=Paper=
{{Paper
|id=Vol-2688/paper5
|storemode=property
|title=Image Statistics as Glossiness and Translucency Predictor in Photographs of Real-world Objects
|pdfUrl=https://ceur-ws.org/Vol-2688/paper5.pdf
|volume=Vol-2688
|authors=Davit Gigilashvili,Midori Tanaka,Marius Pedersen,Jon Yngve Hardeberg
|dblpUrl=https://dblp.org/rec/conf/cvcs/GigilashviliTPH20
}}
==Image Statistics as Glossiness and Translucency Predictor in Photographs of Real-world Objects==
https://ceur-ws.org/Vol-2688/paper5.pdf
Image Statistics as Glossiness and Translucency
Predictor in Photographs of Real-world Objects
Davit Gigilashvili1 , Midori Tanaka1,2 , Marius Pedersen1 , and Jon Yngve
Hardeberg1
1
The Norwegian Colour and Visual Computing Laboratory, Norwegian University of
Science and Technology, Gjøvik, Norway
davit.gigilashvili@ntnu.no, marius.pedersen@ntnu.no,
jon.hardeberg@ntnu.no
2
Chiba University, Department of Imaging Sciences; Chiba, Japan
midori@chiba-u.jp
Abstract. We interpret our surrounding based on the visual stimuli,
and perceive objects and materials around us to have various attributes,
like color, glossiness, and translucency. We analyze the three-dimensional
world based on the two-dimensional images detected by our retina. The
state-of-the-art works conclude that the human visual system has a poor
ability to fully understand and invert the complex optical nature of light
and matter interaction. Some authors rather propose that the human
brain calculates image statistics to perceive appearance, demonstrating
correlation between perceptual attributes and various statistical metrics.
However, the illustrated examples are usually unrealistic nearly-perfect
stimuli, making real-life robustness of the findings questionable. In this
study, we analyzed image statistics of photos of real world objects, and
assessed the performance of statistical image metrics proposedly used by
the human visual system. We identified very interesting trends, as well
as limitations.
Keywords: Material appearance · image statistics · gloss · translucency
1 Introduction
Appearance is a complex psychovisual phenomenon that implies attributing par-
ticular characteristics to surrounding objects based on the interpretation of the
visual data. CIE 175:2006 [23] (as quoted in [5]) defines appearance as ”the visual
sensation through which an object is perceived to have attributes as size, shape,
colour, texture, gloss, transparency, opacity, etc.” The CIE identifies color, gloss,
translucency and texture as four major appearance attributes [23]. Appearance
measurement has been developed towards hard metrology, i.e. instrumental mea-
surements [12, 18, 30], and soft metrology relying on psychophysics [8, 22, 27].
Copyright c 2020 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0). Colour and Visual Com-
puting Symposium 2020, Gjøvik, Norway, September 16-17, 2020.
2 D. Gigilashvili et al.
While we orient ourselves in the 3-dimensional world we still interpret the en-
vironment based on the 2D retinal images. And we do pretty well: humans with
normal vision can easily distinguish glossy and matte or translucent and opaque
objects; furthermore, we are good at identifying materials, easily distinguishing
ceramics from wax or human flesh from plastic dummies. Although multisensory
information, like tactile or auditory, facilitate this process, the crucial amount of
information is extracted from the above-mentioned 2D retinal images. Fleming
and Bülthoff [6] have proposed that the human visual system (HVS) has poor
optics inversion abilities, and that it relies on simple image cues to interpret
material properties. Motoyoshi [21] tried to correlate image statistics with ma-
terial properties, and found indications that skewness, or a similar measure of
luminance histogram asymmetry, might be used by the HVS to judge surface
properties. The finding is further supported and manifested by Landy [15]. Mar-
low and Adelson [16] demonstrated that sharpness, contrast and coverage area
of the highlights are correlated with perceived level of glossiness. Qi et al. [24]
tried to find correlation between perceived glossiness and various statistics of
specular highlights, like spread, size, number, strength, and percentage cover-
age, and found a statistically significant correlation between the percentage of
the highlight coverage and perceived glossiness.
