perceptual image quality: effects of tone...

Journal of Electronic Imaging 14(2), 023003 (Apr–Jun 2005)

Perceptual image quality: Effects of tonecharacteristics

Peter B. DelahuntExponent Inc.

149 Commonwealth DriveMenlo Park, California 94025

Xuemei ZhangAgilent Technologies Laboratories3500 Deer Creek Road, MS 26M-3

Palo Alto, California 94304

David H. BrainardUniversity of PennsylvaniaDepartment of Psychology

3401 Walnut Street, Suite 302CPhiladelphia, Pennsylvania 19104

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Abstract. Tone mapping refers to the conversion of luminance val-ues recorded by a digital camera or other acquisition device, to theluminance levels available from an output device, such as a monitoror a printer. Tone mapping can improve the appearance of renderedimages. Although there are a variety of algorithms available, there islittle information about the image tone characteristics that producepleasing images. We devised an experiment where preferences forimages with different tone characteristics were measured. The re-sults indicate that there is a systematic relation between image tonecharacteristics and perceptual image quality for images containingfaces. For these images, a mean face luminance level of 46–49CIELAB L* units and a luminance standard deviation (taken overthe whole image) of 18 CIELAB L* units produced the best render-ings. This information is relevant for the design of tone-mappingalgorithms, particularly as many images taken by digital camera us-ers include faces. © 2005 SPIE and IS&T. [DOI: 10.1117/1.1900134]

1 Introduction

Consumers of digital cameras and related products dehigh-quality images. Consumer preference for imaghowever, is not easy to predict. Even if it were technicafeasible, creating a perfect reproduction of the light tharrived at the camera would not guarantee the mostferred rendering of the original scene. For example mprofessional portraiture employs a large degree of imenhancement, and the results are almost always preferra veridical rendering. This may occur because most csumers judge the attractiveness of an image without direference to the original scene, so that their judgmentsbased on memory, either of the specific scene or of genscenes. There is evidence that memory for colored objcan be unreliable.1–3

Paper 03007 received Jan. 21, 2003; revised manuscript received Aug. 7,accepted for publication Aug. 17, 2004; published online May 12, 2005.1017-9909/2005/$22.00 © 2005 SPIE and IS&T.

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Digital images may be modified through the applicatiof image processing algorithms, but what modificatiomake images look better is not well understood. Oneproach to this problem is to study directly the effectimage processing on image preference. We recently exined the perceptual performance of demosaicing algorithin this manner.4 Previous work has also studied the relatibetween image colorfulness and human observer quanaturalness ratings.5–7 Here we apply similar experimentamethods to study the relation between image tone chateristics and perceptual image quality.

Tone mappingrefers to the conversion of input luminance values, as captured by an acquisition device~e.g., adigital camera!, to luminance values for display on an ouput device~e.g., a computer monitor!. Luminance values ina natural image can range over about five ordersmagnitude.8 This compares to a much smaller rangeabout two orders of magnitude available with a compumonitor under typical viewing conditions. Even for thusual situation where the image acquisition device qutizes the number of luminance levels to match the numof levels available on the output device, tone mapping cstill improve the appearance of an image. The relationtween input and output luminance values produced btone-mapping algorithm is called atone-mapping curve.

Tone mapping changes thetone characteristicsof theimage. By tone characteristics we mean the distributionthe luminance values of the image’s pixels, without regato how the pixels are arranged spatially. In general, tocharacteristics can either be assessed globally~over the en-tire image!, or locally ~over some smaller region of interest!. Within an image region~either global or local!, tonecharacteristics are completely described by theluminancehistogramof the region. This specifies the number of ima

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Delahunt, Zhang, and Brainard: Perceptual image quality . . .

pixels within the region that have each possible outputminance value. In this paper, we will consider both globand local tone characteristics.

Previous work on tone mapping has focused on coparisons of the performance of different tone-mappmethods. Much of this work was conducted in the contof film-based photography, where practical consideratilimited attention to global tone-mapping methods in whia single tone-mapping curve was applied to the entireage ~see review by Nelson9!. Bartleson and Breneman10

suggested that a good tone-mapping curve establishedrelation between relative perceived brightness values inscene and the rendered image, where relative brightnewere computed using a modified power function derivfrom research on brightness scaling.11 Their curve corre-sponded closely to curves that received high ratings ipsychophysical study performed by Clark.12 Further workby Hunt and co-workers13,14 suggested that the Bartlesoand Breneman principle10 should be modified depending othe viewing conditions~in particular the surround of theimage! and suggested that although a linear relationtween scene and image relative brightnesses was apprate for reflection prints, a power-law relation between retive brightnesses was more appropriate for transparenThe widely used zone system for photographic tone mping ~reviewed in Reinhardet al.15! relies on perceptuajudgments of how regions in the original scene appearethe photographer.

