Conference PaperPDF Available

The persistent influence of viewing environment illumination color on displayed image appearance

Authors:

Abstract and Figures

Chromatic adaptation considering competing influences from emissive displays and ambient illumination is a little studied topic in the context of color management in proportion to its influence on displayed image appearance. An experiment was conducted to identify the degree to which observers adapt to the white point of natural images on an emissive display versus the color of ambient illumination in the room. The responses of observers had no significant difference from those of a previous experiment which was conducted with roughly the same procedure and conditions on a mobile display with a significantly smaller viewing angle. A model is proposed to predict the degree of adaptation values reported by observers. This model has a form such that it can be re-optimized to fit additional data sets for different viewing scenarios and can be used in conjunction with a number of chromatic adaptation transforms.
Content may be subject to copyright.
The persistent influence of viewing environment illumination
color on displayed image appearance
Trevor Canham, Marcelo Bertalm´ıo; Universitat Pompeu Fabra; Barcelona, Spain
Abstract
Chromatic adaptation considering competing influences
from emissive displays and ambient illumination is a little stud-
ied topic in the context of color management in proportion to its
influence on displayed image appearance. An experiment was
conducted to identify the degree to which observers adapt to the
white point of natural images on an emissive display versus the
color of ambient illumination in the room. The responses of ob-
servers had no significant difference from those of a previous ex-
periment which was conducted with roughly the same procedure
and conditions on a mobile display with a significantly smaller
viewing angle. A model is proposed to predict the degree of adap-
tation values reported by observers. This model has a form such
that it can be re-optimized to fit additional data sets for different
viewing scenarios and can be used in conjunction with a number
of chromatic adaptation transforms.
Introduction
In the content production industry, those responsible for
mastering content to be distributed attempt to configure their
viewing scenario to not only be reliable and consistent, but also
to mimic the average conditions in which the content will ulti-
mately be seen. For this reason, standards exist which dictate
the parameters for display operation and mastering suite envi-
ronmental setup [1,2,3]. While this paradigm may have been
effective in the more homogeneous distribution landscape which
existed at the time of its development, the only clear way it can be
scaled to maintain its effectiveness in today’s more varied market
is via dynamic, device-side content adjustment methods. While
a number of proprietary methods of this type have been devel-
oped and implemented by display manufacturers in their respec-
tive devices, these have been counterproductive to the creation
of a standard color management ecosystem, as these parties view
this as a way to differentiate their products rather than to pre-
serve the creative intent of mastering artists. In addition, these
systems only address specific parts of the problem and leave oth-
ers out entirely. For example, image appearance shifts due to
viewing conditions (ambient lighting [4], display field of view
and size [5]) and observer perceptual variability [6], to name a
few. A major reason for this is that the variability related to these
factors has not been properly characterized (or even addressed
in some cases) by the scientific community, which is explica-
ble given that these involve complex interactions between human
perception and imaging technology.
The topic of this writing, chromatic adaptation considering
simultaneous influence from emissive displays and ambient illu-
mination, can be included among the understudied. This problem
focuses on identifying the degree to which observers adapt to the
color of ambient illumination over the white point of the display.
Here, we expand on an earlier study [7] which aimed to iden-
tify this for a mobile viewing scenario under a variety of viewing
conditions. Our contributions include further experiments which
add another set of tested conditions, this time using a display oc-
cupying a significantly larger field of view. In the experiment a
series of images are first displayed under a reference condition,
where observers are instructed to take notice of the quality of
their achromatic and memory color regions. Then, the observers
are asked to adjust them back to this color balance from memory
as they are displayed again under varying environmental condi-
tions. To complete this task, observers use a 1D white balance
control which shifts the color balance of images along a linear
pathway connecting the values for the white point of the display
to the color of the ambient illumination.
Using these data and those from the previous experiment,
a model for predicting degree of adaptation in a motion picture
viewing scenario is optimized and validated. This model takes
a similar form to that which was included in CAT02 [8], and
could potentially be used interchangeably with a range of chro-
matic adaptation transforms. The results of our additional tests
show that even when the display is expanded far into the sur-
round portion of the observer field of view, partial adaptation to
illumination conditions persists to a similar degree compared to
the mobile scenario.
