Journal of Perceptual Imaging R 3(2): 020503-1–020503-10, 2020.
c Society for Imaging Science and Technology 2020

Modeling the Relationship between the Impressions andImage Features of Crinkle Finish of DSLR Camera
Takayuki Gotoh1 , Takuroh Sone1 , Yusuke Tani2 , Kensuke Tobitani2 , Noriko Nagata2
1 Innovation/R&D Division, Ricoh Company Ltd., Kanagawa, Japan; 2 School of Science and Technology/Research Center for

Kansei Value Creation, Kwansei Gakuin University, Hyogo, Japan
E-mail: takayuki.gotoh@jp.ricoh.com

Abstract. In this article, the authors study the relationships between
the principal impressions of crinkle-finished surfaces, which arefound on camera exteriors, and the characteristics of test images ofcrinkle-finished surfaces. They extracted impression words for thesurfaces through subjective experiments with humans. The results
suggest that the impression is affected by five factors: two physicalimpression factors and three emotional impression factors. The
surface images were obtained using a multi-angle measurement
system that was built to collect images under various conditions. The
authors used stepwise multiple regression to derive equations topredict the impressions of the surface given the characteristics of its
test images. The results of the equations are highly correlated withthe subjective scores of the five impression factors. These models
will enable designers to design attractive crinkle-finished surfacesc 2020 Society for Imaging Science andand camera exteriors.
Technology.
[DOI: 10.2352/J.Percept.Imaging.2020.3.2.020503]

1. INTRODUCTION
Appearance is an important factor that affects the motivationof buyers of industrial products for consumers. Surface
finish painting and fabrication methods to alter a product’s
appearance have been developed for many years. In digital
single-lens reflex (DSLR) cameras, the impression of a premium feeling is a key factor. Therefore, manufacturers havedeveloped painting methods to generate this impression.
One painting method used to provide the feeling ofpremium quality on camera exteriors is crinkle finish.
Figure 1 shows an example of a crinkle-finished surface,where dark particles are splashed onto a black base surface.
These surfaces are mainly used for the exterior of DSLR andparts of automobiles; for example, head covers of engines.
Designers determine the qualities of a crinkle finish toincrease affective value. However, these qualities may resultin unintended or even negative affective value because thecrinkle finishes are determined by a designer’s subjective
impressions, which may not correspond to those of thecustomer. To avoid such problems, it is important to clarify
the principal impressions of a crinkle finish and construct amodel to relate those impressions with surface features. This
Received Oct. 4, 2010; accepted for publication July 4, 2020; published online
Sep. 1, 2020. Associate Editor: James A. Ferwerda.
2575-8144/2020/3(2)/020503/10/$00.00

J. Percept. Imaging

would make it possible to design a crinkle-finished surface
for providing the intended impression.
Kansei engineering is a philosophy and method for
product design, which considers what consumers feel fromproducts and not only functions [10, 16, 20]. Regarding
the relationships between impressions and shape design ofproducts, some previous studies have analyzed car front grill
design [7], PET container design of bottled beverages [1],cosmetic box design [12], and glass design [2]. With
respect to the relationships between impressions and surface
features, some previous studies have also analyzed spectral
spatial features of pearl surfaces [14, 17, 19], exterior
colors of cellular phones [9], image features of wood floor
materials [13], and physical properties of woven fabrics [15].
In contrast, there exist no studies that extract and model theprincipal impressions for exterior paint on DSLRs.
The purpose of this study is two-fold: (1) to determine
the factors that influence the visual impressions of a crinkle
finish and (2) to model the relationships between thesefactors and image characteristics. These relationships canhelp designers to provide better impressions to users of
DSLRs.
Hence, we conducted experiments to evaluate theimpression of crinkle-finished samples and measured thefeatures of images of the surfaces.
Previously, we had reported the results of impression
evaluation and modeling the relationships between a selected
list of the impressions and image characteristics [3]. In thisarticle, we report the results of impression evaluation andmodeling the relationships between all extracted impressions
through these experiments and image characteristics ofsurfaces.
2. EXPERIMENT 1
Impression evaluation experiments are psychological tests
that clarify and quantify principal impressions of samples [18]. There is no standard dataset of words used
to evaluate product impression for consumer electronic
devices. Therefore, we had to create our own dataset. To
determine the proper evaluation words, we conducted threeexperiments before the impression evaluation experiment: an
impression-word extraction test, an impression-word appro-

