The Journal of the Society for Art and Science, Vol. 22, No. 4, pp. 15:1-15:7 (2023)

Algorithmic Analysis of Color Combinations Principle in Game
Concept Art
Tiancheng San (Student Member) Yoshihisa Kanematsu (Member)
Koji Mikami (Member)
1) School of Media Science, Tokyo University of Technology{d312000256, kanematsuy, mikami} at stf.teu.ac.jp
Abstract

Numerous investigations have been conducted on the topic of color combinations, with a focus on generating color
palettes to depict the patterns inherent in such combinations. These color palettes have proven to be immensely
valuable in the realm of visual design. Our research also concerns color combinations; however, our approach involves
the utilization of algorithms to demarcate visual center and non-visual center regions within game concept art. By
adopting an artist's perspective, this research delves into the comprehension of color combinations. In light of ourprior investigations, we summarize the colors associated with the visual and non-visual centers, using color
segmentation, and subsequently compute the respective color proportions in these two areas for conclusive purposes.
Furthermore, we ascertain the hue, saturation, and lightness characteristics of each color, thereby discerning theunderlying laws of color combinations within the game concept art. The objective of this research paper is to provide
digital artists with the means to produce superior and expeditious artworks that align with contemporary aesthetics.

1. Introduction
The exploration of color combinations has long held
significance within the domains of painting and visual design.
Colors possess three-dimensional attributes encompassing hue,saturation, and brightness, causing the combination of colors acomplex undertaking. Moreover, the aesthetics governing color
combinations have evolved. Notably, several analogous
investigations on color combinations have been conducted invarious areas. For instance, Jahanian [1] conducted researchanalyzing color combinations and proportions in the context oflogo design. Differing from this research, our study primarily
concentrates on game concept design. Scholars have endeavored
to comprehend color through varied perspectives, seeking novel
methodologies and patterns for color combinations. For example,
Yamazaki's investigation [2] investigated the interplay between
Kansei and color, while Hu's study [3] combined familial factors
and rhythm span to establish a novel approach to color selection.
Distinct from visual arts such as pictures and photographs, game
concept art holds utmost significance within the gaming industry[4]. In game concept art, colors serve multiple purposes such as
defining a unique style, drawing viewers' attention, and making
a contrast. Many ideas and results garnered from prior researchon color combinations can be applied to game concept design
drawings. However, the treatment of color in game concept
design and painting differs considerably between the visual
center and the non-visual center, an aspect often overlooked inprevious studies. In our prior investigation, we employed amodule to distinguish between visual center areas and non-visual
center areas in 2D game concept art. Approaching the subject
from an artist's perspective, this study will meticulously analyze
color transformations and combination patterns within the
distinct areas of the visual center and non-visual center.
Consequently, the application of color combinations in the fieldof game design will be further nuanced. We anticipate that thefindings will enhance the dexterity of game artists in employing
color during the drawing process.
Our research target are game concept art that follow thecontemporary aesthetics. Contemporary aesthetics refers to thebranch of philosophy and art criticism that explores and analyzes

the nature, principles, and theories of beauty and artistic
expression in the context of the present time or contemporary
culture. Aesthetics, in general, is concerned with understanding
what makes something beautiful or aesthetically pleasing andhow we perceive and appreciate art and beauty. It takes into
account the influence of the current cultural, social, political, andtechnological factors on our understanding of aesthetics. Given
the rapid advancements in technology, contemporary aesthetics
also explores how digital media, virtual reality, and othertechnological innovations have reshaped the ways in which art iscreated, experienced, and understood.
Our study differs from previous studies of color combinations inthree main points. The first point is that our study's focus differsfrom that of photographs and visual designs. Game concept
artwork diverges from photographs and graphic design in its
utilization of colors, which are employed in bolder and more
complex ways. This is especially evident in game scene designs,
where game concept art incorporates color contrasts and subtle
color changes.
The second point is that our research concentrates on the visual
center. In previous research, color information usually from theentire image was analyzed and summarized. However, for game
concept designers, understanding the visual center is a crucial
design method. The methods and rules of using colors in thevisual center and in the non-visual center area will be very
different. This is a unique feature not found in other similar
studies, such as those focused on photographs or visual designs.
The third point emphasizes the importance of color proportion,
which is often a neglected aspect in similar color combination
studies. The percentage of color can significantly alter the overallvisual impact of an image and can also affect the intensity ofcolor contrasts.

2. Related work
2.1 Color combination paletteColor combinations belong to similar research across various
fields, each with its own particular focus of research. However,the underlying objective remains constant, namely, the quest to
uncover patterns in color combinations that can enhance theefficacy of artists or AI systems in the area of design and painting.