Related work
As stated previously, a limited amount of research has been
conducted on the topic of surround chromatic adaptation while
viewing emissive displays. That which does exist presents con-
flicting results, revealing the extensive number of relevant factors
which need to be accounted for in the problem. Several stud-
ies [9,10,11] were conducted in the past decades addressing
this topic for the application of soft copy/hard copy proofing.
Each experiment involved observers performing an asymmetric
matching or achromatic preference task involving natural or syn-
thetic stimuli on an emissive display in a variety of viewing con-
ditions. The results of these three studies averaged over all ob-
servers ranged from 60% to complete adaptation to the display
while testing similar environmental conditions. There were sev-
eral differences between the experiments which could potentially
explain the discrepancy, including the observer task, the stimu-
lus type, and the experimental cadence (adaptation times, etc.).
This is evidence that degree of adaptation data cannot be reliably
extrapolated to different viewing contexts unless these relevant
factors are considered.
This work is an extension of [7], which aimed to character-
ize degree of adaptation to the display versus the surround for
a mobile viewing scenario under six ambient lighting conditions
(10000K, Illuminant A, and 2200K CCTs at brightness levels 5
and 102 cd/m2). In this experiment, observers were presented
a series of images (1 synthetic and 2 natural) on a mobile dis-
play under standard HDTV mastering environmental conditions
(D65, 5 cd/m2) and were later asked to recreate the images via
a 1D white balance control after adapting to the various illumi-
nation conditions. The control scheme was based on a von Kries
[12] mixed-adaptation transform which shifted the white point
of the image on screen along a vector in 1976 u’v’ space con-
necting the display and ambient white points. The results of the
experiment demonstrated that the illumination color, brightness,
and image content (synthetic vs. natural) were all statistically
significant factors, leading to a range of results showing observer
adaptation to be controlled between 26 and 90% by the display,
considering experimental error tolerances.
The most widely recognized implementation of a device-
side, real-time ambient illumination correction scheme is the Ap-
ple True Tone system, which was recently addressed by Wu et al.
in [13]. This writing describes a series of experiments which
were performed to develop the system, as well as the practical
details of its implementation. This included an experiment sim-
ilar to [7] where observers were seated in front of a light booth
and adjusted images on a display via a 1D white balance control
to their achromatic preference. The stimuli set included natural
images, application user interfaces, and text documents. A wide
range of lighting conditions were tested including highly satu-
rated colors, but a consistent luminance level was maintained.
The results of each experiment condition were averaged and
stored as a single degree of adaptation value in a look up table,
allowing for a corrective method to be implemented for which
a prediction for any given illumination color can be interpolated
between tested points. Separate lookup tables were created for
different viewing scenarios, demonstrating that they are specific
to the conditions tested.
Chromatic adaptation
The oldest prevailing model for chromatic adaptation was
proposed by Johannes von Kries [12]. It is calculated in the fol-
lowing way:
Ica=kcIc,kc=1
Icw
f or c L,M,S(1)
where Icais the adapted cone response, Icis the original per
channel cone excitation and Icwis the cone response to the scene
white point. This states that the cone responses after adaptation
are equal to the original responses scaled by those of the adapting
stimulus. In reflection upon his theory, von Kries states that this
is likely a simplification of a significantly more complex process.
While further research proved this to be true, all chromatic adap-
tation models that have followed are expansions of the von Kries
form which further address the complexity of the mechanism in
various ways. In general, these extensions result in changes in
the magnitude of predicted adaptation shifts while maintaining
the direction of the von Kries prediction [14].
A significant finding confirming this greater degree of com-
plexity comes from Hurvich and Jameson [15,16] who identified
the phenomenon of incomplete adaptation. In a study of factors
affecting the perception of achromatic self-luminous fields, the
two discovered that observers were more likely to identify a field
with a D65 white point as achromatic at lower luminance levels
than any other tested white point. The study also revealed that
in the case of white points with a correlated color temperature of
2900K and below, observers did not report the fields as appear-
ing white at any luminance level. Along with evidence from our
own visual experience (ie. we do not completely adapt to a pure
red light source, for example, and perceive it as white over time),
this illustrates the fact that our visual system is not able to adapt
fully to any given illumination color, demonstrating the existence
of some limiting processes in the visual pathway.
