Tone Mapping HDR Panoramas for Viewing in Head Mounted Displays
Miguel Melo
1
, Kadi Bouatouch
2
, Maximino Bessa
1,3
, Hugo Coelho
3
, Remi Cozot
2
and Alan Chalmers
4
1
INESC TEC, Porto, Portugal
2
IRISA, University of Rennes I, Rennes, France
3
UTAD, Vila Real, Portugal
4
WMG, University of Warwick, Coventry, U.K.
Keywords:
Head Mounted Displays, HDR Panoramas, Tone Mapping, High Dynamic Range.
Abstract:
Head-mounted displays enable a user to view a complete environment as if he/she was there; providing an
immersive experience. However, the lighting in a full environment can vary significantly. Panoramic images
captured with conventional, Low Dynamic Range (LDR), imaging of scenes with a large range of lighting
conditions, can include areas of under- or over-exposed pixels. High Dynamic Range (HDR) imaging, on
the other hand, is able to capture the full range of detail in a scene. However, HMDs are not currently HDR
and thus the HDR panorama needs to be tone mapped before it can be displayed on the LDR HMD. While a
large number of tone mapping operators have been proposed in the last 25 years, these were not designed for
panoramic images, or for use with HMDs. This paper undertakes a two part subjective study to investigate
which of the current, state-of-the-art tone mappers is most suitable for use with HMDs.
1 INTRODUCTION
Panoramic images capture the full 360
of detail in
a scene (Figure 1). When shown within a Head-
Mounted Display (HMD) the viewer can look around
the captured environment as if he/she was there, pro-
viding a highly immersive experience. There can be
a wide range of lighting in any scene, from the sun to
dark shadows. Traditional panoramic imaging tech-
niques, known as Low Dynamic Range (LDR) are
only able to capture a dynamic range of up to 256 to
1 (8 stops) and thus will fail to capture all the detail,
resulting in areas of the panoramic image with under-
or over-exposed pixels. By using 32 bit IEEE floating
point values to represent each colour channel, High
Dynamic Range (HDR) imaging is able to capture the
full range of lighting in a scene.
Worldwide sales of Head Mounted Displays
(HMDs) are increasing rapidly with cumulative to-
tal sales expected to top 200 million from 2015 to
2020 (Tractica, 2017) and the revenue for VR re-
lated hardware is due to exceed $3.6 billion in 2017
(Nafarrete, 2017). The low cost availability of high
quality HMDs, such as the Oculus Rift, HTC Vive,
etc., means that these devices are now being used for
a wide range of applications from computer games
to films. In particular 360
content is becoming
more widely available helped by companies, such as
YouTube which provides the ability to upload and
view 360
videos on their website in March 2015.
Currently HMDs are not HDR, and thus an HDR
image has to be tone mapped before it is displayed
within the HMD. A large number of tone mapping
operators (TMOs) have been presented in the last 25
years (Banterle et al., 2011). However, these TMOs
were not designed for panoramic images and how the
perception of a scene may change as the user looks
around within the environment. This paper under-
takes a detailed two part subjective evaluation of the
use of TMOs for 360
HDR panoramic images. In the
first study, participants were asked to rank according
to their preference a set of tone mapped HDR panora-
mas without reference. In the second part of the
study, the same participants were asked to rank non-
panoramic images tone mapped with the same opera-
tors within the HMD against the reference HDR im-
age displayed on an HDR display. As can been seen in
Figure 1, a 360
HDR panoramic image displayed on
an HDR display would be distorted and thus not be a
fair comparison with the image as it would be viewed
in the HMD. In addition, the study also compared the
performance of two makes of HMD, the Oculus Rift
232
Melo, M., Bouatouch, K., Bessa, M., Coelho, H., Cozot, R. and Chalmers, A.
Tone Mapping HDR Panoramas for Viewing in Head Mounted Displays.
DOI: 10.5220/0006615402320239
In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 1: GRAPP, pages
232-239
ISBN: 978-989-758-287-5
Copyright © 2018 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Figure 1: HDR panorama.