Image statistics have been used for studying perceived translucency as well.
Motoyoshi [20] manipulated images of various materials and concluded that
“spatial and contrast relationship between specular highlights and non-specular
shading patterns is a robust cue for perceived translucency of three-dimensional
objects”. On the other hand, it has been also shown that image statistics alone
do not entirely explain the complex nature of appearance perception and they
are usually subject to multiple photo-geometric constraints [2, 13, 14, 16].
Although the above-mentioned findings are interesting, they are oftentimes
based either on the synthetic stimuli, rendered in constrained and unrealistic
environments, or few photographs taken in limited conditions. The studies using
large photograph databases have no access to the physical ground truth of the
material (e.g. [25, 31]), while wherever the ground truth is available, the number
is stimuli is low (e.g. [21]).
The novelty of this study is using a photograph dataset with full access
to the ground truth physical stimuli. We had a particular motivation for using
photographs in this study. The vast majority of the authors using computer gen-
erated stimuli do not account for imperfections and artifacts present in the real
world. As computer vision emerges, with autonomous vehicles among the most
prominent applications, in-the-wild performance of particular metrics becomes
vitally important for material identification. Therefore, we decided to extract
image statistics not from the synthetic stimuli, but from photographs of real
world objects coming with unintended artifacts, and to study the robustness of
image statistics, as predictors for actual material appearance. We photographed
objects with varying degree of gloss and translucency and described them with
statistical metrics known to be correlated with them.
Image Statistics as Glossiness and Translucency Predictor 3
The paper is organized as follows: in the next section, we present the acquisi-
tion setup and methodology. The results are presented and discussed in Sections
3 and 4, respectively. Finally, we draw conclusions and summarize the potential
directions for the the future work.
2 Methodology
2.1 Stimuli
We photographed spherical resin objects from the Plastique collection [28]. The
objects have been created by an independent artist with an intention to be used
in material appearance research. The resin substrate material is colored with
different combinations of blue, yellow and white colorants, followed by different
levels of surface processing (polishing). The objects come in three levels of surface
coarseness that affects apparent gloss of the materials. We photographed 30
spheres in total with 3 different levels of surface roughness, 3 hues, and various
levels of translucency (Fig. 1). It is worth mentioning that the objects have
several visible artifacts, like scratches on the surface and bubbles in the volume,
that make them good targets for testing the robustness of image-based metrics.
The close-ups of some of the objects are shown in Fig. 2. Renderings of spherical
Fig. 1. The resin objects used as targets. Column A - objects with smooth surface;
Column B - rougher objects; Column C - the roughest among the three. Objects in the
same row are made of the identical material and differ only in surface processing.
4 D. Gigilashvili et al.
objects are very commonly used in computer graphics for studying appearance
(e.g. [22, 29]), and a simple curved shape of a sphere ensures apparent specular
reflections, as well as distinctness of image gloss, that are very widely used cues
for glossiness assessment by the HVS [8, 9, 22].
2.2 Image Acquisition
The objects were photographed in a GretagMacbeth Spectralight III viewing
booth under diffuse D50 illumination with around 4900K color temperature. The
illuminant is placed on the ceiling of the viewing booth, placing all objects under
top-lit geometry - the most commonly encountered illumination geometry, both
outdoors under sunlight, as well as in an office environment. The light intensity
on the bottom of the viewing booth was 1858 lux, as it was directly exposed
to the light, while it was 900 lux on the background. The acquisition setup is
shown in Fig. 3.
The objects were placed on a white matte paper. Metal rings were used to fix
the position of the spherical objects. In order to avoid possible bias from highly
specular metal rings, they were covered with a white tape sticker. The imme-
diate background of the object was white for opaque objects, while translucent
objects were photographed twice, with black and white backgrounds. A Nikon
D3200 camera was used with ISO 100, shutter speed 1/250 sec., F-stop 2.2, and
50mm focal distance. The object was located around 50 centimeters away from
the camera. The camera was characterized using a MacBeth ColorChecker. The
estimates of CIE XYZ values were obtained by a regression-based method us-
ing manufacturer-provided and camera-acquired color coordinates of the color
checker patches. The color correction matrix was found by the least squares ap-
proximation. The spheres were segmented from the images of 3008×2000 pixels.