Later works from Fairchild [17] and Zhai [18] further ex-
plore this concept by comparing its effects in the context of
emissive and illuminated stimuli. The results showed that ob-
servers were capable of adapting more completely to a range of
colored lighting when viewing illuminated stimuli, implying that
our visual system is more capable of ”discounting the illuminant”
and maintaining color constancy in this scenario than it is when
viewing emissive or self-luminous stimuli (like electronic image
displays). This shows that even our adaptation processes which
occur sub-consciously make a distinction between real environ-
ments and ones reproduced by imaging systems.
The multi-faceted and universal nature of adaptation pro-
cesses is well characterized in the recent review from Webster
[19]. Within, the author pulls examples from the large body of
work on visual adaptation to show that adaptive processes happen
at all stages of the visual system and thus are a combination of
long term, high level cognitive and near-instantaneous low level
physiological processes. To illustrate, Webster calls upon the ex-
ample of luminance adaptation, citing how it combines instan-
taneous gain shifts in the retina along with cognitive adaptation
considering a pool of reference points from multiple surfaces and
fixations. In doing this, the visual system characterizes the aver-
age illumination in the environment allowing for color constancy
to be enforced in response to lighting changes. This implies,
however, that an element of memory and an extended adaptation
time course must be involved in the process. It is hypothesized
that chromatic adaptation behaves in a similar fashion [17].
In order to describe these processes with a simplified set of
input parameters, various groups have proposed the use of de-
gree of adaptation functions to be included alongside practical
chromatic adaptation transforms. These functions return an “ef-
fective” white reference point Iwfor which incomplete adapta-
tion has been considered. This new reference white can then be
passed to any simple chromatic adaptation transform such as that
of von Kries, making it a modular and simple solution for pre-
dicting these complex phenomena. While the first of this type of
consideration was proposed by Hunt [20], the most well-known
is that which is included in the chromatic adaptation step of
CIECAM02 [8]. This function is limited, however, as it only con-
siders the luminance of the adapting stimulus and not its color,
which Hurvich and Jameson clearly show is relevant. Thus, ex-
tensions have been proposed which also include the parameters
of illumination color and saturation along with luminance, such
as that of Lee et al. [21].
Methods
The goal of this experiment is to determine the impact of
surround chromatic adaptation on the color appearance of image
content viewed on emissive displays. As demonstrated by the
studies cited above, this requires at the very minimum some num-
ber of observers to perform an achromatic preference or match-
ing task. In the spirit of testing scenarios that are as close as pos-
sible to the application of aesthetic image evaluation, the chosen
stimuli are natural images viewed on a common reference mon-
itor. The images are first displayed under the SMPTE standard
HDTV mastering environmental conditions as a control (D65, 5
cd/m2), where observers are instructed to take note of the gen-
eral image color balance and the appearance of specific memory
color/achromatic regions. Then the observers are asked to ad-
just them back to this color balance from memory as they are
displayed again under varying environmental conditions.
This adjustment is executed via a 1D control scheme which
alters a mixed adaptation weighting factor din a simple von Kries
based transform (Eq. 1). The concept of mixed adaptation is used
to describe scenarios in which multiple white references exist in
a scene. This involves finding a new effective Iw, which is deter-
mined as some weighted ratio between the chromaticity values
of the two references, calculated as follows:
Iw=dIcd isplay + (1d)Icambient (2)
Thus, the control scheme is a discretized adjustment of image
color balance on some straight line vector in 1976 u’v’ chro-
maticity space which intersects the color of a given ambient con-
dition and the display white point. Thus, it should be noted that
observer responses will be reported as the chosen mixed adapta-
tion ratio d, for which a value of 1 indicates complete adaptation
to the D65 display white point.
To verify that the 1D control was appropriate to describe the
adapted states of observers, an abbreviated version of the exper-
iment was administered to professionals (colorists from the cin-
ema industry.) In this version of the experiment, the observers
were permitted a second dimension of control which would shift
the white point perpendicularly to the 1D control vector. Through
post-experiment discussions, the participants reported that they
could determine an acceptable result given their memory of the
reference without the use of the second dimension of control.