DK2 and the Oculus Rift CV1, to investigate whether
the technical specifications of the HMD itself had an
influence on the choice of TMO for the HMD.
2 RELATED WORK
Tone mapping attempts to preserve the perception of
a scene, captured with an HDR image, on an LDR
display. Many TMOs have been proposed since the
first one was published in 1993 (Chiu et al., 1993),
For a detailed overview of these see (Banterle et al.,
2011). TMOs are typically classifed as global or lo-
cal. Global TMOs apply the same operation to all
pixels in the image. They are typically able to run
in real time. Local TMOs, on the other hand, ap-
ply apply the operator differently to each pixel, taking
into account the pixels around the pixel being consid-
ered. Local TMOs tend to be much more complex
than global ones but they are, unlike global TMOs,
able to preserve both global and local contrast. An
additional category temporal was recently introduced
to take into account how a tone mapper may need to
deal with how a scene changes over time, for example
when tone mapping HDR video (Banterle et al., 2011;
Boitard et al., 2014).
In this paper four state-of-the-art TMOs, which
have previously been shown to be very effective on a
number of different displays (Melo et al., 2014), were
used:
Fer: Developed by Ferweda et al. (Ferwerda et al.,
1996), this TMO includes a model of visual adap-
tation which takes into account aspects of the Hu-
man Visual System (HVS), including visibility,
visual acuity, and illumination changes adapta-
tion. Threshold-versus-intensity (TVI) functions
are used to model photopic and scotopic vision,
while a linear combination of both photopic and
scotopic vision is used for mesopic vision.
Man: This display adaptive TMO uses a model of
the HVS to minimise visible contrast distortions
(Mantiuk et al., 2008). In addition, this TMO
takes into account the ambient light levels and dis-
play characteristics.
Boi: Designed especially for HDR video, this is a
temporal coherent TMO that preserves overall
contrast (Boitard et al., 2012). This is achieved
by considering the perceptual consistency of an
object throughout the video. A frame is processed
in two steps. In the first step the frame is tone
mapped with (any) global tone mapper, while in
the second step, the luminance of the frame is ad-
justed taking into account the lighting of the entire
video.
Pat: Known as the time-dependent visual adaptation
TMO (Pattanaik et al., 2000), this method takes
into account that the HVS does not react instantly
to large changes in luminance levels. The TMO
simulates how the HVS responds to real world
lighting levels.
2.1 Evaluating Tone Mappers
The different TMOs have been evaluated in detail
over the years using both objective and subjective
metrics. The most widely used objective metric has
been VDP (Daly, 1993) and its HDR versions, HDR-
VDP (Mantiuk et al., 2004) and HDR-VDP-2 (Man-
tiuk et al., 2011). Other objective metrics include
VQM (Narwaria et al., 2015) and the dynamic range
independent quality assessment metric (Aydin et al.,
2010).
One of the first subjective evaluations of TMOs
was by Drago et al. (Drago et al., 2002) in which
Tone Mapping HDR Panoramas for Viewing in Head Mounted Displays
233
4 HDR scenes were tone mapped with 7 different
TMOs. Ledda et al. (Ledda et al., 2005) were the first
to use an HDR display as a reference. Tone mapped
images displayed on two LDR displays on either side
of the HDR display were compared with the HDR im-
age on the HDR display. Other evaluations of TMOs
include work by (Yoshida et al., 2005), (Kuang et al.,
2007), (
ˇ
Cad
´
ık et al., 2008) and (Melo et al., 2014;
Melo et al., 2015). Recently temporal TMOs have
been subjectively evaluated by Eilertsen et al. (Eilert-
sen et al., 2013).
