturalness of the Infovis images andpreference, it is necessary to evaluate the relationship between natural palettes and aesthetic quality toverify our claim that naturally-derived color palettes are preferred over the status quo. As a first step in theevaluation of the experimental data, we have produced charts depicting the rankings for all trials and plateswith respect to the natural or Infovis origin of the noise images (figures F, G). Unfortunately these chartsare woefully insufficient for the purposes of identifying trends in the data visually. Numerically, a standardcorrelation analysis of each trials' aesthetic rankings to naturalness of the source image shows a wide rangeof values for individual experimental participants; however, on average the correlation is somewhatpositive at .32. As a caveat, we note that standard methods for correlation assume a linear relationshipbetween the values to be correlated. In our case, the actual relationship is unclear (though it does notappear linear), so a more appropriate measure of the actual correlation is to use Spearman's method. Thismethod confirms a positive correlation for most cases (figure H), averaging at a medium strength .34.

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Figure C: L*a*b* channel histograms for composite Infovis noise image.

Figure D: L*a*b* channel histograms for composite natural noise image.

Figure E: L*a*b* channel histograms for composite Infovis image with whites removed.

Other preference studies show a similar result: Vessel and Biederman asked subjects rank a seriesof photographs both in terms of their aesthetic value and their “naturalness” versus “man-made” quality.Their study shows a strong correlation between the perceived “naturalness” of a given photograph andaesthetic value [VB00]. In our case, we do not consider naturalness a variable nor do we acquire ratingsfrom the subjects: our results consider only a boolean value for the naturalness of an image based on itsorigin as a photograph or Infovis scan. In this sense, their results correlate perceived naturalness of thesource image to aesthetic preference, a somewhat different quality than we are considering in this study.

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Figure F shows little consistency in rating across trials for status quo images.

Figure G shows a similar lack of consistency in rating across trials for natural images.

Figure H: Here we see the generally positive Spearman correlation between naturalness and aesthetic ranking, an overall average positive correlation of .34

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To get a better sense of the spread of ratings, we consider the distribution of ratings across allparticipants for the Infovis and the natural image sets separately (figure I). In this plot of rating (horizontalaxis) versus number of scores falling in a given value bin, we can see a clear bias towards lower ratings forInfovis images in comparison with the more evenly distributed natural image scores. In evaluating theshape of the plot, we must consider that the sample size for Infovis images was slightly higher, whichexplains the overall larger number of values in the chart. Normalization concerns aside, the charts clearlyindicate the general shape of the distribution in the case of the Infovis images' scores. The natural images'scores are somewhat less determinant in distribution.

Figure I: Plot of number of ratings versus rating for Infovis (left) and natural images (right).

The answer of what makes an image aesthetic is rather more elusive. We have seen that there aresome significant differences between our disparate sets of images: natural images average slightly lessluminant (64.07 versus 77.53), are equivalent in average a* value, and average slightly higher in averageb* value (149.94 versus 128.97). Natural images have a somewhat lower standard deviation acrossL*a*b* channels. A lower standard deviation in color signifies lesser overall color contrast throughout agiven image- in this sense the colors in the average natural image are more similar to each other than thecolors in the average Infovis image. Related to this, we reiterate that the Infovis images in our study arestrongly white-dominated, and this may contribute to the general lower aesthetic rating trend in Infovisimages as well. Spearman correlation between ratings and color deviation reveals correlation similar to thatof rating versus naturalness, -.3563, which makes sense as the color deviation and naturalness are stronglyrelated, but this does not mean that deviation of color is necessarily the causal factor in rating. In theabsence of clear statistical indicators of the relevant components of the aesthetic rating, we consider insteada visual examination of the best and worst average ranked images from each set (figures J,K,L,M). Thestandard deviation of ratings on all these images is fairly consistent for both the natural and Infovis imagesets, with Infovis ratings having an average deviation of 17.42 and natural ratings an average of18.95,suggesting a similar stability in the rating method. Visually, the worst-rated Infovis images reflect a biastoward the luminance axis; that is, they are mostly grays, blacks, and white. Both highly-ranked Infovisimages exhibit hue characteristics similar to highly ranked natural images. The second-best image clearlytending towards a set of greens and the most highly tending towards a set of oranges, though the highly-ranked natural images resembling this “best” image (i.55b) were not among the top two. Histograms ofthese images (figure N) confirm the trend towards grayscales in the worst images as evidenced by theminimal spread of the a* and b* distributions. In the best two images, we note that a* and b* have morecomplicated distributions, and particularly that in each images' case, a* and b* distributions fall mostly toone side of the axis. Little can be inferred from the luminance channel as it differs greatly in all fourhistograms.

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Natural images' worst-ranked images show in the second-worst case (i.43) a similar trend towardsless saturated colors. The worst image shows a similar color palette to the best images, but with muchhigher contrast and greater diversity of hues. The best images are both highly saturated in the green family,with a lower contrast than the worst image. The histograms (figure O) for these images are less informativethan visual inspection, unfortunately, except to note that again in the best image cases the distributionsseem to fall mostly to one side or the other of the a* and b* axes. This can also be said of the worst naturalimages, however, and the luminance channel again reveals little.