This corroborates the hypothesis that the von Kries mixed adap-
tation transform is acceptably accurate in predicting the direction
of observer chromatic adaptation, allowing observers to report
their adapted state using the control scheme within a visually ac-
ceptable range of error.
This experiment not only includes ambient illumination
chromaticity as a variable, but also image content, dis-
play/ambient brightness ratio, and image starting color balance
are varied between trials (Table 1). Environmental illumination
was varied between three chromaticity and two brightness levels.
Three natural images were used, which were graded under the
reference environmental conditions of the experiment to have
equal primary values for their diffuse white portions and also to
have an appearance similar to what might be seen in television
or cinema content (figure 1). Images are also presented to
observers three times for the same conditions with starting color
balances at different points along the control vector to obtain an
average between presentation biases. The combination of these
variables results in a total of 54 observations per observer during
the body of the experiment.
Table 1: Experimental factors.
Factor Type Levels
Ambient color Varying Ill A, 2200K, 1800K
Ambient luminance Varying 5 cd/m2, 102 cd/m2
Image content Varying 3 natural images
Starting image balance Varying Cool, Middle, Warm
Display luminance Constant 100 cd/m2
Viewing angle Constant 50x28 Degrees (27” diag)
User task Constant Memory Matching
(a) (b) (c)
Figure 1: Experimental Images. Provided by ARRI camera sys-
tems for the HDR4EU project.
The experiment is carried out in the IP4EC mastering lab
at Universitat Pompeu Fabra in Barcelona, Spain. The lab is
equipped with two Arri Sky Panel s30-c LED fixtures which il-
luminate the back wall of the lab. The LEDs work on a four pri-
mary system which is capable of producing light of high spectral
quality and dynamic range [22]. The lights are controlled with
8-bit drive values transmitted via art-net DMX signals using the
Open Lighting Architecture (OLA) framework. OLA commands
are called by a Matlab Psychophysics toolbox [23] test bed which
also displays images according to experimental cadence and ob-
server input. Chromatic adaptation transforms were applied to
images following observer input using pre-calculated 3D LUTs.
Images generated from the workstation (Late 2018 Mac book pro
running OSX 10.8.5) are displayed via HDMI connection on a
Sony PVM-A250 reference monitor calibrated to Rec. 709 pri-
maries and a D65 white point. Observers are seated at a viewing
distance of two picture heights from the surface of the display
resulting in a 50x28 degree viewing angle for the 16:9 full HD
screen, reflecting a real-world mastering scenario. The ambient
illumination of the room was characterized using a Photo Re-
search PR 705 Spectrophotometer. Measurements were taken
with the device aimed at the wall just above the display. Extra
measurements were taken around the screen to confirm a degree
of illumination uniformity on the back wall.
Prior to the experiment all observers were screened for
color discrimination deficiencies and briefed with experimen-
tal instructions aloud. Next, they were led into the experiment
area and were given two minutes to adapt to the reference envi-
ronmental conditions. During each adaptation period, observers
were encouraged to shift their gaze around the room, taking no-
tice of familiar objects like their skin, clothing and belongings.
The test images were then presented in their native color bal-
ances and observers were given as much time as they liked to
memorize the appearance of the achromatic and memory color
scene elements of each one. Next, observers were asked to com-
plete a brief training period introducing them to the experiment
task. The memorization period was then repeated for reinforce-
ment. Following this, the body of the experiment was carried
out. The order of environmental conditions was randomized be-
tween observers to avoid any bias in the results caused by the
proximity of the trial to the memorization period. After adapting
to each environmental condition, observers were presented with
the three images from the memorization phase in three different
initial color balances each. Observers were asked to adjust the
presented image back to their memorized color balance using the
control scheme described above.
A total of 15 observers (11M, 4F) participated in the exper-
iment. Their ages ranged from 23-39 with a mean age of 28.6.
Observers were compensated for their participation.