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Figure 1. Exterior of a digital single-lens reflex camera and magnified image of a crinkle-finished surface.

priateness test, and a rating word distance measurement test.
Through an impression-word extraction test, we gathered
impressions of crinkle-finished surfaces widely. However,these widely gathered words contain uncommon or personal
impressions. Therefore, we extracted users’ common expressions by an impression-word appropriateness test. Then
we conducted a rating word distance test to group similar
impressions.
2.1 Methods
2.1.1 Impression-Word Extraction TestFirst, we extracted general impressions about crinkle finishes.
Fourteen subjects observed three crinkle-finished samples
and two general DSLRs and then wrote down impressions
they felt with single words or short phrases as many asthey could. They were encouraged to clamp samples andview them at various angles. They could write down only
visual impressions, not haptic impressions. The subjects wereencouraged to observe a small flat area (about 15 mm ×
20 mm) to avoid the effects of sample and camera shapes.
The subjects were image science engineers in our company
and consisted of 13 men and 1 woman in their 20s to 50s.
They were selected randomly without any consideration of
their knowledge of DSLR. They were told that samples wereparts of DSLR exteriors.
This test was conducted using a standard light source(Spectra Light QC; X-Rite, MA, USA). Standard illuminant
D65 was used, and the illuminance of the observation
area was set at 1,300 lx. These conditions were also used
for the appropriateness tests and impression evaluation
experiments.
After all participants wrote down impressions, we took
all words that had appeared at least once. Then, 114 words
were extracted from this experiment. Additionally, we added
25 words used by paint designers. A total of 139 words
were extracted to describe the general impressions of crinkle
finish.
2.1.2 Impression-Word Appropriateness Test
The words gathered by the impression-word extraction test
contain complex and uncommon words. These words are
not appropriate for use in evaluation experiments. Therefore,
J. Percept. Imaging

in this test, we evaluated appropriateness of these words forcrinkle paint impression evaluation.
In this test, 21 participants evaluated the appropriateness of 139 words using seven scores from 1 (‘‘very
inappropriate’’) to 7 (‘‘very appropriate’’). Subjects observed
three crinkle-finished samples and two general DSLRs andthen evaluated how appropriate each word was for the
impression of samples. In this test, subjects could also
clamp samples and observe them at various angles. Subjects
were encouraged to observe a small flat area to avoid theeffects of sample and camera shapes. Ten participants were
instructed to evaluate how appropriate each word was forthe impression of physical condition and 11 participants
were instructed to evaluate how appropriate each word
was for the impression of what they feel from samples.
Because the number of evaluation words must be smaller
than the number of samples to perform factor analyses, thenumber of evaluated impressions is limited. Therefore, we
analyzed physical impressions and emotional impressions
separately to evaluate more impressions [5, 6, 11, 21].
We call the former ‘‘low-level impressions’’ and the latter‘‘high-level impressions.’’ We calculated the average andstandard deviation of each sample score. Words with anaverage score of more than five and a standard deviation ofless than 1.5 were selected as appropriate words.
We extracted 36 words appropriate for low-level impressions and 32 words appropriate for high-level impressions.
Eighteen words were appropriate for both levels of impression.
As mentioned in Section 1, the crinkle finish is intended
to provide a premium feeling. However, no words related
to ‘feels premium’ were extracted by the impression-word
appropriateness test. Hence, we added ‘‘feels premium’’ tothe high-level impression appropriate words to investigate
the relationship between the premium feeling and otherimpressions.
2.1.3 Rating Word Distance Measurement Test
We conducted a rating word distance measurement test tomeasure the distances of the meanings between impression
words.