We are aware of the limitations related to the acquisition pipeline. Although
the camera response function (CRF) has not been measured or estimated, the
non-linearity of the CRF that is typical to consumer cameras might have affected
the results. It is especially worth highlighting that the limited dynamic range
of the acquisition system and clipping of the high luminance information could
have impacted the recorded luminance histogram and its statistical moments.
2.3 Analysis of the Data
Only manually segmented images were studied and the background is not in-
cluded in the statistics. It has been proposed that chromatic information has
negligible impact on gloss perception [9, 22, 27]. In depth analysis of this is be-
yond the scope of this work. We assume that the vital portion of the information
needed for glossiness estimation is embedded in luminance, and therefore, ana-
lyze the luminance channel Y from CIE XYZ. We found luminance histograms
for each of the segmented objects and calculated the first four moments of it.
Finally, the following statistical measures have been considered for the analysis:
skewness and kurtosis of the luminance histogram, coverage of the highlights,
Image Statistics as Glossiness and Translucency Predictor 5
Fig. 2. The difference in contrast as well as in the reflected image is apparent between
the dark blue and yellow smooth-surfaced objects (A and B). The object shown in
illustrations C and D is the same, but its appearance differs due to the change in the
background color. Some artifacts and bubbles are visible in image D.
Fig. 3. The setup used for image acquisition.
mean luminance of the object, and standard deviation of the luminance distri-
bution. The coverage was defined as the percent of the total surface covered by
the areas which were larger than 20 pixels and had luminance value above 0.9
(luminance is normalized to 0-1 range, 1 corresponding to the largest luminance
recorded by the acquisition system. We do not report cm/m2 measurements). A
correlation between gloss and the size of the highlights has been reported in the
literature [16, 17]. Finally, we used these five statistical metrics for clustering the
objects.
3 Results
The images of the 30 objects are shown in Figure 1. Objects shown in rows 11
and 12 are the same as the ones in rows 6 and 8, respectively, but photographed
6 D. Gigilashvili et al.
Fig. 4. Luminance histograms for segmented images. Top row - dark blue objects (row 1
in Fig. 1); bottom row - white objects (row 7 in Fig. 1). Column A - smoothest objects;
Column C - roughest objects. The histograms show that the smoothest objects are
positively skewed. As the mean luminance is lower, the skewness is stronger for the
dark blue one. The histogram of the roughest white object is negatively skewed.
9
8
7
6
Skewness
5
4
3
2
1
0
-1 1 4 7 10 13 16 19 22 25 28 31 34
Object number
Fig. 5. Skewness of the luminance histogram for each segmented object (as to be
counted from left to right, and top to bottom, in Fig. 1. Clear regularity of the triplets
is visible in the pattern.
with the black background. Two major histogram asymmetry metrics have been
studied: skewness and kurtosis. How the luminance histogram varies among dif-
ferent colors and levels of surface roughness is illustrated in Fig. 4. The results
for skewness are shown in Fig. 5-6. As we see from the plots, the luminance
histogram of the objects with smoother surface, i.e. higher gloss (difference in
perceptual glossiness is apparent among the three levels of surface coarseness,
although not quantified psychophysically), has always a positive skew, and the
skewness is higher than that of the rougher, i.e. less glossy objects. Skewness
Image Statistics as Glossiness and Translucency Predictor 7
difference between the two other surface levels is visible, but not large. A clear
regular pattern for the triplets is visible in Fig. 5. If we refer to rows 1-4 in Fig. 1,
the objects vary from darkest blue in row one, to lightest blue in row four. As
we increase lightness of the object, the skewness of the luminance histogram
decreases. Row 7 stands out on the plot with its low histogram skewness. This
can be explained with the fact that the object is white, close to the illumination
color. As the specular reflections on the surface are also whitish, they cause less
skew in the luminance distribution, than for the low luminance bluish objects,
where the tail of the distribution was high luminance specular highlights.