Results
Average observer mixed adaptation ratios between display
white point and ambient illumination chromaticities are plotted
with 95% confidence error bars in Figures 2and 3. An ANOVA
test showed no significant difference when comparing the mean
degree of adaptation results between the different ambient illu-
mination colors (p = 0.1285). As in the previous experiment, a
significant difference was shown between the user response dis-
tributions to the two brightness levels (p <0.0001) as well as
between the images tested (p = 0.0174).
Corrective Model
In a similar fashion to [8,21] we propose a degree of adap-
tation model considering the illumination color and luminance.
This model is intended to be used for the specific case of adjust-
ing electronically displayed natural images to account for ambi-
ent viewing conditions, and takes the following form:
d=1(αexp(x) + β(La/Ld)) (3)
Here, a value of d=1 would indicate that the observer is fully
adapted to the white point of the display. It takes as input the
Figure 2: Experimental results per condition tested, compared to
results of [7] for common conditions tested, reported as degree
of adaptation to the display.
Figure 3: Experimental results per image tested, reported as de-
gree of adaptation to the display.
ambient/display luminance ratio (La/Ld)and illumination color
(specifically its delta u’v’ [Du’v’] distance xfrom the monitor
white point.) Each factor is fitted with a scalar coefficient which
can be optimized to best fit the data for the conditions tested.
The function returns a degree of adaptation value dwhich can be
used with Eq. 2 to determine the ’effective’ adapting stimulus
Icwas far as mixed/incomplete adaptation is concerned, encod-
ing the effects of high-level cognitive processes. This new adapt-
ing stimulus value can then be passed to a chromatic adaptation
transform such as that of Eq. 1.
An optimization process was performed to find the proper
coefficient values for αand β. The optimization function aimed
to minimize the Du’v’ value between the color corresponding
to the degree of adaptation value predicted by the model and
that which was reported by the observers. We found optimal
coefficient values α=0.1529 and β=0.2419, training for the
data set from [7]. Using these coefficients the function predicts
the responses of the observers from the experiment described
here. The Du’v’ values for both sets are reported in Table 2.
Table 2: Minimum, mean, and maximum Du’v’ prediction errors
for optimization and test sets using the reported coefficients.
Error Optimization Test
Min <0.001 <0.001
Avg 0.007 0.009
Max 0.023 0.024
While it may seem more appropriate to use a perceptual dif-
ference metric like DeltaE for an error function, the use of this
metric requires the specification of an adapting stimulus, and in
this case we are looking at the difference between adapting stim-
ulus values. However for perspective, if we take the delta E 2000
value for the worst prediction from the test set, taking D65 as the
white point, we find an error of 2.2.
Discussion
Despite the shift in display field of view between this exper-
iment and the one previously reported in [7], if we compare the
similar lighting conditions for the previous experiment (isolat-
ing for natural images) a one-way ANOVA comparison returns
no significant difference (p = 0.6375). Figure 2compares mean
responses for each condition between the two field of view set-
tings. For three of the four viewing conditions, the 95% con-
fidence error bars overlap, and neither viewing angle results in
a consistently higher or lower degree of adaptation result than
the other. A possible explanation for this could be that that our
adaptive processes always maintain some consideration for the
current illumination (if any exists) of the environment which we
identify ourselves being located within, even if we are focusing
on an emissive display.
Aside from the ways in which this particular experiment de-
viates from being a perfect simulation of an observer motion pic-
ture viewing scenario, we know that the results likely cannot be
generalized to other viewing scenarios, like user interfaces or e-
books for example. For this reason, the proposed model incor-
porates the scalar coefficients αand βwhich can be retrained
for different observer data sets. This can be done in the same
way as was described above, by searching for new coefficient
values which minimize Du’v’ error between observer responses
and model predictions.
Conclusion
An experiment was conducted identifying the degree to
which observers adapt to the white point of natural images on
an emissive display vs. the color of ambient illumination in the
room. The results were general for all ambient illumination col-
ors tested, and were similar to those of the previous experiment
which was conducted with a drastically reduced viewing angle
for roughly the same conditions. A possible explanation for this
could be that that our adaptive processes maintain some consid-
eration for the current environmental illumination (if any exists)
whenever we view emissive displays, with little regard to the
amount that the display occupies the observer field of view. Us-
ing these data and those from the previous experiment, a degree
of adaptation model was formulated, and optimized. This model
has a form such that it could be re-optimized to fit any differ-
ent or additional data, for instance an expanded set with a larger
number of tested environmental conditions.