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Table II. Crinkle-finish sample types.

Table I. Evaluation words.
Low-level impressions
smoothfinenonuniform
cloudymatte

High-level impressions

granularroughglittering
lumpy

slick
statelyclear
lumpy
sparkling

Coating material TYPE-A

finenonuniform
bumpy
simplefeels premiumParticipants were handed the list of all pairs of words
extracted in the previous experiment and judged whether
each pair of words could be exchanged semantically. This
experiment was conducted without the observation of samples. Ten participants evaluated pairs of low-level impression
words. They had also evaluated appropriateness of low-level
impression at the impression-word appropriateness test.
Twelve participants evaluated pairs of high-level impression
words. Among 12 participants, 11 participants had also
evaluated appropriateness of high-level impression at the
impression-word appropriateness test and 1 was a newparticipant. The new participant was shown crinkle-finishedsamples before this experiment.
Then, the word distances between each pair were
calculated by the following equation:
D=

Nn
.
Nall

(1)

Here, D is the word distance between a pair of words, Nallis the number of all participants, and Nn is the number ofparticipants who judged that the pair of words could not
be exchanged semantically. When all participants answer
that a pair of words cannot be exchanged, D = 1. When allparticipants answer that a pair of words can be exchanged,
D = 0. Therefore, D corresponds to normalized similarity
between a pair of appropriate words. A pair with a shortdistance indicates a similar impression and a pair with a long
distance indicates different impressions.
We plotted the words in multi-dimensional space
using multi-dimensional scaling. The maximum numberof dimensions of the space is one less than the number
of appropriate words. To analyze the similarities between
appropriate words correctly, low- and high-level impression
words were plotted in 35- and 31-dimensional spaces,
respectively.
To group impression words according to the similarityof impressions, hierarchical cluster analysis was conductedusing Ward’s method. The high- and low-level impression
words were divided into nine clusters, each corresponding to
one type of impression. The nine impression words that were
the nearest to the center of mass of each cluster were selected
as the final evaluation words (Table I).
2.1.4 Impression Evaluation Experiment
Figure 2 shows an example of the crinkle paint samples
used in the impression evaluation experiments. The samples
J. Percept. Imaging

Large
Size of particles

Large
A1
MediumA4
Small
A5
Coating material TYPE-B
Large

Size of particles

Large
Medium
Small

B1
B4
B5

Quantity of particles
Medium

Small

A2

A3

Quantity of particles
Medium

Small

B2

B3

are parts of camera exteriors that were finished using
crinkle paints. Table II shows the quantity and size of thepaint particles used for the 10 crinkle-finished samples.
Three levels of each factor were tested. The sizes of large
particles are approximately 0.08 mm2 , medium particles are
0.06 mm2 , and small particles are 0.05 mm2 . Furthermore,
we used two particle coating materials (TYPE-A and TYPEB). The base coating material is the same for all samples.
Figure 3 shows some examples of pictures of sample surfaces.
Participants were presented each sample separately andevaluated how strong they felt each impression, whichis extracted by a rating word distance measurement testfrom crinkle-finished samples on a five-level scale from‘‘not felt at all’’ to ‘‘strongly felt.’’ They were encouraged
to clamp samples and view them at various angles. They
were encouraged to evaluate only visual impressions, nothaptic impressions. The samples were shown in random
order. Eleven participants evaluated low-level impressions.
Among 11 participants, 10 participants had also evaluatedlow-level impressions in previous experiments and 1 wasa new participant. Ten participants evaluated high-level
impressions. They had also evaluated high-level impressionsin previous experiments.
2.2 Results
To extract the principal impressions, the average subjectivescores were calculated and factor analyses of the low- andhigh-level impressions were performed. The maximumlikelihood method was used to extract low-level impressions.
The least-squares method was used to extract high-level
impressions because factor analysis did not converge using
the maximum-likelihood method. Promax rotation was used
for both analyses. Table III shows the factor loadings of theevaluation words for low-level impressions. The cumulative
contribution ratio up to the second factor is 76.07%.
The results reveal two factors for low-level impressions.
The first consists of the words ‘‘lumpy,’’ ‘‘rough,’’ ‘‘granular,’’‘‘glittering,’’ and ‘‘smooth.’’ The second consists of the words‘‘matte,’’ ‘‘cloudy,’’ and ‘‘fine.’’020503-3