2.2 Tone Mapping for HMDs
Cutchin et al. presented an interactive view depen-
dent tone mapping technique for HDR panoramas on
HMDs via a view-adjusted mapping function stored
in a separate texture file, which they termed Tone-
Texture (Cutchin and Li, 2016). This technique was
used to expand the perceived colours within the HMD
in order to improve the panorama’s visual appear-
ance. The approach was shown to provide a bet-
ter visual appearance compared to the panorama tone
mapped with the Photographic TMO (Reinhard et al.,
2002). They provided demonstration systems for We-
bGL and the Oculus Rift and GearVR HMDs.
Yu presented a dynamic TMO for HDR panora-
mas which takes into account the user’s head position
and thus the viewport (Yu, 2015). A simple model
of eye adaptation is used to simulate light and dark
adaptation. Yu’s TMO was shown in a study of only
8 participants to be preferred over the global Photo-
graphic tone mapper (Reinhard et al., 2002).
Perrin et al. (Perrin et al., 2017) undertook a qual-
ity assessment of 360
HDR content when displayed
in HMDs. Three global (Simple linear scaling, Pho-
tographic (Reinhard et al., 2002), Display adaptive
(Mantiuk et al., 2008))) and two local TMOs (Detail
preserving (Mantiuk et al., 2006) and Exposure fusing
(Mertens et al., 2009)) were evaluated using a simple
pairwise comparison. There results showed that the
participants did not have a clear preference for any of
the TMOs evaluated.
3 SUBJECTIVE EVALUATION
This subjective study evaluated four state-of-the-art
TMOs across two consumer-grade HMDs, namely
Oculus RIFT CV1 and Oculus RIFT DK2. The
study was divided into two. The first experiment
consisted of evaluating five HDR panorama images
viewed on the HMD. These panoramas had each been
tone mapped with the four TMOs. Participants were
asked to record their preference. The second experi-
ment asked the participants to rank the TMOs applied
to set of HDR images viewed on an HMD, compared
to the HDR images displayed on a SIM2 HDR dis-
play. A participant only performed the experiments
on one of the two HMDs (between subjects) but ev-
ery participant performed the rankings for both HDR
panoramas and HDR images (within-subjects).
3.1 Sample
A total of 15 participants with ages between 20 and
44 (M = 27.62, SD = 7.38 ) were divided into two
groups. One group performed the experiments using
the Oculus RIFT CV1 (N=7) and the other group per-
formed the experiments using the Oculus RIFT DK2
(N=8). All participants reported normal or corrected-
to-normal vision.
3.2 Variables
There were three independent variables: the HMDs,
the Content Type, and the selected TMOs. The depen-
dent variable is the TMO ranking provided by partic-
ipants. Ranking was preferred over rating as it avoids
narrow distributions by guarantying that each ranked
item has a unique value for all tone mapped stimuli.
Ranking was also chosen over pair-wise comparisons
as this would require a significantly larger number of
comparisons to compare all stimuli.
3.3 Materials
The HMDs used for the experiments were the Oculus
Rift CV 1 that has a resolution of 2160 × 1200, a re-
fresh rate of 90Hz and a field of view of 110
and the
Oculus Rift DK2 with a resolution of 980 × 1080, a
refresh rate of 75Hz and a field of view of 100
. The
stimuli consisted of 5 HDR panoramas, and 5 HDR
images
1
. These were tone-mapped using the 4 dif-
ferent TMOs: Fer, Man, Boi and Pat, as described in
section 2.
Custom VR software was developed to enable
participants to perform the experiments with ease.
This software consisted of different screens (one per
each panorama) where participants could watch ev-
ery tone-mapped panorama the number of times they
needed to perform the rankings. For ranking the tone
mapped panoramas, participants had to drag the cor-
responding tone mapped panorama thumbnails to a
set of ranking boxes numbered from 1 to 4 where 1
1
Please refer to http://www.miguelmelo.pt/hdrhmd/ for
the images used in this study.
GRAPP 2018 - International Conference on Computer Graphics Theory and Applications
234
was the preferred (or closer to the reference in ex-
periment 2) and 4 the less preferred (or less close to
the reference in experiment 2). The experiments were
conducted using a desktop PC with an Intel i7-5820K
CPU, a NVIDIA GeForce GTX 980 Ti graphics card
and 16GB of RAM.