In film photography, it is not practical to automaticaladjust the tone-mapping curve between images at seplocations within an image, since the shape of these cuis governed by physical characteristics of the emulsionsfilm-development process. With the advent of digital imaing, a wider range of tone-mapping algorithms becomepractical interest. On the other hand, in many digital caeras image quantization precedes the application of a tmapping algorithm, a feature that increases the challenfor successful tone mapping. Thus there has been reneinterest in developing tone-mapping algorithms~see, e.g.,Refs. 8 and 16–18!. Evaluation of these methods has agaemphasized comparing the output of competing algorithA recent study by Dragoet al.,19 for example, appliedseven tone-mapping techniques to four digital imagestheir performance was rank ordered based on observererences.

Algorithms that apply a fixed tone-mapping curve to aimage have the feature that the tone characteristics ofimages produced by the algorithm can vary widely, sinthese characteristics depend strongly on the input. Digimaging presents the opportunity to develop algorithmsing a different principle. Rather than defining the relatioship between input and output luminances, one can spetarget output tone characteristics and apply an imadependent transformation that yields a good approximaof these characteristics. One early digital tone-mappinggorithm, histogram equalization, is based on this idea:algorithm maps the luminance values in the input imageproduce a desired luminance histogram in the output imaAlthough it seems unlikely that the optimal output histgram is completely independent of image content, the pciple of specifying target output image tone characterishas been incorporated into recent tone-mapping algorit


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intended to improve upon histogram equalization. In thealgorithms, the output histogram varies with an analysisimage content.8,16

The work we present here is intended to further explthe idea that effective tone mapping can be achiethrough specification of desired output image tone charteristics. Rather than focusing on the development aevaluation of tone-mapping algorithms, we chose todress the underlying issue of whether we could idenoutput tone characteristics that produce perceptually atttive images, and whether such characteristics dependimage content. To this end, we report the results of timage preference studies and analyze how image preence is related to image tone characteristics.

The work presented here employs images captured wstandard digital cameras and is directed at improvingquality of images produced from such cameras. We doexplicitly consider the case where the dynamic range ofcapture and display devices varies greatly~see Refs. 8, 17,18, and 20!.

As most amateur digital photographs include people,studies employ an image set that consisted mainly ofages of people. We also wanted to include images of peofrom different ethnic backgrounds, since many earlier tomapping studies used images of Caucasians only~e.g. Refs.12, 21, and 22!.

2 Experiment 1

2.1 Overview

Experiment 1 was exploratory, with the goal of identifyinsystematic relationships between tone variables and imquality. We applied four different tone-mapping methodseach of 25 experimental images and measured the pertual quality of the different renderings of each image. Thealgorithms produced output images with a range of tocharacteristics. Image preference was measured usinpairwise comparison procedure. On each trial, observindicated which of two presented images was the mosttractive.

The pairwise comparison procedure is intuitive for oservers and yields reliable data.4 Note, however, that ob-servers only make judgments about different renderingsthe same input image. Thus some analysis is requireaggregate a data set large enough to explore the questiohow an image’s tone characteristics relate to its percepquality. To this end, the preference choice data were alyzed using a regression procedure23 to yield metric differ-ences in image quality between image pairs. The procedyields difference ratings that are commensurate across iimages. We then asked whether differences in specificagetone variableswere predictive of the difference ratingsHere the term tone variable refers to a summary meassuch as mean luminance, that may be computed fromoutput luminance histogram.

We used 25 digitally acquired images and rendered eon a CRT computer monitor using four different tonmapping methods. The four methods produced resultswere perceptually different for most of the images, thproviding variation in image tone characteristics whosefect we could study.

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2.2 Methods: Image Acquisition

Twenty-five images were used in Experiment 1. Twenone were captured in Santa Barbara, California and fwere taken in Palo Alto, California. All of the images wecaptured under daylight, at different times of the dthroughout May 1999. The illuminant was measured immdiately following the acquisition of each image by placingwhite reflectance standard in the scene and measuringreflected light using a Photo Research PR-650 spectraometer. Of the 25 images, 17 were portraits of peoplewere landscapes, and 3 were of objects.

The 21 Santa Barbara images were taken with a KoDCS-200 camera and the 4 Palo Alto images with a KodDCS-420 camera. Both cameras have a resolution of 131012 with RGB sensors arranged in a Bayer mosai24

The DCS-200 captures the input light intensity using 8-linear quantization, whereas the DCS-420 captures withbit precision. The 12-bit values captured by the DCS-4are converted to 8-bit values on-camera via a nonlintransformation. The relative RGB spectral sensitivities aresponse properties of both cameras were characterizedescribed elsewhere.25 This characterization left one freparameter describing the overall sensitivity of the camundetermined, as this parameter varies with acquisitionposure duration and f-stop. The images were croppedmaximum size of 575~w! by 800 ~h! pixels to ensure thatwo renderings of each image could be displayed simuneously on the computer screen used in our experimen

2.3 Image Processing

2.3.1 Dark level subtraction

For the DCS-200, a dark level was subtracted from thequantized pixel values before further processing. The dlevel was estimated from an image acquired with the lcap on and computing the spatial average of the resulimage. The average for the red, green, and blue senwere all 13.5 on the camera’s 8-bit~0–255! output scaleand this is the value that was subtracted. To estimatedynamic range of the images, we compared the minimand maximum pixel values for the green sensor. These tcally occupied the entire allowable output range~approxi-mately 13–255 before dark subtraction!. Given that somepixels had values near zero after dark subtraction, it ispossible to express the dynamic range of these imagesmeaningful ratio.

