Comparison of Visual Impression Given by Texture ofReal Surfaces and Synthesized Images
Atsushi Takemoto*

Taishi Fujiwara†

Kensuke Tobitani

Yusuke Tani

Noriko Nagata‡

Kwansei Gakuin UniversityA BSTRACT
We present a method to compare visual impressions given by thetextures of real surfaces and 3D synthesized images of synthetic
resins to improve the reality of CG images. We analyzed the structures of the visual impressions by conducting subjective evaluation
experiments using real textures and generated images of the textures
based on measurements of surface characteristics. We clarified therelationship between the visual impressions of the textures and thesurface characteristics and showed the possibility of feedback toimprove the reality of CG images.
Index Terms:
Computing methodologies—Artificial
intelligence—Computer vision representations—Appearance
and texture representations
1 I NTRODUCTION
In the field of computer graphics, many attempts to represent objects more realistically have been made [1, 2, 5]. However, so far,hardly any general method of making an objective and quantitativeevaluation of the reality of CG images exists. In previous studies,
visual roughness perceptions on dot pattern textures and their CG
images were compared [3], and an impression evaluation of “pearllike” quality was performed using photographs and synthesized CG
images of pearls [4]. However, these studies were restrictive interms of dependence on objects. In this study, we focus on emboss
processing on surfaces of synthetic resins, and aim to investigate
visual impressions given by real surfaces and generated texture images of synthetic resins. In addition, we propose a method to clarify
physical differences by comparing them. Finally, we apply the results to improve the reality of CG images.
2 S UBJECTIVE E VALUATION E XPERIMENTS IN T EXTURES
We conducted subjective evaluation experiments to investigate thevisual impressions given by real surfaces and synthesized texture
images.
2.1 Collecting evaluation words
Prior to experiments, we collected words for evaluation of visualimpression. First, we conducted a free writing experiment. Participants in this experiment observed the real surfaces of synthetic
resins and freely wrote out the visual impressions given by them.
Then we verified whether the obtained words were suitable for visual impression. As a result, we collected 59 adjectives with highfitness. After that, we verified whether these adjectives could replace each other and calculated the similarity of each word basedon the semantic replaceability. Then, using the similarity of words,
we performed multi-dimensional scaling and hierarchical clustering using Ward’s method. Finally, we collected 19 adjectives that
* e-mail: a.takemoto@kwansei.ac.jp† e-mail: t.fujiwara@kwansei.ac.jp‡ e-mail: nagata@kwansei.ac.jp

were comprehensive and representative to evaluate visual impression, and we used them for evaluation experiments. These adjectives are actually Japanese words.
2.2

Evaluation of real textures

2.2.1

MethodWe used 20 samples of synthetic resins as stimuli. Each was madeusing various emboss processing, for example, a wrinkle or dot pattern (Fig. 1). The participants in this experiment were 19 Japanesecollege students in their 20s (17 men, 2 women). They evaluateda 19-adjective scale of approximately 20 stimuli in 5 stages. This
experiment was conducted in a darkroom where the lighting environment was controlled.

(a) Sample 5.

(b) Sample 13.

(c) Sample 18.

Figure 1: Examples of synthetic resin samples.

2.2.2

Results and discussion

We performed factor analysis to rate the data using the maximum
likelihood method and the Promax rotation. Table 1a shows theresults of factor analysis. We extracted 3 factors. We interpreted
each factor as follows: Factor 1 is “freshness” because “dry” and“aged” have high negative factor loading; Factor 2 is “roughness”
because “slick” and “smooth” have high negative factor loading;and Factor 3 is “preference” because “uncool” and “unlikable” havenegative factor loading. We regarded the adjectives that did nothave factor loading of 0.5 or more for any factor do not belong to
any factor.
2.3
2.3.1