3.4 Procedure
The experiments were conducted in a laboratory set-
ting where all the environment variables such as
sound, temperature and lighting levels were con-
trolled. The first step was to present the experimen-
tal study to the participants without disclosing its pur-
pose to avoid bias. A consent form was given to par-
ticipants for them to agree to take part in the study.
How the software worked was then explained and a
demonstration scene instructed them how to perform
the two-part experiments.
Part 1: Participants were asked to rank 4 TMOs
applied to the HDR panoramas according to their
preference taking into consideration overall image
quality, naturalness and detail across 5 scenes. In
this experiment, there was no reference.
Part 2: Participants were asked to rank 4 TMOs
applied to HDR images according to a reference
that was shown on an HDR display across 5
scenes.
The experimental team helped participants take the
HMDs on and off and remained in the experimental
room in case participants had any questions and to
ensure that the data was collected properly. Partici-
pants were free to drop-out from the experiments at
any given time. On average, the entire procedure took
approximately 15 minutes.
3.5 Statistical Procedures
To study the impact of HMDs across Content Types
over TMOs rankings, a 2 (HMD) × 2 (Content Type)
× 4 (TMOs) mixed design factorial ANOVA was per-
formed where TMO was a within-participant vari-
able and HMD and Content Type were between-
participant variables. A Boxs M test was used to
test the multivariate homogeneity of the data. To in-
vestigate the performance of TMOs across the dif-
ferent scenarios, a Mann-Whitney U was performed.
The Friedman test was carried out to verify if TMOs
rankings were statistically significantly different in
each scenario. Additionally, post hoc analysis with
Wilcoxon signed-rank tests was conducted with a
Bonferroni correction applied, to identify possible
groupings of TMOs. Kendalls Coefficient of Con-
cordance W was also used to give an estimate of
the agreement amongst participants. A Coefficient
of Concordance of W = 1 signifies perfect agreement
amongst the participants and W = 0 indicates com-
plete disagreement.
4 RESULTS
For ease of understanding, the results are divided into
subsections: Results across all HMDs across all con-
tent types; HMD impact on TMO performance for
HDR panoramas; HMD impact on TMO performance
for HDR images; Content type impact on TMOs per-
formance on Oculus RIFT CV1; and ,Content type
impact on TMOs performance on Oculus RIFT DK2.
4.1 Results Across All HMDs Across All
Content Types
To test the multivariate homogeneity, a Box’s M test
was applied which revealed that the within-group co-
variance matrices are equal and that a MANOVA
can be applied (Box’s M = 1.511, p > 0.05). The
MANOVA analysis showed that a statistically signifi-
cant impact of the HMDs over TMOs ranking can be
assumed but with some reservation as there is only a
moderate effect F(4,143)= 2.369, p = 0.055, Wilks
0
λ
= .938, η
2
p
= .062, O.P. =.673. For Content Type, sta-
tistically significant differences were found in TMOs
rankings, F(4,143)= 5.668, p = 0.000, Wilks
0
λ =
.859, η
2
p
= .141, O.P. = .981. For HMD × Con-
tent Type, no statistically significant differences were
found, F(4,143)= .994, p = 0.413, Wilks
0
λ = .973, η
2
p
= .027, O.P. = .308.
4.2 HMD Impact on TMO Performance
for HDR Panoramas
A Mann-Whitney U test showed that there was no sta-
tistically significant difference in the performance of
TMOs across the two HMDs for the HDR panoramas:
Man: U = 596.000, p = .227
Fer: U = 641.500, p = .511
Boi: U = 553.500, p = .105
Pat: U = 558.500, p = .081
As there were no differences of performance across
the two HMDs, the results for the two scenarios were
grouped to perform a Friedman test to analyze if
there were differences across the TMOs. The Fried-
man test revealed a statistically significant difference
between the different TMOs (χ
2
(3) = 70.041, p =
0.000). Post hoc analysis with Wilcoxon signed-rank
Tone Mapping HDR Panoramas for Viewing in Head Mounted Displays
235
Table 1: Wilcoxon Signed-Rank Test results across HMDs
for HDR panoramas.