For the DCS-420, it was possible to linearize the outvalues using a look-up table provided as part of eachimage file. This was done prior to further processing. Aflinearization, the estimated dark level for the DCS-420 wclose to zero and no explicit dark level subtraction wperformed. The dynamic range of these images couldestimated by taking the ratio of the maximum to minimulinearized output value for the green sensor. These ravaried from 17 to 140 across the DCS-420 images usethis experiment.

2.3.2 Demosaicing

Because the two cameras employed a mosaiced dewith each raw pixel corresponding to only one of the thrsensor types, it was necessary to apply a demosiacing arithm to convert the raw mosaiced image to a full co


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RGB image. We used a Bayesian demosaicing algoritdeveloped by Brainard and colleague26–28 and summarizedin a recent paper4 where we evaluated the perceptual quity of demosaicing algorithms.~The performance of theBayesian algorithm is controlled by a number of paraeters. For the application here, the correlation betwnearest-neighbor pixels was assumed to be 0.90, whethe correlation between the responses of different senclasses at the same image location was estimated frobilinear interpolation of the mosaiced image. Finally, talgorithm assumed that there was additive normally distuted pixel noise with a standard deviation for each senclass equal to 4% of the spatial average of responsesthat class. The estimates at each location were obtaineapplying the algorithm to a 535 image region surroundingthat pixel.! The demosaicing results for our images weregeneral quite good, with very few noticeable artifacts.

2.3.3 Color balancing

We by-passed the on-board color balancing of the camand used our measurements of the scene illuminantcolor balance the images. Given the camera’s RGB senrelative spectral sensitivities and the measured illuminawe were able to estimate the relative surface spectral retance of the object at each scene location. This was dusing a Bayesian estimation procedure that will bescribed in a future report. Briefly, we constructed a nmally distributed multivariate prior distribution for objecsurface reflectances by analyzing the Vrehlet al.29 data setof measured surface reflectance functions. The analysislowed closely the method introduced by Brainard aFreeman30 in their work on computational color constancGiven the prior, estimating reflectances from the sensorsponses is a straightforward application of Bayes rule.ing the estimated surface reflectance functions, we cothen synthesize an image that consisted of the CIE Xtristimulus coordinates that would have been obtainedthe surface been viewed under standard CIE daylight Dup to an overall scale factor. This scale factor varied froimage to image depending on the scene illuminant, acqsition exposure, and acquisition f-stop. Uncertainty abthe scale factor is equivalent to uncertainty about the ovall intensity of the scene illuminant and is thus handltransparently by the tone-mapping algorithms that weplied to render the images, which are designed to applyimages captured over a wide range of overall scene lunances. Note that image L* properties reported in this paper refer to L* values for the experimental images diplayed on the experimental monitor, not to L* properties ofregions of the original scene.

To check the accuracy of the color balancing processimage of a Macbeth color checker was taken usingKodak DCS-420 digital camera. Raw RGB values~beforedemosaicing! were extracted for each of the 24 colochecker patches. The Bayes color correction was useestimate the XYZ values of the patches under CIE Dillumination. These estimates were compared with tarvalues computed from measured spectral reflectances ocolor checker patches and the known spectral relative stral power distribution of CIE daylight D65. Here the freoverall scale factor was determined so that the two midgray color checker patches~patch Nos. 21 and 22! matched

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Fig. 1 Tone mapping. The top panel in the figure shows the global L* luminance histogram of theoriginal image. The four panels in the first full row show the histograms after application of the fourtone-mapping algorithms. The four panels in the middle row show the tone-mapping curves used bythe four algorithms for the image shown. The bottom panels show the output images for each of thefour algorithms.

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in average luminance between the color balanced and tavalues. The average CIELABDE 94 difference betweenthe estimated values and directly determined target va~average taken over the 24 patches! was 3.6 units, indicat-ing that the algorithm worked well.

2.3.4 Tone mapping

Four tone-mapping algorithms~Clipping, HistogramEqualization, Larson’s Method, andHolm’s Method! wereapplied to the color balanced XYZ images. These arescribed below. Each method transformed the luminanceeach image pixel while holding the chromaticity of eapixel constant. The relation between a particular measment of input and output luminance is referred to asalgorithm’s tone-mapping curve. In general, the tone

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mapping curve produced by an algorithm depends on imcontent. Each of the algorithms used was global, insense that the same tone-mapping curve was applied toery pixel in the image.

It should be emphasized that our main goal was to usvariety of tone-mapping algorithms that would produce dferent tone-mapping characteristics. The performanceeach algorithm was not of primary concern. All four metods led to acceptable~as judged by the authors! renderingsfor all of the images. In Fig. 1 we show an example of thistograms, tone-mapping curves, and output imagesduced by the four algorithms.