Acknowledgments
The authors would like to thank all observers for their par-
ticipation, in particular the colorists from Deluxe Spain who gen-
erously lent their time and spectrophotometer to the experiment.
This work has received funding from the European Union’s Hori-
zon 2020 research and innovation programme under grant agree-
ment number 761544 (project HDR4EU) and under grant agree-
ment number 780470 (project SAUCE), and by the Spanish gov-
ernment and FEDER Fund, grant ref. PGC2018-099651-B-I00
(MCIU/AEI/FEDER, UE).
References
[1] SMPTE ST 2080-1:2014, “Reference White Luminance Level and
Chromaticity for HDTV.”
[2] SMPTE RP 2080-2:2014, “Measurement and Calibration Proce-
dures for HDTV Display Luminance Levels and Chromaticity.”
[3] SMPTE ST 2080-3:2017, “Reference Viewing Environment for
Evaluation of HDTV Images.
[4] C. James Bartleson and Edwin J. Breneman. 1967. ”Brightness per-
ception in complex fields”, JOSA, 57(7):953–956.
[5] Alexandre Chapiro, Timo Kunkel, Robin Atkins, and Scott Daly.
2018. ”Influence of Screen Size and Field of View on Perceived
Brightness”, ACM Transactions on Applied Perception.
[6] Abhijit Sarkar, Laurent Blonde, Patrick Le Callet, Florent
Autrusseau, Patrick Morvan, Jurgen Stauder. 2010. ”Toward Reduc-
ing Observer Metamerism in Industrial Applications: Colorimetric
Observer Categories and Observer Classification”, 18th Color Imag-
ing Conference Final Program and Proceedings.
[7] Trevor Canham, Micheal J. Murdoch, David Long. 2018. ”Influence
of Ambient Chromaticity on Portable Display Color Appearance”,
SMPTE 2018 Annual Technical Conference Exhibition.
[8] CIE. 2004a. ”A Colour Appearance Model for Color Management
Systems: CIECAM02”, CIE TC8-01 Technical Report, CIE Pub.
No. 159.
[9] Mark E. Gorzynski. 1992. ”Achromatic perception in color image
displays”, Thesis, Munsell Color Science Laboratory, Rochester In-
stitute of Technology.
[10] Sharron Henly. 2000. ”Quantifying mixed adaptation in cross-
media color reproduction”,Thesis, Munsell Color Science Labora-
tory, Rochester Institute of Technology.
[11] Naoya Katoh. 1998. ”Effect of ambient light on the color ap-
pearance of softcopy images: Mixed chromatic adaptation for self-
luminous displays”, Journal of Electronic Imaging 7(4), 794–806,
October 1998.
[12] J. von Kries. 1902. ”Chromatic Adaptation”, Festschrift der
Albrecht-Ludwig-Unversitat, Fribourg, [English translation: D. L.
Mac-Adam, Sources of Color Science, MIT Press, Cambridge,
1970].
[13] Jiaying Wu, Lu Zhang, Serhan Isikman, Cheng Chen. 2019. ”En-
hanced Viewing Experience Considering Chromatic Adaptation”,
SID 2019 Digest, 61-1, 857-860.
[14] Tushar Chauhan et al. 2014. ”The achromatic Locus: Effect of nav-
igation direction in color space”, Journal of Vision; 14(1):25, 1-11.
[15] Leo M. Hurvich and Dorothea Jameson. 1951a. ”A Psychophysical
Study of White. I. Neutral Adaptation”, Opt. Soc. Am., 41, 521-527.
[16] Leo M. Hurvich and Dorothea Jameson. 1951b. ”A Psychophysical
Study of White. HI. Adaptation as Variant”, Opt. Soc. Am., 41, 787-
801.
[17] Mark Fairchild. 1992. ”Chromatic Adaptation to Image Displays”,
TAGA 2, 803–824.
[18] Qiyan Zhai, M. Ronnier Luo, Peter Hanselaer, Kevin A.G. Smet.