Visual inspection of the entire range of pictures and aesthetic rankings shows a general trend:high-contrast images and those images whose colors are mostly grayscale tend to rank lower. Higher-ranked images tend to be highly-saturated and colorful, but have low contrast. That is, these images tend torepresent many shades of the same color family (e.g. sets of greens). While we did not study the nature ofcolor gradients (in the sense of a sequence of slowly changing colors over a small space) in natural versusstatus quo images, these results seem to suggest that images with shorter, fewer, or smoother gradients inL*a*b* space are more aesthetically pleasing.

4. FURTHER WORK

First-order color characteristics are preserved in the generation of our test images and this studyseeks to evaluate color palettes only in terms of sets of single colors. Given the results which seem tosuggest that lower-contrast color palettes of a small span of the hue space are more aesthetic, and given thecommon usage of continuously varying colorscales for representing sequential data values in multivariatevisualization, it may be interesting to consider a similar study comparing smooth color gradients extractedfrom natural images to the color gradients in common use for multivariate visualization (e.g. the rainbowcolorscale, [LH92]). Even though color gradients exist in all of our original natural images, the process ofgenerating noise images removes any ordering of color in the output image hence obfuscating this feature.More in-depth statistical analysis such as principle component analysis of the L*a*b* color cross-sectionoccupied by the colors in a given palette may reveal further interesting characteristics of aesthetic palettes.

ACKNOWLEDGMENTS

This work is supported by a grant from the Army High Performance Computing Research Center(AHPCRC) under the auspices of the Department of the Army, Army Research Laboratory (ARL), underCooperative Agreement number DAAD19-01-2-0014., with additional funding from NSF (CTS-0324898).The content does not necessarily reflect the position or the policy of the government and no officialendorsement should be inferred. Computing resources for the production and calculation of color palettesand color palette images were provided by the Minnesota Supercomputing Institute and the DigitalTechnology Center of the University of Minnesota.

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Worst: image 21, ave. rating 19.55 Second-worst: image 8, ave. rating 22.79Figure J: Infovis: Lowest average ranked images

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Best: image 55b, ave. rating 43.62 Second-best: image 55, ave. rating 40.22Figure K: Infovis: Highest average ranked images

Worst: image 35, ave. rating 30.95 Second-worst: image 43, ave. rating 31.24Figure L: Natural: Lowest average ranked images

Best: image 48, ave. rating 51.09 Second-best: image 54, ave. rating 48.88

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Figure M: Natural: Highest average ranked images

Infovis: Worst image histogram: 21

Infovis: Second-worst image histogram: 8

Infovis: Second-best image histogram: 55

Infovis: Best image histogram: 55b

Figure N: Infovis: L*a*b* Histograms for best and worst images

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Natural: Worst image histogram: 35

Natural: Second-worst image histogram: 43

Natural: Second-best image histogram: 54

Natural: Best image histogram: 48

Figure O: Natural: L*a*b* Histograms for best and worst images

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BRT95 Bergman, L.D., Rogowitz, B., Treinish, L.A. A Rule-Based Tool for Assisting Colormap Selection. Proceedings of IEEE Visualization, 1995, 118-125.

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Hea96 Healey, C. Choosing Effective Colours for Data Visualization. Proceedings of IEEE Visualization, 1996, 263-ff.

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HIH*05 Hagh-Shenas, H., Interrante, V., Healey, C., Kim, S., Meyer, G. Weaving versus Blending: a Quantitative Assessment of the Information Carrying Capacities of Two Alternate Methods for Conveying Multivariate Data with Colors. manuscript in progress.

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and Theoretical Account. Journal of the Optical Society of America. 51, 1, 1961, 46-53.

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SW05 Stasko, J., Ward, M., eds., Proceedings of IEEE Information Visualization, 2005.

Sau05 Saunders, P.C., Interrante, V., Garrick, S.C., Pointillist and Glyph-based Visualization of Nanoparticles in Formation. Joint Eurographics/IEEE-VGTC Symposium on Visualization, 2005 ,169-176.

TF97 Thomson, M.G.A., Foster, D.H. Role of Second- and Third-Order Statistics in the Discriminability of Natural Images. Journal of the Optical Society of America, 14, 9, 1997, 2081-2090.

Tho99 Thomson, M.G.A. Higher-order Structure in Natural Scenes. Journal of the Optical Society of America, 16, 7, 1999, 1549-1553.

VB00 Vessel, E., Biederman, I. Picture Preference Habituation. Proceedings of Object Perception and Memory 8th Annual Workshop, 2000.

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Journal of Vision, 2, 7, 2002, 492

VR05 Vessel, E., Rubin, N. When Beauty is in the Eye of the Beholder: Individual Differences Dominate Preferences for Abstract Images but not Real World Scenes. Perception: ECVP Supplement, 2005.

War04 Ware, C. Information Visualization: Perception for Design. Elsevier Science & Technology Books, 2004.

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Appendix I: Experimental Ratings Data for 21 Trials, 54 Plates

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