9
A B C
8
7
6
Skewness
5
4
3
2
1
0
1 2 3 4 5 6 7 8 9 10 11 12
-1
Row in the figure
Fig. 6. Skewness of the luminance histogram for each segmented object. Each curve
corresponds to each level of surface coarseness, as shown in columns in Fig. 1. Numbers
in horizontal axis correspond to the rows in Fig. 1.
While skewness measures asymmetry towards particular direction, either pos-
itive, or negative skew, kurtosis measures general ”tailedness” of the distribution
in both directions. Kurtosis for glossiest class of the objects is highest, and gener-
ally follows the same pattern, as it is for the skewness (refer to Fig. 7). However,
the distinction between the two other classes is negligible with this measure.
The surface coverage by specular highlights was equal to zero for all rougher
objects (columns B and C in Figure 1). The only exception was row 7, where the
whitish color of the object biased our calculations and led to unreasonably many
false positives. On the other hand, the coverage did not differ significantly among
the smooth objects (column A), and the specular highlights covered around 0.8%
of the total visible area of the sphere.
Mean luminance for each object is summarized in Fig. 8. Studying mean
luminance can be interesting for two reasons: first of all, overall shininess of the
object, as observed in [9], can evoke gloss perception in itself; secondly, it has
been demonstrated [16, 22] that contrast between specular and diffuse areas, has
significant impact on perceived gloss. Considering that specular highlights are
white and nearly identical on all objects, we assume that mean luminance of
the object is inversely correlated with the contrast gloss - i.e. higher the mean
8 D. Gigilashvili et al.
90
A B C
80
70
60
Kurtosis
50
40
30
20
10
0
1 2 3 4 5 6 7 8 9 10 11 12
Row in the figure
Fig. 7. Kurtosis of the luminance histogram for each segmented object. Each curve
corresponds to each level of surface coarseness, as shown in columns in Fig. 1. Numbers
in horizontal axis correspond to the rows in Fig. 1.
luminance of the entire object, lesser is the contrast between specular and diffuse
areas. The objects with smoothest surface have less mean luminance than objects
made of the identical material but with rougher surfaces. This can be explained
with the fact that the substrate white material is exposed to the surface due to
scratches, artifacts and irregularities presented on the rough surface.
0.9
A B C
0.8
0.7
Mean luminance
0.6
0.5
0.4
0.3
0.2
0.1
0
1 2 3 4 5 6 7 8 9 10 11 12
Row in the figure
Fig. 8. Mean luminance for each segmented object. Each curve corresponds to each
level of surface coarseness, as shown in columns in Fig. 1. Numbers in horizontal axis
correspond to the rows in Fig. 1.
In contrast with the findings by Wiebel et al. [31], standard deviation of
the luminance distribution is a poor predictor for surface coarseness class in
our study (refer to Fig. 9). However, it significantly rises when the background
Image Statistics as Glossiness and Translucency Predictor 9
of the object is changed from white to black. This might be a good indication
that impact of the background on the luminance variance is a result of volume
scattering - thus, used as a predictor of translucency for see-through objects.
In addition to this, we hypothesize that complexity of the scene might impact
the statistics. We photographed objects in one additional condition placing a
checkerboard-covered cube close to the object (the setup is shown in Fig. 10).
The general trend is that rougher the surface is, the smaller the impact of the
cube on statistical measures. This can be explained with the fact that the smooth
surface has a good distinctness-of-image reflection, and the image reflected from
the surface significantly impacts the statistics, while rough surfaces diffuse the
light and no pattern is visible on the surface reflections. This trend deserves
further attention.
Afterwards, we compared the metrics for the identical objects between white
and black background photographing conditions (the results for rows 6 and 8 are
compared with the results for rows 11 and 12, respectively). Interestingly, skew-
ness and kurtosis decrease when the background is changed to black. To some
extent, this can be accounted for many white-colored artifacts of the object which
are visible on the black background only. As expected, mean luminance is de-
creased for black background due to absorption of the energy by the background,
and thus, less back-reflections. Finding the ratio of the luminance measured on
white and black backgrounds is an established technique for transmittance mea-
surement of the flat objects (e.g. [10]). This observation holds at some extent
for spherical objects as well. Also, as already discussed above, standard devia-
tion changes significantly due to change in the complexity of the background.