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Several research pertaining to color combinations and visual
centers serve as illustrative examples.
Firstly, within the domain of visual design, a study hasconcentrated on investigating the relationship between color
combinations on magazine covers and the thematic content of themagazines, as shown in Figure 1[1]. This investigation delves
into the consistency between color and content. To achieve this,the study used the LDA-duel approach to categorize magazine
keywords into 12 distinct themes. Subsequently, the colors usedon the covers were separated into 10 color palettes.

Impressionists in the realm of traditional paintings, game concept
artists use diverse strategies aimed at guiding the viewer's
attention. This involves deliberately directing the viewer's
attention towards a specific area of the artwork, thus delineating
the visual center in the game concept art.

Figure 1. Matching of Color Combinations and Text Content [1]Figure 2. An Example of Visual Interest Research [6]This research similar to our own investigation; however, it
focuses on a different domain. Our study places greater emphasis
on elucidating the disparities between visual and non-visual
centers, as well as highlighting the significance of color
proportions.
Similar investigations have been conducted in prior research,such as Decarlo's study [5], wherein strokes and area
segmentation were performed based on photo colors,
subsequently exploring the impact of objects on saliency.
In recent investigations, novel methodologies have been
discovered to explore the field of color palette analysis. To
illustrate, Chang's seminal study [6] introduced an interactive
tool was designed to allow non-professionals to fill the color
photographs using a color palette generator. Furthermore,
Kumar's research [7] endeavored to elucidate color palette
extraction through the application of the k-means clustering
algorithm, thereby presenting an innovative approach for thecolor palettes.
However, our study differs from their research. Firstly, ourfocus centers on game concept artwork, which possesses acomprehensive and more exaggerated color and shape. Secondly,
our investigation places particular emphasis on the analysis ofcolor combinations, with a specific focus on discerning the
disparities between the visual and non-visual centers.

2.2 Visual Interest
Prior investigations using eye-tracking methodologies havealso explored the concept of visual centers. For instance, as
shown in Figure 2, which represents one of the earlier tests ofvisual centers. In this particular study, participants were invited
to view multiple traditional paintings, while their eye movements
were recorded using an eye-tracking system [8]. Subsequently,
participants were finished a questionnaire aimed at elucidating
the factors that influenced their observations.
The present study was conducted by a questionnaire-based
approach to investigate the factors associated with the visual
center, although it should be noted that the majority ofparticipants were not professional artists. Consequently, theinfluence of color, a significant determinant of the visual center,
is often ignored by the general audience.
The visual center constitutes a subjectively determined area,
influenced by multiple variables, including the content,
coloration, and compositional attributes within an image.
Analogous to the practices employed by traditional artwork and2.3 Fine Art Instruction
The next example applies the findings to the realm of fine art
instruction [9]. This investigation utilizes digital technology toanalyze patterns in color combinations, subsequently integratingthese patterns into painting education. Through the utilization ofdigital tools, students acquire an understanding of color
interactions and learn to manipulate the relationships between
colors. For art students in training, it is crucial to understand thetheoretical underpinnings of color. The authors outline theirexperiences teaching color concepts using various digital tools
and elucidate how the results are effectively applied to traditional
painting instruction. It is evident that historically, color
approaches relied heavily on the subjective experiences andpractices of artists. However, the advent of technology has
facilitated digital tools that enhance our comprehension of color.
This advancement enables the formless attribute of color topossess value and enables a more objective analysis.
At the same time, Stepanova's research also illustrates theimportance of color utilization in traditional artwork. Stepanova's
work emphasizes that the proportion of colors used and thevariety of colors in a traditional artwork play a vital role indetermining the artwork's value or impact [10]. In other words,
the balance between different colors and the range of colors
employed can greatly influence the overall aesthetic andemotional appeal of a traditional painting or drawing. The
proportion of colors is often neglected in the study of digital
paintings, as mentioned in 2.1.

2.4 AI painting
In recent times, there has been a surge in the development ofAI painting systems, exemplified by the disco diffusion approach[11]. Disco diffusion represents a nascent AI painting system that
rapidly generates game concept art based on input keywords,
demonstrating proficiency in image generation for game scenes.
Furthermore, it can emulate the style of specific artists throughmachine learning techniques. Notably, this type of AI system
offers the advantage of producing a substantial number of results
within a short time and at a relatively low cost. Artists canleverage such AI systems to expedite the completion of
preliminary drafts. However, upon evaluating the generated
outcomes, it becomes apparent that further enhancements are
required in terms of visual center processing and color
combinations. Professional artists are still necessary to select themost available results for refinement, and there is space for– 15:2 –The Journal of the Society for Art and Science, Vol. 22, No. 4, pp. 15:1-15:7 (2023)
improvement in color and composition as well. In future plans,
we hope to combine the data obtained from this experiment withAI.