Evaluation of synthesized texture images
MethodWe used 20 synthesized texture images as stimuli (Fig. 2). They
were each generated based on measurements of BRDF (bidirectional reflectance distribution function) and height maps of 20 samples of synthetic resins. To generate images, we used Autodesk
MAYA, and for the lighting conditions at the time of rendering, weset the same environment as the lighting environment of the previous experiment described in section 2.2. The participants in thisexperiment were 10 Japanese college students in their 20s (9 men,1 woman). They evaluated a 19-adjective scale of approximately
20 stimuli in 5 stages. Then, we used the same adjectives in theexperiment described in section 2.2 for evaluation. This evaluation
experiment was conducted in a darkroom. For presentation of thestimuli, the display monitor was used at a resolution of 1920×1200．

(a) Sample 5.

(b) Sample 13.

(c) Sample 18.

Figure 2: Examples of synthesized texture images.
2.3.2 Results and discussion
We performed factor analysis to rate the data using the maximum
likelihood method and the Promax rotation. Table 1b shows the results of the factor analysis. We extracted 4 factors. We interpreted
each factor as follows: Factor 1 is “innovativeness” because “futuristic” and “progressive” have high positive factor loading; Factor
2 is “smoothness” because “smooth” and “slick” have high positive factor loading; Factor 3 is “profoundness” because “firm” and“heavy” have high positive factor loading; and Factor 4 is “unevenness” because “warty” and “uneven” have high positive factor loading.

3.3 Discussion
Compared with the results of the factor analysis for the real textures and the synthesized texture images, Factor 2 in the real textures separated into Factor 2 and Factor 4 in the synthesized texture
images. The results of two-way ANOVA indicated significantly different interactions in “jagged,” “slick,” “smooth,” and “warty.” We
infer that the result is due to the difference in visual impressions in
the real textures and the synthesized images. Picking out “jagged,”“slick,” “smooth,” and “warty” in Factor 2, we recolored based on
the results of the multiple comparison (Table 3). In this process,“jagged” and “warty” were replaced with “not jagged” and “notwarty.” The stimuli colored in red show increasing the visual impression of “smoothness” in the synthesized images. On the otherhand, the stimuli colored in blue show decreasing it in the synthesized images. For these reasons, we interpreted that the visual impression of smoothness in synthesized images increases in coarseand deep textures, such as in sample 5; however, it decreases in fineand shallow textures, such as in sample 13. These results suggest
the possibility of improving the reality of CG images considering
physical characteristics.
Table 2: Results of multiple comparison.

Table 1: Results of factor analysis.
(a) Real textures.

(b) Synthesized texture images.

Table 3: Results of recoloring.

4 C ONCLUSION
We investigated the difference in visual impressions regarding thetexture of synthetic resins of real surfaces and 3D synthesized images. Based on the results, we clarified the relationship between thevisual impressions of the textures and their surface characteristics.
This suggests the possibility of feedback to improve the reality ofCG images.
3C OMPARISON OF V ISUAL I MPRESSIONS

3.1 Two-Way ANOVA
We performed a two-way ANOVA to investigate the difference ofthe visual impressions, with factor condition (real and synthesizedimages) and stimuli (texture). The analysis was performed with thedata of 7 participants who took part in both evaluation experiments.
From the results of the analysis, there were no significant differences in the conditions. However, for the specific adjectives “beautiful,” “dry,” “jagged,” “rough,” “slick,” “smooth,” “unlikable,” and“warty,” there were significant differences in interaction between
condition and stimuli at the 5 percent level.
3.2 Multiple comparison
To clarify in which stimuli significant differences occurred for specific adjectives, we performed a multiple comparison using theBonferroni method. Table 2 shows the results of the multiple comparison. In the table, the stimuli for which there were significant
differences are colored. The stimuli colored in red show that theevaluation score of the real texture was higher than that of the synthesized image. The stimuli colored in blue show that the evaluation
score of the synthesized texture image was higher than that of thereal texture.

ACKNOWLEDGMENTS
This work was supported in part by the Center of Innovation Program from Japan Science and Technology Agency, JST.
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