Paired Comparison Z p
Fer-Man -4.849 0.000
Boi-Man -2.662 0.008
Pat-Man -6.338 0.000
Boi-Fer -2.152 0.031
Pat-Fer -3.979 0.000
Pat-Boi -5.320 0.000
tests was conducted with a Bonferroni correction ap-
plied, resulting in significance levels p < .031 for all
the comparisons as shown in table 1. The TMOs rank-
ing order was: Man (M=1.73), Boi (M=2.13), Fer
(M=2.67), and Pat (M=3.41) with no groupings of
TMOs. Kendalls coefficient of Concordance was W
= 0.311 (p < 0.05).
4.3 HMD Impact on TMO Performance
for HDR Images
A Mann-Whitney U test showed that there was no sta-
tistically significant difference in the performance of
TMOs across the two HMDs for the HDR images:
Man: U = 566.500, p = .113
Fer: U = 624.500, p = .394
Boi: U = 607.000, p = .295
Pat: U = 635.500, p = .436
Again, as there were no differences of performance
across the two HMDs for HDR images, the results
for the two scenarios were grouped to perform a
Friedman test to analyze if there were differences
across the TMOs. The Friedman test revealed a sta-
tistically significant difference between the different
TMOs (χ
2
(3) = 70.041, p = 0.000). Post hoc anal-
ysis with Wilcoxon signed-rank tests was conducted
with a Bonferroni correction applied, resulting in sig-
nificance levels across all TMOs except for the pair
Fer - Man (p = .884) as shown in table 2. The TMOs
ranking order was: Man (M=1.93), Fer (M=1.95),
Boi (M=2.69), and Pat (M=3.44), with Man and Fer
grouped together. Kendalls coefficient of Concor-
dance was W = 0.312 (p < 0.05).
4.4 Content Type Impact in TMOs
Performance on Oculus RIFT CV1
A Mann-Whitney U test showed that there was no sta-
tistically significant difference in the performance of
TMOs across the two content types:
Man: U = 523.500, p = .258
Table 2: Wilcoxon Signed-Rank Test results across HMDs
for HDR images.
Paired Comparison Z p
Fer-Man -0.146 0.884
Boi-Man 3.755 0.000
Pat-Man -6.512 0.000
Boi-Fer -3.314 0.001
Pat-Fer -5.906 0.000
Pat-Boi -3.701 0.000
Table 3: Wilcoxon Signed-Rank Test results across content
types for Oculus Rift CV1.
Paired Comparison Z p
Fer-Man -0.146 0.064
Boi-Man 3.755 0.016
Pat-Man -6.512 0.000
Boi-Fer -3.314 0.498
Pat-Fer -5.906 0.000
Pat-Boi -3.701 0.000
Fer: U = 452.000, p = .051
Boi: U = 564.000, p = .554
Pat: U = 588.000, p = .750
As there were no differences of performance
across the two content types when using the Ocu-
lus Rift CV 1, the results for the two scenarios were
grouped to perform a Friedman test and verify if
there were differences across the TMOs. The Fried-
man test revealed a statistically significant difference
between the different TMOs (χ
2
((3) = 35, 584, p =
0.000). Post hoc analysis with Wilcoxon signed-
rank tests was conducted with a Bonferroni correc-
tion applied, resulting in significance levels across all
TMOs except for the pair Fer - Man (p = .064) and
Boi Fer (p = 495) as shown in table 3. The TMOs
ranking order was: Man (M=1.99), Fer (M=2.31),
Boi (M=2.45), and Pat (M=3.24), with Man and Fer
grouped together as well as Fer and Boi. Kendalls co-
efficient of Concordance was W = 0.169 (p < 0.05).