3. Proposed method
Our study follows the structure in Figure 3, comprisingmultiple sequential steps. In the initial step, we used image
segmentation to separate the local region within the image,specifically differentiating the visual center and the non-visual
center regions. A comprehensive explanation of this step can befound in our prior study [12]. In the second step, we divided thedifferent color intervals according to the HSV (hue, saturation,
value) values of the colors into black, white, grey, red, orange,
yellow, green, cyan, blue, and violet.

segmentation methods, including 2*2, 3*3, 4*4, 5*5, and 6*6.
The 2*2 and 3*3 segmentation methods cause the local regions
too large, making it challenging to determine the location of thevisual center. Conversely, segmentation sizes of 5*5 or more than
5*5 cause the local regions that were excessively small, hindering
the comprehensive analysis of color changes within each local
region. In conjunction with Wang's research survey, we chose 4*4segmentation approach to conduct the experiment.

Figure4. Segmentation of game concept art

4.2 HSV Color interval
We determined the specific values for each color interval. Theintervals for specific colors are not consistent in different studies.

Figure 3. Framework of method
Our research methodology is focused on the artistic
perspective, whereby an algorithmic framework is employed toemulate the artistic design process for an artwork. The HSV color
space has been found to be most conducive to traditional color
mixing methods including a wide range of materials such as oil
painting, watercolor painting, and other conventional materials.
Such methods commonly involve the utilization of three primarycolors (red, blue, yellow) to create a range of hues, adjust color
saturation using complementary colors, and change thebrightness by using black and white colors. That is the reason wechose HSV as the color space. By individually scrutinizing the
proportions of each color interval within each local region, wesought the patterns in color usage. These outcomes werecompiled into a table, observing the percentage of different color
intervals. This analysis validated the visual center's consistency,
as established in the preceding image segmentation step. Finally,
we comprehensively analyzed the data presented in the table todraw definitive conclusions regarding the basic color
combination patterns.

4. Experimental method
4.1 Visual center detection
In our initial investigation, a clear differentiation wasestablished between the visual centers and non-visual centers in
the game concept art. As illustrated in Figure 4, we partitioned
the concept art into 16 distinct regions, denoted as local regions.
This approach draws upon the research conducted by Wang [13],which suggests that using a 4x4 grid segmentation represents anefficient method for scene recognition. Each of these segmented
regions includes both the visual center local region and the nonvisual center local region.
At the start of the experiment, we explored various

Figure 5. The range of color HSV values
Figure 5 is the experimentally calculated vague range. Thisvalue includes the maximum and minimum values of Hue,saturation, and lightness. There are many color spaces in thecurrent perception of color, such as Lab, HSV, RGB, and YIQ[14]. For our specific objectives, we have chosen to utilize theHSV color model due to its resemblance to the traditional
approach to color in painting. In traditional painting, a set ofsaturation and lightness base colors is initially available, andthese colors are blended in varying proportions to create a wide
range of colors. This process of color mixing involves the use ofcontrasting colors to reduce purity and brightness, black andwhite to adjust brightness and decrease saturation, and the
addition of the initial single color to enhance saturation. Thesefundamental color grading methods align with the principles ofthe HSV color space. Our method is designed based on the HSVcolor space that aims to analyze color in game concept art fromthe perspective of a designer. Zhang's research [15] highlights thevariations in color space, including RGB, HSV, LAB, YIQ, andOpponent. While the RGB color model is the fastest to employ,
it is also the least accurate, whereas the HSV color model proves
to be more precise [15]. Cui [16] also suggests that the HSV color
space accurately represents the human perception of color whencompared to other color models, demonstrating a strong
correlation with the subjective understanding of color by humaneyes. So, we chose to use HSV as our color space. We areconverting the color values into color labels based on the HSV
range. Then, as shown in figure 6, we count the number of pixels– 15:3 –The Journal of the Society for Art and Science, Vol. 22, No. 4, pp. 15:1-15:7 (2023)
in each color label. Finally, we convert it into a histogram.