~1! Clipping: For the clipping method, the tonemapping curve relating image luminance to display lumnance was a straight line through the origin. Image lum

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nances that were mapped to display luminances grethan the maximum available on the output device wclipped to the maximum. The slope of the tone-mappcurve was determined so that maximum display luminawas equal to five times the mean luminance of the tomapped image. This clipping method provides a simbaseline that works reasonably well.

~2! Histogram equalization: A widely used method thare-assigns luminance values to achieve a particular taluminance histogram~e.g., uniform or Gaussian! in thetone-mapped image.31 This method efficiently uses the dynamic range of the display device, but can generate imathat have exaggerated contrast and thus a harsh appeaIn our implementation, the target histogram was a Gauscentered at the middle of the output range.

~3! Larson method: A more sophisticated version of histogram equalization. The idea is to limit the magnitudeluminance mapping, so that luminance differences witthe image that were not visible before tone mapping aremade visible by it. Images tone mapped with the Larsmethod generally have a more natural appearancewhen using the traditional histogram equalization metho

~4! Holm’s method~Ref. 16!: Part of a color reproduction pipeline created at Hewlett-Packard Labs for usedigital cameras. We used only the tone-mapping segmenthe pipeline for consistency with the other methods.Holm’s method, the input image is first classified as oneseveral different types~e.g., high key or low key! using aset of image statistics. A tone-mapping curve is then gerated according to the image type and image statistics,this curve is applied to the whole image. This methodcorporates preference guidelines that came from the invtor’s extensive experience in photographic imaging.

2.3.5 Rendering for display

The images were presented on a CRT monitor. Converbetween the tone-mapped XYZ values and monitor settiwas achieved using the general model of monitor permance and calibration procedures described by Braina32

The calibration was performed using the PR-650 spectrdiometer. Spectral measurements were made at 4 nm inments between 380 and 780 nm but interpolated witcubic spline to the CIE recommended wavelength sampof 5 nm increments between 380 and 780 nm. CIE XYcoordinates were computed with respect to the CIE 1color matching functions.

2.3.6 Room and display setup

The experimental room was set up according to the Innational Organization for Standardization Recommentions for Viewing Conditions for Graphic Technology anPhotography.33 The walls were made of a medium gramaterial and the table on which the monitor was placed wcovered with black cloth. The room was lit by two fluorecent ceiling lights~3500 K! controlled by a dimmer switchset at a dim level. The illumination measured at the oserver position was 41 lux. The experiment was controlby MATLAB software based on the PsychophysToolbox.34,35 The images were displayed on a HewlePackard P1100 21 in. monitor~128031024 pixels! drivenby a Hewlett Packard Kayak XU computer.


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2.3.7 Procedure

On each trial of the experiment, observers were shopairs of the same scene rendered via different tone-mapmethods and were asked to choose the image thatfound to be the most attractive. To further explain thisstruction, observers were asked to choose the imagewould select to put into their own photo album. The oservers were also asked to look around the images bemaking a decision rather than focus on just one aspect

The experiment started after a 2 min adaptation periodThree seconds after each pair of images was presentedselection boxes appeared under the images. This 3 s dwas to encourage the observers to carefully consider tdecision. There was no upper limit on response time. Tobservers indicated their preference by using a mousmove a cursor to the selection box under the preferredage and clicking. The observer could subsequently chahis/her mind by clicking on the alternative box. When tobserver was satisfied with his/her selection, he/she clicon an enter button to move to the next trial.

The images were viewed from a distance of 60 cm. Timages ranged in width from 17 to 19 cm~subtending vi-sual angles 16.1° to 18.0°! and ranged in height from 13 to25 cm ~subtending visual angles from 12.4° to 23.5°!. Im-ages were shown in pairs on the monitor, one on theand one on the right. Each image had a border of widtcm which was rendered as the brightest simulated D65luminant the monitor could produce~78 cd/m2!. The re-maining area of the monitor emitted simulated D65 illumnant but at a luminance level of about 20% of the bordregion ~measured at 14.9 cd/m2!.

Using four rendering methods gives six pairwise prestation combinations per image. For the 25 experimentalages, this produces a stimulus set of 150 image pairs.

2.3.8 Observers

Twenty observers participated in the experiment~12 malesand 8 females! with an average age of 31~range 19–62!.The experiment took place at Hewlett Packard Labs in PAlto and the observers were recruited by posting flyaround the building complex. The observers were a mixtof Hewlett Packard employees and outside friends and fily. Only color normal observers participated. Color visiowas tested using the Ishihara color plates.36

2.3.9 Data analysis

The aim of our analysis was to summarize image tone chacteristics using simple tone variables, and to determwhether these variables predicted image preference.hoped to identify systematic relationships between preence ratings and tone variables. Thus our data analysistwo important components: the procedure used to transfthe pairwise image judgments to image preference ratiand the procedures used to extract variables that capimage tone-mapping characteristics.