4.5 Content Type Impact on TMOs
Performance on Oculus RIFT DK2
The Mann-Whitney U test conducted to identify if
there were differences of TMO performance across
the two types of content have revealed that there are
statistically significance differences for Fer and Boi:
Man: U = 691,000, p = .241
Fer: U = 386,000, p = .000
Boi: U = 471,000, p = .001
GRAPP 2018 - International Conference on Computer Graphics Theory and Applications
236
Table 4: Wilcoxon Signed-Rank Test results for HDR
panoramas for Oculus Rift DK2.
Paired Comparison Z p
Fer-Man -4.560 0.000
Boi-Man -1.810 0.070
Pat-Man -5.463 0.000
Boi-Fer -2.292 0.022
Pat-Fer -3.771 0.000
Pat-Boi -4.825 0.000
Table 5: Wilcoxon Signed-Rank Test results for HDR im-
ages for Oculus Rift DK2.
Paired Comparison Z p
Fer-Man -4.560 0.948
Boi-Man -1.810 0.000
Pat-Man -5.463 0.000
Boi-Fer -2.292 0.001
Pat-Fer -3.771 0.000
Pat-Boi -4.825 0.007
Pat: U = 730,500, p = .425
As there were significant differences on TMOs perfor-
mance across the two content types, the TMOs rank-
ing was analyzed separately. For the HDR panora-
mas, a Friedman test revealed a statistically signifi-
cant difference between the different TMOs (χ
2
(3) =
57, 316, p = 0.000). Post hoc analysis with Wilcoxon
signed-rank tests was conducted with a Bonferroni
correction applied, resulting in significance levels
across all as shown in table 4. The TMOs rank-
ing order was: Man (M=1.60), Boi (M=2.01), Fer
(M=2.76), and Pat (M=3.63), with Man and Boi
grouped together. Kendalls coefficient of Concor-
dance was W = 0.478 (p < 0.05).
Regarding HDR images, the Friedman test re-
vealed that there are statistically significant dif-
ferences between the different TMOs (χ
2
(3) =
52, 010., p = 0.000). Post hoc analysis with Wilcoxon
signed-rank tests was conducted with a Bonferroni
correction applied, resulting in significance levels
across all as shown in table 5. The TMOs rank-
ing order was: Man (M=1.78), Fer (M=1.85), Boi
(M=2.83), and Pat (M=3.55), being that Man and Fer
were grouped together. Kendalls coefficient of Con-
cordance was W = 0.433 (p < 0.05).
5 DISCUSSION
This paper has investigated three important research
questions: 1) Is the choice of TMO for displaying
an HDR image affected by the use of an HMD for
the display?; 2) Does content type have an effect
on the choice of TMO for use with an HMD?; and
3)Is the TMO performance consistent across HMDs
across Content Types?
Table 6 summarizes the TMOs Rankings and
groupings across scenarios from the analysed results.
From the statistics of the results we obtained for
our user studies, the following is clear:
The choice of HMD seems to have an impact
on TMO ranking: Although MANOVA analy-
sis revealed a p = 0.055, the null hypothesis is
rejected with some reservation as there is only a
moderate effect of the sample and an OP of near
67%. This latter result corroborates previous stud-
ies such as (IJsselsteijn et al., 2000), (IJsselsteijn
et al., 2001), (Lin et al., 2002), (Ba
˜
nos et al.,
2008), and (Young et al., 2014) which have iden-
tified technology is a factor that influences a VR
experience. Our paper extends this previous work
to now also include the influence of technology
when displaying HDR content on HMDs.
The Content Type has impact on TMO rank-
ing: Depending on the HDR content to be shown,
one should carefully select the appropriate TMO.
For instance, for HDR panoramas, Boi performs
better than Fer but the inverse happens when the
content is HDR images. If one considers the fea-
tures of both TMOs, the results seem to indicate
that local TMOs perform better for HDR panora-
mas while global TMOs are more suited for HDR
images. This might happen because of the dis-
tortion of the source panorama images; a local
processing would consider that distortion and per-
form better tone mapping due use of the neigh-
bouring pixels’ information.