It has been demonstrated [9] that translucency, when objects are placed on a
white background, can make objects look glossier. Here we observe that white
background leads to more skewed luminance histograms that itself is proposedly
related to gloss. Therefore, there might be a gloss-translucency cross-attribute
interaction that is described by changes in image statistics. However, this needs
further experimental evidence.
Finally, we conducted clustering to validate our hypothesis that the five sta-
tistical measures are good predictors for object class (smooth, moderately rough,
and highly rough surfaces). We used k-means clustering with 3 clusters. Falsely
detected highlight coverages for objects in the seventh row were manually set
to 0. The cluster was defined as the centroid being the mean of all points in that
particular cluster. Maximum number of iterations was set to 1000. Cluster cen-
troids were initialized using k-means++ algorithm [3]. All objects with rougher
surfaces (columns B and C) ended up in the same cluster. A small separate clus-
ter was objects 1A and 2A, i.e. dark blue objects with low mean luminance, with
the highest positive skew in luminance histogram. Four smooth-surfaced objects
7A, 10A, 11A, and 12A were clustered together with rough objects. While all
other smooth objects were grouped together in a separate cluster. Clustering
gives us an indication that five variables, the five statistical descriptors we use,
might be enough to separate very smooth and glossy objects from rougher and
less glossy objects. However, they fail describing intra-group differences.
10 D. Gigilashvili et al.
0.2
A B C
0.18
0.16
Standard Deviation
0.14
0.12
0.1
0.08
0.06
0.04
0.02
0
1 2 3 4 5 6 7 8 9 10 11 12
Row in the figure
Fig. 9. Standard deviation of the luminance histogram for each segmented object. Each
curve corresponds to each level of surface coarseness, as shown in columns in Fig. 1.
Numbers in horizontal axis correspond to the rows in Fig. 1.
4 Discussion
We have observed that as the surface becomes rougher, skewness and kurtosis
of the luminance histogram decrease, and the distribution becomes less tailed.
While glossy objects look solid opaque, like billiard balls, rougher surfaces look
milkier, and at some extent evoke illusion of subsurface scattering via surface
scattering only that is not surprising considering that the HVS has poor optics
inversion ability [6]. This can be an indication in support for Motoyoshi’s pro-
posal [20] that blurring non-specular regions, i.e. squeezing the tails towards the
center, reflected in decreased skewness and kurtosis, can enhance translucency
perception. On the other hand, it has been observed earlier [7] that translu-
Fig. 10. A cube next to the object is covered with a checkerboard texture that is
reflected on the surface of the sphere affecting its image statistics and appearance.
Image Statistics as Glossiness and Translucency Predictor 11
cency perception declines with blurring the entire object or image, i.e. when we
decrease variance and histogram asymmetry. However, this proposal certainly
needs validation psychophysically.
Although specular highlights are small and very simple in texture (just sat-
urated blobs), covering less than 1% of the total visible area of the sphere, they
strongly skew the luminance histogram, and evoke strong perception of gloss.
Interestingly, van Assen et al. [4] have studied photographs with various pat-
terns of highlights (disk, square, window etc.), and found that simpler specular
highlights evoke stronger gloss perception than more complex ones. However,
the role of the highlights should not be exaggerated, as the perception of gloss is
a complex cognitive process and neither specular reflections are the only source
of the highlights, nor all highlights evoke perception of glossiness. To demon-
strate this, we have superimposed specular highlights of a smooth surface to
a rougher surface of the identical materials (Fig. 11). In one case, the target
rough object has relatively homogeneous texture, while in the other case, there
are very apparent scratches and visual artifacts that help observers deduce the
surface composition of the object. While glossiness for the former object looks
reasonably realistic, the latter object does not look glossy as the highlights start
looking more like artifacts. Presence of roughness cues limit perception of glossi-
ness, although the statistical metrics are similar to that of glossy objects. This
once again demonstrates photo-geometric constraints limiting the usage of image
statistics as an appearance predictor. Interestingly, the HVS can still be tricked
in particular scenarios when additional cues are missing (the manifestation of
this phenomenon is the viral glossy legs illusion [19]).