2.4 Image Ratings

The raw data consisted of pairwise rankings betweenfour different renderings of each image. For each image,used a regression based scaling method23 to convert thepairwise rankings topreference ratingsfor each of the four

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versions. Denote these ratings asp ji where the superscripti

denotes the image (1< i<25) and the subscriptj denotesthe rendering version (1< j <4, algorithms as numbereabove!. Within image, these ratings for the four differeversions of an image are directly comparable. Sincepreference judgments were made across images, howthe ratings across images are not necessarily commerate.

Although we cannot make comparisons of preferenratings across images, we can make such comparisondifferences in preference ratings. Under assumptionswe found reasonable,23 the four ratings generated for eacimage lie on an interval scale. The unit of this scale corsponds to one standard deviation of Gaussian percepnoise that observers are assumed to experience whening preference judgments, and the unit is thus commonthe ratings generated for all 25 images. What differs acrimages is the origin of the scale, which is assigned atrarily by the regression method. To remove the effectorigin, we can computedifference ratingsbetween thej’thand k’th renderings,p jk

i 5p ji 2pk

i (1< j ,k<4). Becausethe rating scale constructed for each image has a comunit, the difference ratings are commensurate acrossages. Thus we can explore whether there are imagecharacteristics whose differences predict difference ratin

From the four renderings for each image, we can tasix pairwise differences. Only three of these are indepdent, however, in the sense that given any three pairwdifferences the other three may be reconstructed. To athis redundancy, we used only the difference ratingsp12

i ,p23

i , andp34i ~1<i<25! in the analysis.

2.5 Image Tone Characteristics

To describe image tone characteristics, we used the L* co-ordinate of the CIELAB uniform color space.37 This mea-sure of luminance is normalized to a white point, andnormalized values are transformed so that equal differenin L* represent approximately equal differences in the pception of brightness. The maximum monitor output~allthree phosphors set at the maximum! was used as the whitpoint for converting image luminance to L* . We consideredtwo summary measures of the L* histogram: the mean L*value and the standard deviation of the L* values. For eachimagei, we denote the mean L* of the j’th rendering valueby m j

i and the standard deviation of the L* values bys ji .

These are both global tone variables, computed fromentire image. Note thatm j

i is in essence a measure of thoverall luminance of the image, whereass j

i is in essence ameasure of image contrast.

A preliminary analysis indicated that to the extent imaquality ratings depended on the tone characteristicsm j

i ands j

i , this dependence was not monotonic. This observamakes intuitive sense. Consider the mean L* valuem j

i . Animage with am j

i value equal to zero will be entirely blacand not provide a satisfactory rendering. Similarly, an iage with a very largem j

i value will be entirely white.Clearly a rendering with am j

i value between the two extremes is indicated. Similar arguments apply tos j

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Here the parameterm0 represents theoptimal valuefor m ji ,

that is the value that leads to the highest image quaacross all images and renderings, and thus deviations om j

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from m0 should correlate with reduced image quality. Simlarly, the parameters0 is the optimal value fors j

i .

2.6 Analysis

As noted previously, our data set does not provide us wdirect access to image quality, but rather to quality diffeence ratingsp jk

i between pairs of images. To ask whethimage tone characteristics predict image quality, we invtigated whether differences between the tone variablesm j

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and s ji predict the difference ratingsp jk

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i ands jk

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i and examined the linear dependence ofp jki

on each of these differences. Since each transformed vable depends on its corresponding optimal value, numerparameter search over the optimal value was used to mmize the predictive value (R2) of m jk

i and s jki .

2.7 Face Images

In follow-up questioning conducted at the end of the eperiment, many observers commented that for images ctaining people, the appearance of faces was an imporfactor in their decision making. For images containinfaces ~17 of 25! we examined the face regions in mordetail and defined face subimages so the tone characttics of these regions could be extracted. The subimawere defined by hand: an example of how a face subimwas defined is shown in Fig. 2.~One image had two facesonly the foreground face was used for this analysis.! Thefaces were of various ethnicities~8 Caucasian, 4 African-American, 3 Asian, 1 Hispanic, and 1 Polynesian!.

For the images containing faces, we repeated our ansis of difference ratings when the tone characteristicspended only on the pixels in the face subimage. We den

Fig. 2 Face subimages were created by cropping the faces out ofthe images as illustrated.

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these difference ratings bym f aceI jki ands f aceI jk

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3 Results

Figure 3 shows the difference ratingsp jki plotted against

tone characteristic differencesm jki ~top panel! ands jk

i ~bot-tom panel! for our entire data set. From the figure, we csee that any systematic dependence of difference ratingm jk

i is weak at best, but that there is a clear dependencthe difference ratings ons jk

i . Note that the negative slopof the dependence shown in the bottom panel of Figmakes sense: if a renderingj is preferred to imagek ~posi-tive difference ratingp jk

i ), then the deviation of imagej’sL* standard deviation from its optimal value is smaller ththe corresponding deviation for imagek ~negative s jk

i ).These conclusions are confirmed by statistical tests onsignificance of the linear relation between thep jk

i and eachindependent variable. TheR2 value for m jk

i is small ~0.07!but significant (p,0.05), whereass jk

i explains a substantial fraction of the variance (R250.31, p,0.001). The op-timal value found form0 was 46.6, whereas that found fos0 was 17.8.