TMOs performance is consistent across scenar-
ios (except for Content Type when the Oculus
Rift DK2): Despite the impact of the different
HMDs and/or content types on the TMOs perfor-
mance, the same TMO will have similar perfor-
mance across conditions.
Concordance between participants on rank-
ings are higher for Oculus Rift DK2 (CV 1
.20 vs DK2 .45): Participants were able to
identify more differences in CV1 between TMOs
therefore there was less concordance. This sug-
gests that less complex TMOs can be used for
older devices as the difference between TMOs is
not so noticeable.
From the statistics of the results we obtained for
our user studies, the global MANOVA and the set of
pairwise comparisons, the following is clear:
The choice of HMD has impact on TMO ranking
Tone Mapping HDR Panoramas for Viewing in Head Mounted Displays
237
Table 6: Overall results obtained for each scenario. Coloured groupings represent TMOs that were not found to be significantly
different using pairwise comparisons to each other, via Bonferroni adjustment, at p < 0.01.
Kendall’s
Co-efficient
of Concordance* 1
st
2
nd
3
rd
4
th
Rankings across HMDs
for HDR Panoramas**
0.311
Man Boi Fer Pat
Rankings across HMDs
for HDR images**
0.312
Man Fer Boi Pat
Rankings across
Content Type for CV1**
0.169
Man Fer Boi Pat
Rankings for HDR panoramas
using Oculus Rift DK2
0.478
Man Boi Fer Pat
Rankings for HDR images
using Oculus Rift DK2
0.433
Man Fer Boi Pat
*
for (p < 0.01)
**
The independent variables were grouped as there was no statistically significant difference
between conditions,p < 0.005.
The content Type (panorama × image) has impact
on TMO ranking
Concordance between participants on rankings
are much higher for DK2 (CV1 20 vs DK2
45). In particular, participants where able to
identify more differences in CV1 between TMOs
therefore there was less concordance. This sug-
gests that less complex TMOs can be used for
older devices as the difference between TMOs is
not so noticeable.
6 CONCLUSIONS
With the rapid growth of VR technology and the in-
creasing demand for more realism within VR experi-
ences, more content is likey to be produced in HDR,
as this has no areas of under- or over-exposed pixels.
This is also very likely to be HDR panoramic content.
Until HMDs include HDR displays, this HDR content
will need to be tone mapped for enhanced display on
the LDR displays of the HMD. This paper has shown
that we cannot rely on previous evaluations of TMOs
to chose the right TMO for use with HDR panoramic
content for HMDs. A new choice of TMO specifi-
cally for HMDs is necessary to achieve a high level of
perceived realism in the HMD, especially when con-
sidering panoramic images.
The subjective evaluation has considered only 5
HDR panoramas. To provide conclusive results more
HDR panoramas need to be considered. In addition,
these panoramas should be chosen from a mixture of
scenes, such as outdoor daylight, outdoor night light,
indoor lighting, mixture of indoor and outdoor lights,
etc. We will investigate this in the future as we wish
to investigate whether the final preference of a TMO
may depend not only on the HMD but also on the con-
tent, for which we will need to capture this more com-
plete HDR panorama database.
Future work will also investigate, including with
the use of eyetrackers, whether more in-depth knowl-
edge of how humans view content on HMD screens,
could lead to even more effective TMOs for display-
ing HDR panoramic content on HMDs than the ones
preferred in our experiments.
ACKNOWLEDGEMENTS
Part of the work was developed at MASSIVE VR
Laboratory established by INESC TEC through the
project REC I/EEI-SII/0360/2012 entitled “MAS-
SIVE - Multimodal Acknowledgeable multiSenSory
Immersive Virtual Environments” financed by the Eu-
ropean Union (COMPETE, QREN and FEDER).
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