Fig. 11. While both highlights are artificial, the left object looks glossier due to the
lack of artifacts, while the scratches help us know the right object is not smooth, i.e.
not glossy.
Hunter [11] names contrast gloss, i.e. contrast between specular and diffuse
areas, among one of the types, or dimensions of gloss. Pellacini et al. [22] have
demonstrated that darker objects look glossier than lighter ones, and this effect
has also been observed in other studies [9, 16, 29]. Although we did not have a
direct measure for contrast in this work, considering that highlights were nearly
identical among objects, we assumed that mean luminance of the entire object is
inversely correlated with the contrast gloss. It has been demonstrated that up-to
12 D. Gigilashvili et al.
some threshold rough and light surfaces might look glossy [8, 24]. Moreover, it
has been proposed that luminance information associated with shininess might
significantly increase perceived glossiness [9]. Although mean luminance alone
cannot be a good predictor for apparent gloss of the materials, it might carry
rich information regarding contrast and distinctness-of-image (another dimen-
sion of gloss according to Hunter [11, 12]), and could be eventually included in
the perceptual gloss model.
Standard deviation turned out a poor predictor of surface roughness class
in our study. This interestingly contradicts with Wiebel et al. [31], who studied
natural images, observed a strong positive correlation between standard devia-
tion of luminace histogram and gloss, and found it a better predictor for gloss
than skewness. Although we have not conducted perceptual measurements of
our stimuli, we can draw some qualitative parallels. The inconsistency can be
explained with the type of objects depicted in authors’ natural images. If we
examine the images illustrated in [31], we notice that images considered glossy
consist of large segments of contrasting luminances, i.e. photographed complex
shaped objects yield high number of pixels with highlights and also high number
of dark pixels with shading - leading to large standard deviation. Unlike theirs,
the highlights covered less than 1% of our stimuli, while the luminance gradient
on the rest of the sphere was relatively homogeneous. This led to strong skew but
was not enough for yielding high standard deviation in the luminance histogram.
Distinctness-of-the-reflected-image, the mirror image of the surrounding we
can see on very glossy surfaces is another cue for glossiness. The background
and surrounding vary dramatically in dynamic scenes, and hardly ever are as
simple in real life, as studied in the laboratory conditions. Image statistics are
neither static, nor consistent among different conditions. We observed that even
a minor change in the environment (Fig. 10) can affect image statistics that
makes its possible use by the HVS and even by machines, questionable. On the
other hand, appearance is also dynamic; even though the HVS has ability to
perceive some appearance attributes consistently across different conditions, i.e.
demonstrates some constancy (e.g. color constancy), the constancy is valid up-
to certain extent only, and completely fails in many conditions. While the vast
majority of the studies trying to explain appearance with image statistics rely
on a few images in very limited conditions, it remains an open question how
appearance and image statistics co-vary. We have shown above that particular
image statistics are promising and deserve further attention, but for more solid
conclusions, psychometric measurements are needed. Understanding how image
statistics correlate with perceived appearance can be beneficial in two ways:
– It can unveil further mechanisms that are used by the HVS to interpret the
surrounding.
– It can have commercially significant applications in computer vision. Many
image statistical metrics are extremely efficient computationally, and might
be used for material identification and quality assurance. Moreover, general-
ity across different conditions might not be the mandatory requirement for
image statistics. Many computer vision applications are limited to very spe-
Image Statistics as Glossiness and Translucency Predictor 13
Fig. 12. The object shown in the left and right photos is the same. However, even a
slight change in illumination angle leads to dramatic changes in its appearance. If the
smoothest (region A) and the roughest (region B) surfaces are distinguishable in the
left image, they (regions C and D) look nearly identical in the right one due to dynamic
range limitations.
cific conditions by nature, and limits of particular statistics might not have
vital importance, as long as a correlation between statistics and appearance
is established for given (application-specific) conditions.
Finally, we should mention that above-discussed variation in luminance dis-
tributions was observed due to the curvature of the spherical objects. Our find-
ings might not be applicable to other surfaces, especially to the planar ones.