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Figure 2. Example of crinkle paint (left) and magnified image of the crinkle-finished surface (right).

Table III. Results of factor analysis for low-level impressions.

We define the first factor as ‘‘macroroughness’’ because
it corresponds to a large-scale impression regarding the

Figure 3. Examples of pictures of sample surfaces: (a) samples painted
with TYPE-A; (b) samples painted with TYPE-B.

J. Percept. Imaging

particles of the crinkle finishes. The second factor isdefined as ‘‘microroughness’’ because it corresponds to asmaller-scale impression.
Subjective scores of low-level impressions were calculated by factor analysis. Figure 4 shows the subjective scoresof low-level impressions for each sample. This result indicatesthat macroroughness becomes higher as the quantity ofparticles becomes larger. Microroughness becomes higher as
the size of the particles becomes smaller. The type of paintdoes not relate to these two impressions.
Table IV shows the factor loadings of each evaluation
word for high-level impressions. The factor analysis was
performed using nine evaluation words (except ‘‘slick’’)
because the number of evaluation words must be smaller
than the number of samples. The cumulative contribution
ratio up to the third factor is 81.40%.
There are three factors for high-level impressions.
The first consists of the words ‘‘sparkling,’’ ‘‘nonuniform,’’‘‘bumpy,’’ ‘‘lumpy,’’ ‘‘clear,’’ ‘‘feels premium,’’ and ‘‘simple.’’We define this factor as ‘‘randomness’’ because the first factor
is positively correlated with ‘‘nonuniform’’ and ‘‘sparkling’’and negatively correlated with ‘‘simple.’’ The second andthird factors consist of the words ‘‘stately’’ and ‘‘fine,’’respectively, and hence we call these factors ‘‘stateliness’’ and‘‘fineness.’’
Subjective scores of high-level impressions were calculated by factor analysis, and Figure 5 shows that for

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Figure 4. Subjective scores of low-level impressions for each sample. The
blue marker indicates the samples with coating material TYPE-A and thered marker indicates TYPE-B.

Table IV. Results of factor analysis for high-level impressions.

each sample. This result indicates that randomness increases
with increasing size of particles. As regards high-level
impressions, there is no difference among the types of paints.
The quantity of particles does not show a simple relation tohigh-level impressions.
3. EXPERIMENT 2
We modeled the relationships among the five impression
factors and the characteristics of crinkle-finished surface
images. We measured the image characteristics using theprocesses described below and modeled the relationshipusing multiple regression analyses.
3.1 Methods
Figure 6 shows a schematic diagram of the multi-angle
measurement system that we constructed to collect imagesof crinkle-finished samples. The system can measure thesamples at various lighting and camera angles. The lighting
device is a Xe light source (MAX-303, Asahi Spectra Co.,
Ltd., Tokyo, Japan). The light beam is parallelized by a rod
J. Percept. Imaging

Figure 5. Subjective scores of high-level impressions for each sample.
The blue marker indicates the samples with coating material TYPE-A andthe red marker indicates TYPE-B.