Fig. 3 Prediction of difference ratings from global tone characteris-tics. The figure plots the difference ratings obtained for all images inExperiment 1 against u jk

i (top panel) and s jki (bottom panel).


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i is greater for imagescontaining faces than for nonface images. The four panof Fig. 4 show the difference ratings plotted against them jk

i

~top panels! ands jki ~bottom panels! for the face~left pan-

els! and nonface~right panels! images separately. The linear predictive value ofm jk

i and s jki is significant only for

the face images, and again only the global L* standarddeviation accounts for a substantial proportion of varian~Face images,m jk

i : R250.09, p,0.05; face images,s jki :

R250.58, p,0.001; nonface images,R250.04, m jki : p

50.34; nonface images,s jki : R250.00,p50.83.)

We focused on the face images for further analysis aconsidered whether the local tone variable differenm f aceI jk

i and s f aceI jki extracted from the face region pro

vided additional predictive value. Figure 5 plots the diffeence ratings for the face images against these two ational variables. Both local tone characteristics apredictive of the difference ratings (m f aceI jk

i : R250.59, p,0.001; s f aceI jk

i : R250.29,p,0.001).The analysis previously presented shows that both

global and local~face region! tone characteristics were predictive of image quality: differences in each variable seprately are significantly correlated with the difference raings. We used multiple regression to ask how well all fovariables could jointly predict image quality. The overaR2 when the difference ratings were regressed onu jk

i , s jki ,

m f aceI jki , and s f aceI jk

i was 75%. Stepwise regressioshowed that almost all of the explanatory power was cried by two of the four variables:s jk

i and m f aceI jki . These

two variables alone provided anR2 of 0.72. Figure 6 showsthe measured difference ratings for the face images ploagainst the predictions based ons jk

i and m f aceI jki . If the

two variables were perfect predictors of image quality, tdata would fall along the diagonal line.

Recall that the data analysis involves finding the optimvalues for the tone variabless jk

i and m f aceI jki . Figure 7

shows a plot of how theR2 measure for the face imagevaries with the optimal valuess0 andm f aceI0 . The optimalvalues0 was 17.8, whereas that form f aceI0 was 48.7.

To test if the optimal values varied across ethnicities,divided the images into two groups~8 Caucasian imageand 9 non-Caucasian images! and then re-ran the analysisThe results for the two groups were very similar for famean and standard deviation L* values (m f aceI0 valueswere, Caucasian images: 48.6, non-Caucasian images:and s f aceI0 values were, Caucasian images: 19.2, noCaucasian images: 18.4! but differed somewhat for globaL* standard deviation (s0 values were, Caucasian image15.4, non-Caucasian images: 20.7!. Although the perfor-mance of each of the four algorithms was not of primaconcern in this paper, a summary of the preferencesshown in Table 1 for completeness. Note that Holmmethod performed particularly well overall.

The data from Experiment 1 support the following coclusions: ~i! We were unable to find a tone variable thpredicted perceptual image quality for nonface images.~ii !For face images, a number of tone variables were signcantly correlated with the difference ratings. Two variabaccounted for the majority of the variance in the data t

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Journal of

Fig. 4 Prediction of difference ratings from global tone characteristics, face images (left panels) andnonface images (right panels) shown separately. The difference ratings obtained for all images inExperiment 1 against u jk

i (top panels) and s jki (bottom panels) are plotted.

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we could explain. These were the difference in L* standarddeviations across the entire image (s jk

i ) and the mean L*value difference for the face subimage (m f aceI jk

i ). ~iii ! Thedata allowed identification of optimal values for eachthese variables.

4 Experiment 2

The results from Experiment 1 suggest that for images ctaining a face, the standard deviation of image luminavalues and the mean luminance level of the face itself dgood job of predicting predictive image quality. In Expement 2, we explored the effect of face mean luminancemore detail. We used a diverse set of face images thacluded people with a wide range of skin tones and imawith multiple faces.

4.1 Methods

The methods were the same as for Experiment 1 excepthe following.

4.1.1 Image acquisition

Images were acquired using the Kodak DCS-420 digcamera. Fifteen images were selected, all of which wportraits taken under daylight. Face subregions were a

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identified by hand. Ten contained only one subject~5 Cau-casian, 3 African-American, 2 Asian! and five containedmultiple subjects~1 of Caucasians only, 2 with AfricanAmericans only, and 3 with a mixture of ethnicities!. Forthe images containing multiple faces, the identified fasubregions included all faces. The dynamic range ofimages, computed as described for Experiment 1, vabetween 37 and 245.

4.1.2 Image processing

We wanted to generate rendered images with different fluminance levels with minimal changes to the L* standarddeviation. This was done by applying a smooth global tomapping curve to the images, with the curve paramechosen so that the output images had the desired facegion mean L* and L* standard deviation tone characteritics. Face subimages were selected by hand and 5 versof each image were created with different mean face*target values~42, 48, 52, 56, and 62! and with the L*standard deviation value held fixed at approximately 18Five different renderings per image produced ten posspairwise presentations for each of the fifteen images. Dference ratingsp12

i , p23i , p34

i , p45i and corresponding dif-

ferences in tone variables were used in the analysis.