To demonstrate this, we tried photographing flat plastic and metallic samples
from the JIDA Standard Sample dataset [1]. We have observed two interesting
phenomena that made studying image statistics of these samples unreliable:
– Because the surface is flat, all points on small objects are under approxi-
mately the same illumination geometry that makes it impossible to see spec-
ular and diffuse areas separately, and the entire part of the patch looks rather
homogeneous, essentially cutting down the luminance histogram to a single
luminance value. This can be seen in the left image of Fig. 12, where the left-
most part of the patch (region A) is smooth and glossy, albeit homogeneous
under given conditions.
– The samples, especially the metallic ones, are extremely prone to appear-
ance changes even with a slight change in illumination geometry. This is
demonstrated in Fig. 12.
Although haze and absence-of-textures on low gazing angles (further dimen-
sions or types of gloss) could be observed on the flat patches, these phenomena
are beyond the scope of this work and should be addressed in the future.
5 Conclusion and Future Work
We have taken photographs of real world objects and studied correlation between
image statistics and actual physical surface properties. Although very clear pos-
14 D. Gigilashvili et al.
itive skew of the luminance histogram is characteristic for smooth (and presum-
ably glossy) surfaces, the robustness of the metric is challenged by complexity
of the surrounding and semantic understanding of the scene and surface geom-
etry. Furthermore, mean luminance can be correlated with contrast gloss, while
change in variance across different conditions can be a predictor for translucency.
It is worth mentioning that the dynamic range of our acquisition system was lim-
ited, and analysis of the high dynamic range data could reveal further interesting
trends. Complex shapes and wider range of the materials should also be covered.
While difference in perceptual gloss was assumed between smooth and rough sur-
faces, the statistics should be correlated with actual psychophysical measures in
the future. Finally, more statistical measures, like entropy, and chromatic infor-
mation should also be included in future studies and the performance of simple
image statistics should be compared with that of the complex machine learning
(e.g. deep learning) models. It has been demonstrated recently [26] that unsu-
pervised learning techniques outperform image statistics and even supervised
learning techniques in prediction of human perception. This is an interesting av-
enue that not only provides basis for reliable computer vision systems, but can
also reveal curious mechanisms of the human vision.
References
1. JIDA Standard Samples. (Accessed on 21/11/2019 at), https://www.jida-
online.shop/product/5
2. Anderson, B.L., Kim, J.: Image statistics do not explain the perception of gloss
and lightness. Journal of Vision 9(11:10), 1–17 (2009)
3. Arthur, D., Vassilvitskii, S.: k-means++: The advantages of careful seeding. In:
Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algo-
rithms. pp. 1027–1035. Society for Industrial and Applied Mathematics (2007)
4. van Assen, J.J.R., Wijntjes, M.W., Pont, S.C.: Highlight shapes and perception of
gloss for real and photographed objects. Journal of Vision 16(6:6), 1–14 (2016)
5. Eugène, C.: Measurement of ”total visual appearance”: a CIE challenge of soft
metrology. In: 12th IMEKO TC1 & TC7 Joint Symposium on Man, Science &
Measurement. pp. 61–65 (2008)
6. Fleming, R.W., Bülthoff, H.H.: Low-level image cues in the perception of translu-
cent materials. ACM Transactions on Applied Perception (TAP) 2(3), 346–382
(2005)
7. Gigilashvili, D., Pedersen, M., Hardeberg, J.Y.: Blurring impairs translucency per-
ception. In: Color and Imaging Conference. vol. 2018, pp. 377–382. Society for
Imaging Science and Technology (2018)
8. Gigilashvili, D., Thomas, J.B., Hardeberg, J.Y., Pedersen, M.: Behavioral inves-
tigation of visual appearance assessment. In: Color and Imaging Conference. vol.