lens and a telecentric lens. The camera device is an XYZ
camera (RTC-21, Ikegami Tsuhinki Co., Ltd., Tokyo, Japan).
The XYZ camera measures XYZ tristimulus values as a pixel
value [8]. In this experiment, the resolution of the test images
is 40 pixels/mm. The lighting and camera angles are defined
as angles from the line that is normal to the measuring
surface.
We measured the paint surfaces of the crinkle-finishedsamples under eight conditions (Figure 7). Table V shows
the size of the region of interest (ROI) under each condition.
In this report, the measurement conditions are written as‘‘lighting angle [deg]/camera angle [deg].’’Figure 8 shows example test images. Figs. 8(a), (b), and(c) are images of sample A1 under three different conditions(45◦ /0◦ , 30◦ /45◦ , and 0◦ /60◦ , respectively). These three
images were measured at the same position. Here, (c) is partof a region of (b) and (b) is part of a region of (a). The sizes of020503-5

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Table V. ROI size for each condition.
Camera angle(deg)Size of ROI(pixel)Size of ROI
(mm)

0
45
60

600 × 600
424 × 424
300 × 300

15.0 × 15.0
10.4 × 10.4
7.5 × 7.5

(2) Standard deviation of L∗
This characteristic is the standard deviation of L∗ in the
ROI.
(3) Average L∗ of the base and particle regions
The L∗ -channel images in the ROI were divided intobase and particle regions using a threshold set for the averagevalue of L∗ in the samples. The average values of L∗ in eachregion are defined as the average L∗ values of the base andparticle areas, respectively.

Figure 6. Multi-angle measurement system.

(4) Contrast of L∗
This value is the absolute value of the difference between
the average L∗ values of the base and particles.

Figure 7. Conditions of the test images.

ROIs were changed due to changing camera angles (shown in
Table V). The brightness of particles changes in Fig. 8(a) and(b) as the red circled areas show. In Figs. 8(a) and (c), there
are bright points that are smaller than the particles (circled
in green).
We calculated the image characteristics using the following methods. First, we measured the Y-channel images ofthe samples Y (i, j) and a standard white target Ys (i, j) using
the XYZ camera under each condition, where (i, j) denotespatial coordinates. Then, we calculated the brightness L∗ ofeach sample to consider human visual characteristics. The L∗
images of samples L∗ (i, j) were calculated as defined by theInternational Commission on Illumination as follows:
Y (i, j)L∗ (i, j) = 116f
− 16.
(2)
Ys (i, j)Here, f (t) is defined as in the following equations:(1t3
t < (6/29)3f (t)[(29/3)3 t + 16]/116 t ≥ (6/29)3 .

(3)Finally, we calculated the following nine image characteristics:(1) Average L∗
This characteristic is the average value of L∗ in the ROI.
J. Percept. Imaging(5) Low and high spatial frequency characteristics
Figure 9 shows how the spatial frequency characteristics
are calculated. As shown in Fig. 9(a), we calculated spatial
frequency using the Fourier transformation of the test images
and transformed the results into one-dimensional values by
averaging the spatial frequency concentrically. As shownin Fig. 9(b), the integral of the one-dimensional spatial
frequency values over a frequency range (0.5–2.0 and 4.0–10
cycles/mm for low and high spatial frequencies, respectively)
is used as a spatial frequency characteristic [4]. The low
spatial frequency characteristic corresponds to the size of thelarger particles and the high spatial frequency characteristic
corresponds to that of the smaller particles.
(6) Number of particles
We binarized the L∗ -channel images using a thresholdto divide them into particle and base regions. Then, we
calculated the number of particles in the image.
(7) Average particle area
The average particle area was calculated from eachconnected component of the binarized images.
(8) L∗ change at 45◦ and 60◦
The difference of the average L∗ under highlighted(specular reflection) conditions and shaded (diffuse reflection) conditions is defined as the L∗ change. The L∗ change at
45◦ is the difference between 45◦ /45◦ (specular) and 0◦ /45◦
(diffuse) images. The L∗ change at 60◦ is the difference
between 60◦ /60◦ (specular) and 0◦ /60◦ (diffuse) images.
(9) Particle L∗ change
The difference in L∗ values for the particle regions
between near-specular and diffuse conditions is defined as
the particle L∗ change. The difference between 30◦ /45◦ and0◦ /45◦ was calculated.