2016. “Modelling Incomplete Chromatic Adaptation and Colour
Contrast Using Memory Colour”, Color and Imaging Conference.
[19] Michael A. Webster. 2015. ”Visual Adaptation”, Annu. Rev. Vis.
Sci. 1:547-67.
[20] Robert W.G. Hunt. 1987b. ”A Model of Colour Vision for Predict-
ing Colour Appearance In Various Viewing Conditions”, Col. Res.
Appl., 12, 297-314.
[21] Sung-Huk Lee, Myong-Hwa Lee, Kyu-Ik Sohng. 2008. ”Factors of
Incomplete Adaptation for Color Reproduction Considering Subjec-
tive White Point Shift for Varying Illuminant”, IEEE Trans. Funda-
mentals, vol. E91-A, No. 6, June 2008.
[22] Matthew Allard ACS. 2016. ”Hands on with the Arri Sky-
panels – a single operater’s perspective”, News Shooter, On-
line: https://www.newsshooter.com/2016/05/18/hands-on-with-the-
arri-skypanels-a-single-operators-perspective/
[23] David H. Brainard. 1997. ”The Psychophysics Toolbox”, Spatial
Vision; 10:433-436.
Author Biography
Trevor Canham was born and raised in Rochester, NY, where he
went on to receive his B.S. in Motion Picture Science from the Rochester
Institute of Technology in 2018. Since that time he has been working as
a research engineer on dynamic color management systems within the
Image Processing for Enhanced Cinematography group at Universitat
Pompeu Fabra in Barcelona, Spain. His interests lie in the interaction
between human perception and aesthetic imaging systems.
Marcelo Bertalm´
ıo (Montevideo, 1972) is a full professor at Uni-
versitat Pompeu Fabra, Spain, in the Information and Communication
Technologies Department. He received his Ph.D. in electrical and com-
puter engineering from the University of Minnesota, USA, in 2001. His
current research interests are in developing image processing algorithms
for cinema that mimic neural and perceptual processes in the visual sys-
tem, and to investigate new vision models based on the efficient represen-
tation principle.
... Since they allow for a single observer to easily view their device in a wide range of environmental conditions (and as a result, in a range of adapted states), many device manufacturers have explored solutions to adjust displayed imagery to account for these dynamics and render images with a consistent appearance [5]. Concurrently, a series of studies were conducted [9,10] which aimed to characterize the impact of varying ambient brightness and chromaticity on displayed image appearance. In these studies, observers were shown images on displays of varying size first under SMPTE ST 2080 [11] standard viewing conditions (5 cd/m 2 , D65), and later were asked to correct these images back to their original appearance after adapting to a series of lighting conditions. ...
Article
Full-text available
Advancement of color management techniques is required to accommodate for emerging formats and devices. To address this, experiments were conducted by the authors in order to characterize and account for the effect of metamerism error, chromatic adaptation to the surround, contrast adaptation to display dynamic range, and viewing size dependent effects of retinal signal pooling. These topics were assembled to address perceptual representation inconsistencies which are becoming more common with the popularity of mobile, High Dynamic Range (HDR), and Wide Color Gamut (WCG) displays. In this paper, we briefly summarize the findings of these efforts and compile a series of takeaways which are key to the problem of perceptual color management. Furthermore, we discuss the implications of these takeaways on the advancement of the field.
Conference Paper
Full-text available
The share hold of mobile displays in the content distribution market has grown significantly over the past decade. These displays add new complication to media color management as they can be viewed across a wide range of environments over a short span of time. There is currently no consensus within the color science community on the extent to which surround adaptation to ambient chromaticity has a significant impact on the color appearance of image content on these displays. Thus, an investigation into this query has been conducted at the Dynamic Visual Adaptation Laboratory at the Rochester Institute of Technology in Rochester, NY. The study aimed to quantify the color appearance impact of these surround signals. Observers performed an asymmetric memory matching task for a set of images viewed under SMPTE standardized mastering conditions and under a series of ambient illumination conditions with varying chromaticity and luminance. The results suggested that observers adapt partially to the chromaticity of ambient illumination while viewing images on portable displays, and also that this mixed adaptation ratio varies as a function of ambient luminance and stimulus type (self-luminous solid color versus images).