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4.1.3 Observers

Nineteen color normal observers participated in the expment ~12 males and 7 females! with an average age of 3~range 23–62!. Eight of the observers had previously paticipated in Experiment 1.

4.2 Results

The data were analyzed in the same fashion as weredata for Experiment 1 with respect to the predictive powof the m f aceI jk

i variable. The top panel of Fig. 8 showsscatter plot of the difference ratings against mean faregion L* value differences. For images with multipfaces, the mean face L* value was used. The regressioresults showed that this tone characteristic differencesignificantly correlated with the difference ratings (p,0.001) and that percent variance explained wasR2

50.49. This replicates and extends the results of Expment 1 with respect to this tone characteristic.

After the experiment, observers were given a chanceprovide comments and feedback. In Experiment 2, a nuber of observers noted that some renderings of three ofimages contained visible artifacts in the facial regions, athat these artifacts had a strong negative influence on tpreference for those images. Post-hoc examination of

Fig. 5 Prediction of difference ratings from face-region tone charac-teristics, face images only. The difference ratings obtained for theface images in Experiment 1 against u faceI jk

i (top panel) and s faceI jki

(bottom panel) are plotted.


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images confirmed the observer reports. We believe thetifacts arose because the tone-mapping procedure ampthe noise in some of the darker image regions. Becauseinterest was in tone characteristics, not artifacts, it seemof interest to repeat the analysis with the three problemimages excluded. This led to an increase in the percenvariance accounted for by the face L* mean differencevariable, withR250.66 rather than 0.49. The bottom panof Fig. 8 shows the relation between difference ratings athis variable after the exclusion.

As part of the analysis, numerical search was again uto find valuem f aceI0 that optimizedR2. This value was49.2 when the full data set was analyzed and 46.5 withthree images excluded, both very close to the value of 4found in the first experiment. The dependence of theR2

value on the optimal parameter is shown in Fig. 9 for ttwo cases.

We examined if the optimalm f aceI0 value varied acrossethnicities. The images were divided into two groups~4images of Caucasians, 6 images of non-Caucasians!. Fiveof the images were not included~2 had multiple faces ofdifferent ethnicities and three has visible artifacts in tface region as discussed above!. We re-ran the analysis anthe results for the two groups were very similar (m f aceI0values were, Caucasian images: 46.3, non-Caucasianages: 45.7!.

5 Discussion

5.1 Summary

The paper presents experiments that explore whethenumber of simple image tone characteristics are predicof perceptual image quality. For the nonface imagesstudied, we were unable to identify any such variables.images consisting primarily of faces, however, the resusuggest that the best image quality results when the face*

Fig. 6 Measured difference ratings plotted against difference rat-ings predicted as the best linear combination of s jk

i and ufaceI jki for

the face images of Experiment 1. If the predictions were perfect, thepoints would fall on the diagonal line. The error bars show 61 stan-dard error of measurement for the difference ratings, computed us-ing a resampling method (Ref. 41). The raw preference data wereresampled 50 times. For each resampling, difference ratings werecomputed and the standard deviation of the resulting difference rat-ings was taken as the standard error.

3-9 Apr–Jun 2005/Vol. 14(2)

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luminance is in the range 46–49, and the standard detion of the image L* luminances is approximately 18. Thconclusion was suggested by the results of Experimenand the conclusion concerning the optimal level of face*was confirmed directly in Experiment 2.

The images used in our experiments contained fawith a wide variety of skin tones. Analysis of Caucasiand non-Caucasian subgroups suggest that the concluconcerning optimal face L* level may generalize to a widarray of face images. We do note, however, that our im

Fig. 7 Optimal values s0 and ufaceI0 for the face images used inExperiment 1. Each panel plots the percent variance explained by asingle tone characteristic (top panel: s jk

i ; bottom panel: u faceI jki ) as

a function of the corresponding optimal value (top panel: s0 ; bottompanel: ufaceI0).

Table 1 The overall percentage of times the output of each tone-mapping method was chosen as the preferred image in Experiment1. Results for each algorithm were obtained by taking all of thepairwise comparisons involving the output of each algorithm andcomputing the percentage of times the output of that algorithm waschosen as preferred. Data were aggregated across all images andobservers.

ImagesClipping

(%)Histogram

(%)Larson

(%)Holm(%)

All 26.4 19.0 19.0 35.7

Face 30.0 15.7 16.2 38.2

Nonface 18.8 25.9 24.9 30.4


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sample was relatively small and that follow-up work migprofitably probe the generality of our results. For exampwe do not know how sensitive the data are to the noproperties of the camera sensors. The analysis of theperiment 1 data by ethnicity also suggests that the optiglobal L* standard deviation for the rendered image mdepend on ethnicity, although again the generality of tresult is not clear.