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Figure 8. The results of measurement images.

Image characteristics 1–9 were calculated for each of theeight test conditions (see Fig. 4). Therefore, a total of 75 image
characteristics were calculated for each sample.
We derived formulas to predict the five impression
factors using multiple regression analyses with a stepwise
method. In these analyses, the objective variables are
the factor scores of the five factors and the explanatory
variables are the image characteristics. The values of theimage characteristics have dissimilar ranges. Therefore, thestandardized Z scores of the image characteristics were usedas the explanatory variables.
3.2 Results
3.2.1 Macroroughness
Macroroughness is estimated as follows:
Macroroughness = 0.978 × x11 .

3.2.3 Randomness
The following equation estimates randomness:
Randomness = 0.976 × x31 .

(6)Here, x31 is the 45◦ /30◦ average particle area. Figure 12
shows the relationship between the values predicted from
(Eq. (6)) and the subjective scores.
Equation (6) indicates that randomness is positively
correlated with particle areas. The R-squared value is 0.961.
By k-fold cross-validation (k = 10), a mean R-squared valueof 0.937 was obtained.

(4)Here, x11 is the 45◦ /45◦ high spatial frequency characteristic. Figure 10 shows the relationship between thevalues predicted from Eq. (4) and the subjective scoresfor macroroughness obtained by the impression evaluation
experiments.
Equation (4) indicates that macroroughness is highly
positively correlated with the 45◦ /45◦ high spatial frequency
characteristic. The R-squared value of Eq. (4) is 0.95. By
k-fold cross-validation (k = 11), a mean R-squared value of
0.924 was obtained.
3.2.2 Microroughness
Microroughness is estimated as follows:
Microroughness = 0.529 × x21 − 0.51 × x22 − 1.03 × x23 .
(5)Here, x21 is the 45◦ /0◦ standard deviation of L∗ , x22 is
the 60◦ /45◦ base value of L∗ , and x23 is the 0◦ /45◦ average
particle area. Figure 11 shows the relationship between thevalues predicted from Eq. (5) and the subjective scores.
Equation (5) indicates that microroughness is positively
correlated with the standard deviation of L∗ and negatively
correlated with the base value of L∗ values and the average
J. Percept. Imaging

particle area. The R-squared value is 0.863. By k-foldcross-validation (k = 11), a mean R-squared value of 0.664
was obtained.

3.2.4 StatelinessStateliness can be estimated as follows:
Stateliness = 0.701 × x41 + 0.771 × x42 − 0.811 × x43 .
(7)Here, x41 is the 45◦ /0◦ low spatial frequency characteristic,
x42 is the 30◦ /60◦ standard deviation of L∗ , and x43 is
the 60◦ /60◦ standard deviation of L∗ . Figure 13 shows therelationship between the values predicted from Eq. (7) andthe subjective scores.
Equation (7) indicates that stateliness is positively
correlated with low spatial frequency characteristics andthe 30◦ /60◦ standard deviation of L∗ . Furthermore, it isnegatively correlated with the 60◦ /60◦ standard deviation ofL∗ . The R-squared value is 0.973. By k-fold cross-validation(k = 10), a mean R-squared value of 0.921 was obtained.
3.2.5 Fineness
Fineness is estimated as follows:
Fineness = 1.817 × x51 − 1.582 × x52 − 1.676 × x53 . (8)Here, x51 is the 0◦ /45◦ contrast of L∗ , x52 is the 0◦ /45◦
standard deviation of L∗ , and x53 is the 0◦ /60◦ contrast

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Figure 10. Predicted values and subjective scores for macroroughness.

Figure 11. Predicted values and subjective scores for microroughness.