No. 1, pp. 294–299. Society for Imaging Science and Technology (2018)
9. Gigilashvili, D., Thomas, J.B., Pedersen, M., Hardeberg, J.Y.: Perceived glossiness:
Beyond surface properties. In: Color and Imaging Conference. pp. 37–42. No. 1,
Society for Imaging Science and Technology (2019)
10. Hajipour, A., Shams Nateri, A., Sadr Momtaz, A.: Estimation of fabric opacity by
scanner. Sensor Review 34(4), 404–409 (2014)
Image Statistics as Glossiness and Translucency Predictor 15
11. Hunter, R.S.: Methods of determining gloss. NBS Research paper RP 958 (1937)
12. Hunter, R.S., Harold, R.W.: The measurement of appearance. John Wiley & Sons
(1987)
13. Kim, J., Anderson, B.L.: Image statistics and the perception of surface gloss and
lightness. Journal of Vision 10(9:3), 1–17 (2010)
14. Kim, J., Marlow, P., Anderson, B.L.: The perception of gloss depends on highlight
congruence with surface shading. Journal of Vision 11(9:4), 1–19 (2011)
15. Landy, M.S.: Visual perception: A gloss on surface properties. Nature 447(7141),
158–159 (2007)
16. Marlow, P.J., Anderson, B.L.: Generative constraints on image cues for perceived
gloss. Journal of vision 13(14:2), 1–23 (2013)
17. Marlow, P.J., Kim, J., Anderson, B.L.: The perception and misperception of spec-
ular surface reflectance. Current Biology 22(20), 1909–1913 (2012)
18. Marschner, S.R., Westin, S.H., Lafortune, E.P., Torrance, K.E., Greenberg, D.P.:
Image-based brdf measurement including human skin. In: Rendering Techniques’
99, pp. 131–144. Springer (1999)
19. Molloy, M.: Confused by this shiny leg optical illusion? Here’s
how it works. The Telegraph (Accessed on 04/01/2019 at
https://wwwtelegraphcouk/news/2016/10/31/confused-by-this-shiny-leg-optical-
illusion-heres-how-it-works)
20. Motoyoshi, I.: Highlight–shading relationship as a cue for the perception of translu-
cent and transparent materials. Journal of vision 10(9:6), 1–11 (2010)
21. Motoyoshi, I., Nishida, S., Sharan, L., Adelson, E.H.: Image statistics and the
perception of surface qualities. Nature 447(7141), 206 (2007)
22. Pellacini, F., Ferwerda, J.A., Greenberg, D.P.: Toward a psychophysically-based
light reflection model for image synthesis. In: Proceedings of the 27th annual
conference on Computer graphics and interactive techniques. pp. 55–64. ACM
Press/Addison-Wesley Publishing Co. (2000)
23. Pointer, M.: A framework for the measurement of visual appearance. CIE Publi-
cation pp. 175–2006 (2006)
24. Qi, L., Chantler, M.J., Siebert, J.P., Dong, J.: Why do rough surfaces appear
glossy? JOSA A 31(5), 935–943 (2014)
25. Sharan, L., Li, Y., Motoyoshi, I., Nishida, S., Adelson, E.H.: Image statistics for
surface reflectance perception. JOSA A 25(4), 846–865 (2008)
26. Storrs, K.R., Fleming, R.W.: Unsupervised learning predicts human perception
and misperception of specular surface reflectance. bioRxiv p. 25 pages (2020)
27. Tanaka, M., Horiuchi, T.: Investigating perceptual qualities of static surface ap-
pearance using real materials and displayed images. Vision research 115, 246–258
(2015)
28. Thomas, J.B., Deniel, A., Hardeberg, J.Y.: The plastique collection: A set of resin
objects for material appearance research. XIV Conferenza del Colore, Florence,
Italy p. 12 pages (2018)
29. Thomas, J.B., Hardeberg, J.Y., Simone, G.: Image contrast measure as a gloss
material descriptor. In: International Workshop on Computational Color Imaging.
pp. 233–245. Springer (2017)
30. Wetlaufer, L., Scott, W.: The measurement of gloss. Industrial & Engineering
Chemistry Analytical Edition 12(11), 647–652 (1940)
31. Wiebel, C.B., Toscani, M., Gegenfurtner, K.R.: Statistical correlates of perceived
gloss in natural images. Vision Research 115, 175–187 (2015)