5.2 Other Image Statistics

In addition to the image tone characteristics on whichpreviously reported in detail, we also examined other psible predictors of image quality. These included chromavariables and a histogram difference measure. The higram difference measure increased with the differencetween the luminance histogram of the input and outputthe tone-mapping algorithms. The chromatic variablesnot provide predictive power. This is perhaps not surprisgiven that the images were all color balanced to a commilluminant and that the tone-mapping algorithms did naffect pixel chromaticities. The histogram difference mesure was correlated with image quality for the face imag

Fig. 8 Prediction of difference ratings from face-region tone charac-teristics, for Experiment 2. The difference ratings against u faceI jk

i areplotted. The top panel shows the full data set and the bottom panelsshows the data when three images with artifacts were excluded.

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A stepwise regression analysis, however, showed thating the histogram difference measure to the face L* andimage L* standard deviation did not explain substantadditional variance.

Holm16,38 has suggested that classifying images baon histogram properties and then applying different tomapping depending on the classification can be effectTo explore this, we computed Holm’skey valuestatisticfrom our input image histograms and divided the sceinto two sets, low key and high key, based on this statisLow-key scenes have luminance histograms thatskewed toward dark values, whereas high-key scenesluminance histograms that are skewed toward light valuIn Experiment 1, we found that the relation between gloL* value and image quality was strong for the low-kscenes and not significant for the high-key scenes, whethe relation between global L* standard deviation and image quality was significant for both low- and high-kescenes. There was a difference in optimal global L* stan-dard deviation between the two sets, but this differencenot stable with respect to small perturbations of the crrion key value used to divide the data set. In Experimenthe dependence on face L* values was significant for bothlow- and high-key scenes with the optimal value varyibetween 52~low-key! and 47~high-key!. Further experi-ments focused on the stability of scene key as a modulof optimal tone characteristics, as well as on other potenhigher-order histogram statistics~e.g., degree of bimodality!, would be of interest.

5.3 Relation to Other Work

The work here emphasizes comparisons are among imdisplayed on a common output device, so that the dynarange of the comparison set is constant. This is a reasonchoice for the goal of improving the appearance of imaacquired with current digital cameras, whose image caprange is approximately matched to current display technogy. In contrast, a number of papers have examined tomapping across large changes in dynamic range betw

Fig. 9 Optimal value ufaceI0 for Experiment 2. The plot shows thepercent variance explained by ufaceI jk

i as a function of the optimalvalue ufaceI0 . Thin line: full data set. Thick line: data set when threeimages with artifacts were excluded.


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input and output.8,15,17,18,20,39The experimental methodand analysis presented here are general and could beto evaluate the efficacy of these methods for high-dynarange imagery.

A second feature of our work is our focus on the tocharacteristics of the displayed images, rather than onfunctional form of the tone-mapping curve. The results psented here suggest that there is considerable utility inamining tone characteristics. Other recent experimework19,20 has focused on the efficacy of tone-mapping oeratorsper se. These two approaches may be viewedcomplementary. Also of note is the diverse set of psycphysical techniques that have been employed acstudies.19,20,39Here we have focused on image preferenwhich is conceptually quite different from perceptual fideity.

5.4 Using the Results

Although our positive results only apply to images thcontain faces, such images probably form a large proption of those acquired by the average camera user—mconsumers take pictures of their friends and families. Thour results have the potential for leading to useful practialgorithms.

Since our work shows how preference for images ctaining faces depends on tone variables, tone-mappmethods might profitably include algorithms to identify images that contain faces and to apply appropriate mapparameters to these images.~Face recognition software haadvanced greatly in recent years. See recent reviewPentland and Choudhury40!. Indeed, the present work ledirectly to the development of a novel proprietary tonmapping algorithm at Agilent Laboratories.42 The idea thatempirical image preference studies can enable developmof effective image processing algorithms was also sported by our earlier study.4 We believe further studies holdthe promise of providing additional algorithmic insights.

Acknowledgments

The authors wish to thank Jerry Tietz for help with imaacquisition and Jack Holm and Jeff DiCarlo for help wiimage processing and implementation of tone-mappmethods. Jack Holm also helped with the experiment rosetup. Finally they would like to thank Russell limura, Amnon Silverstein, Joyce Farrell, and Yingmei Lavin for helful suggestions.

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Peter Delahunt received his BS degree inpsychology from Lancaster University, En-gland in 1996. He received his MA andPhD degrees in psychology from the Uni-versity of California, Santa Barbara, in1998 and 2001, respectively. He is cur-rently working as a human factors scientistfor Exponent Inc.

Xuemei Zhang received her bachelor’sdegree in psychology from Beijing Univer-sity, master’s degree in statistics, and PhDin psychology from Stanford University.She is currently a research scientist work-ing in Agilent Technologies Laboratories.

David Brainard received his AB in Physicsfrom Harvard University in 1982. He at-tended Stanford University for graduateschool and received his MS degree in elec-trical engineering and PhD in psychology,both in 1989. He is currently professor ofpsychology at the University of Pennsylva-nia. His research interests include humanvision, image processing, and visual neuro-science.

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