Figure 9. (a) Procedure for calculating frequency characteristics. (b)Ranges of low- and high-frequency characteristics.

of L∗ . Figure 14 shows the relationship between the values
predicted from Eq. (8) and the subjective scores.
Equation (8) indicates that fineness is positively correlated with the 0◦ /45◦ contrast of L∗ . Furthermore, it isnegatively correlated with the 0◦ /45◦ standard deviation ofL∗ and the 0◦ /60◦ contrast of L∗ . The R-squared value is0.957. By k-fold cross-validation (k = 10), a mean R-squared
value of 0.937 was obtained.
J. Percept. Imaging

Figure 12. Predicted values and subjective scores for randomness.

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microroughness strongly when there are large luminancechanges, a dark base, and small particles.
Randomness is positively correlated with the particle
area. This result indicates that crinkle-finished surfaces withlarge particles invoke a strong feeling of randomness.
Stateliness is positively correlated with the low spatial
frequency characteristic and the variation of L∗ under diffuse
conditions and negatively correlated with L∗ variation under
specular conditions. This indicates that large particles invoke
a strong feeling of stateliness in users. Furthermore, users
feel stateliness strongly when they see many bright points
under diffuse conditions and view uniform brightness under
specular conditions.
Fineness is positively correlated with the 0◦ /45◦ L∗
contrast and negatively correlated with the 0◦ /45◦ variationand the 0◦ /60◦ contrast of L∗ . We believe that the 0◦ /45◦
contrast corresponds to the impression of the particles.
However, the 0◦ /45◦ variation and the 0◦ /60◦ contrast of L∗
correspond to the glitter of tiny light spots. Therefore, theresults of this study show that users feel fineness strongly
when the impression of particles at 0◦ /45◦ is strong and theglitter of particles at 0◦ /45◦ and 0◦ /60◦ is low.

Figure 13. Predicted values and subjective scores for stateliness.

Figure 14. Predicted values and subjective scores for fineness.

4. DISCUSSION
A crinkle finish is usually used on digital cameras to provide
a premium feeling. However, the results of the impressionword appropriateness test did not include ‘‘feels premium’’as an appropriate term for crinkle-finished surfaces. Hence,subjects are not given much of a premium-feeling impression
from a limited observation area of a planar part. This
indicates that the premium feeling of a digital camera isaffected by the overall shape of the camera.
Macroroughness is positively correlated with the highspatial frequency characteristic of the finish under specularconditions. This indicates that users perceive the luminancechanges at the spatial frequency corresponding to the size ofparticles as macroroughness.
Microroughness is positively correlated with the variation of L∗ and negatively correlated with the value of L∗
in the base and particle areas. This indicates that users feel
J. Percept. Imaging

5. CONCLUSION
We conducted an impression-word extraction test,
impression-word appropriateness test, and rating word
distance measurement test to extract evaluation words that
represent the impressions of crinkle-finished surfaces.
The results of the impression evaluation tests indicatethat low-level impressions consist of macroroughness andmicroroughness. High-level impressions consist of randomness, stateliness, and fineness. Furthermore, the words ‘‘feels
premium’’ were not chosen as an evaluation term in theimpression-word appropriateness tests.
Equations to predict five impression factors from test
image characteristics were derived using multiple regression
analyses. These prediction models indicate that impression
factors are affected by not only simple particle parameterslike quantity and size but also appearances at various lighting
and observation angles. These proposed models enableattractive crinkle-finished surfaces to be designed. They
further prevent designers from designing crinkle finisheswith unintended or negative impressions. These models canimprove the affective value of DSLR cameras.
In future, we will model the relationship between
low-level and high-level impressions. Furthermore, we will
analyze the impressions of the overall appearance of a DSLR
camera, including paints and shapes.
ACKNOWLEDGMENT
This research is supported by the Center of Innovation
Program from Japan Science and Technology Agency (JST).
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