Article
Full-text available
An achromatic stimulus is defined as a patch of light that is devoid of any hue. This is usually achieved by asking observers to adjust the stimulus such that it looks neither red nor green and at the same time neither yellow nor blue. Despite the theoretical and practical importance of the achromatic locus, little is known about the variability in these settings. The main purpose of the current study was to evaluate whether achromatic settings were dependent on the task of the observers, namely the navigation direction in color space. Observers could either adjust the test patch along the two chromatic axes in the CIE u*v* diagram or, alternatively, navigate along the unique-hue lines. Our main result is that the navigation method affects the reliability of these achromatic settings. Observers are able to make more reliable achromatic settings when adjusting the test patch along the directions defined by the four unique hues as opposed to navigating along the main axes in the commonly used CIE u*v* chromaticity plane. This result holds across different ambient viewing conditions (Dark, Daylight, Cool White Fluorescent) and different test luminance levels (5, 20, and 50 cd/m(2)). The reduced variability in the achromatic settings is consistent with the idea that internal color representations are more aligned with the unique-hue lines than the u* and v* axes.
Article
Full-text available
In this paper, we investigated the effect of chromaticity and Iuminance of surround to decide subject neutral white, and conducted a mathematical model of adapting degree for enviroment. Factors for adapting degree consist of two parts, adapting degree of ambient chromaticity and color saturation. These can be applied to color appearance models (CAM), actually improve the performance of color matching of CAM, hence would produce the method of image reproduction to general display systems. © 2008 The Institute of Electronics, Information and Communication Engineers.
Article
Full-text available
With the widespread use of CMSs (color management systems), users are now able to achieve device independent color across different media. However, most of the current CMSs guarantee the same color only if one sees color under a controlled viewing condition. If one sees color under a different viewing condition, the reproduced color does not match the original. The effect of the ambient light on the color appearance of softcopy images is discussed in this paper. In a typical office environment, a computer graphic monitor with a CCT (correlated color temperature) of 9300K is widely used under F6 fluorescent light of 4150K CCT. In such a case, the human visual system is partially adapted to the CRT monitor's white point and partially to the ambient light. A new adaptation model: S-LMS is proposed to compensate for the mixed chromatic adaptation. Visual experiments were performed to evaluate the mixed chromatic adaptation 1, 2, 3. Experimental results indicated that human visual system is 60%
Article
We describe the advanced chromatic adaptation model and framework employed in Apple's True Tone Displays. Combining the theoretical model and empirical user study results, True Tone Display is able to deliver a more color consistent viewing experience.
Article
We present a study into the perception of display brightness as related to the physical size and distance of the screen from the observer. Brightness perception is a complex topic, which is influenced by a number of lower- and higher-order factors—with empirical evidence from the cinema industry suggesting that display size may play a significant role. To test this hypothesis, we conducted a series of user studies exploring brightness perception for a range of displays and distances from the observer that span representative use scenarios. Our results suggest that retinal size is not sufficient to explain the range of discovered brightness variations, but is sufficient in combination with physical distance from the observer. The resulting model can be used as a step toward perceptually correcting image brightness perception based on target display parameters. This can be leveraged for energy management and the preservation of artistic intent. A pilot study suggests that adaptation luminance is an additional factor for the magnitude of the effect.
Article
A previously published model of colour vision predicted the hues, lightnesses, saturations, and chromas of colours seen under a medium photopic level of illumination. This model has now been extended to provide, in addition, predictions of brightness and colourfulness, for both related and unrelated colours, at any level of illumination, whether photopic, mesopic, or scotopic. The model has also been extended to cover a wider range of stimulus intensities and the use of various backgrounds.
Article
Results of brightness-scaling experiments with complex stimulus fields are reported. A family of brightness functions has been computed from the data for complex stimuli such as photographic reproductions viewed with both illuminated and dark surrounds. The resulting brightness vs luminance functions are not simple power functions. They exhibit exponential decay from the power form as a function of both screen luminance and surround luminance; i.e., they are nonlinear in log-log coordinates.