4.3 Data Analysis
The algorithm was employed for the computation of the
proportion of visually similar color pixels within the central
visual area, as well as the proportion of analogous colors within
the non-visual area, for each distinct local region. The resulting
outcomes were subsequently recorded in the table, as shown in
Figure 6. A total of one hundred instances of game concept art
were selected as samples, sourced from the esteemed game
concept art community, Artstation [17]. This platform isrenowned for hosting an extensive collection of original artworks
from prominent games. Each individual sample was segmented
into sixteen local regions, amounting to a cumulative analysis of1600 local regions. Through this comprehensive examination,
distinctive patterns in color combinations were discerned anddocumented.
16 Local Region
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
0
0
0 0.260
0 0.74 0.35 0.44 1.22 1.24 3.32 0.29 0.33 0.14
black
97.13 90.67 96.22 96.17 97.22 85.33 58.18 70.37 20.54 31.09 36.18 32.3 0.34 0.59 4.16
gray
0.07
0 0.140 0.15 0.11 0.9 0.02
0
0
0
0
0
0
white
0
0 0.07 0.33 0.06 0.09 12.68 10.61 0.27 3.32 6.37 0.09
0
0
0
red1
0
0
0 0.1 0.06 3.58 5.43 1.96 0.01 0.37 0.74 0.01
0
0
0
red2
0 0.01 1.27 0.36 2.2 9.62 9.37 11.12 0.64 4.63 1.71 3.4
000orange0 0.02 0.04 0.03 0.16 0.17 0.45
0
0 0.06 0.01 0.06
000yellow
0.02 0.05 0.02
0 0.04 0.01 0.01
0
0 0.010 0.01 1.74 1.87 0.02
green
2.92 10.33 2.1 2.02 0.04 1.45 0.15 0.01 4.1 3.18 0.69 0.45 7.7 14.53 0.49
cyans
0.11 0.12 0.88 1.07 0.53
0 12.44 6.01 74.73 57.71 54.08 58.46 87.35 79.88 94.84
blue
00
0 0.26
0 0.69 1.77 1.22 2.08
2.02 4.19 2.71 3.02 0.7
purples

16
1.5
0.06
0
0
0
0
0
0.02
0.52
95.3
2.92

Figure 6. An example of color combination data, and we
represent the proportion in Figure 7 by using histogram
As shown in Figure 6, we set a range of HSV values for eachcolor by the label of the color which we mentioned in section
4.2, and counted the proportion of each color in each local
region separately by python code. For instance, the orange inthe first local region is shown as 0, which means that there is no
any orange color present in that local region.

In order to analyze the data, we convert the data to thehistogram. Figure 7 illustrates the distribution of colors within
individual local regions. The x-axis pertains to each local region,
while the y-axis represents the proportion of black, white, gray,and other colors present within each region. The red line signifies
the visual center area in Figure 7 and Figure 8. Notably, thehistograms employed depict two distinct approaches to color
analysis. As shown in Figure 7， it encompasses black, white,and gray colors, while the Figure 8 histogram excludes them.
Evidently, the color transitions within the visual center area
exhibit greater intensity. The non-visual center area is almost
occupied by a single color. Lastly, a comprehensive synthesis isconducted, encompassing the observed patterns of color
combinations.

5. Result and Discussion
Following the data analysis conducted in Section 4.3, the initial
step involved the categorization of the entire sample into two
distinct groups. The first group shows a positive correlation
between color alterations and the visual center. In contrast, thesecond group encompassed images wherein no such correlation
was evident between color changes and the visual center.
Subsequently, an analysis was undertaken to discern patterns
within these two groups. Notably, we divided the first group into
two categories based on their characteristics. The first categoryshows significant color changes within the visual center. The
second category is the majority of the image characterized byblack, white, and gray tones, and the visual center represents atleast 50% of the color distribution. Consequently, the entirety ofthe samples was categorized into three distinct typologies.
Our findings indicate the existence of three preeminent color
combinations prevalent within the game concept art. To
categorize these combinations, we designate them as follows:"typical," "balanced," and "monochrome."

5.1 Typical
120
100

80
60
40
20
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Figure 7. An example of color combination data
120
100
80
60

The first color combination, denoted as "typical," accounts for
36.61% in the samples. Figure 9 is one of the examples. Thisparticular combination is distinguished by a pronounced contrast
in color combinations within the visual center, while the nonvisual center primarily comprises analogous colors. Figure 10
visually depicts the distribution of gray, accounting for 51.03%of the image, and blue, accounting for 38.96% of the overallcomposition. Those gray and blue belong to the non-visual centerarea. Notably, the visual center region exhibits a profusion ofdiverse color combinations. In these findings, it is frequently
observed that the visual center prominently featurescomplementary colors of lower contrast. Complementary colors
are pairs of colors that, when placed next to each other, create astrong visual contrast. It will produce a neutral color like gray orwhite when they mix. Complementary colors are positioned
directly opposite each other on the color wheel. Understanding
complementary colors can be helpful when making color choices
in design and art, as they can be used to create eye-catching andharmonious color schemes. Lower contrast complementarycolors are colors that are 120 degrees apart on the color wheel,
like yellow and blue.

40
200
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Figure 8. An example of color combination data

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50
40
30
20
10
0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Figure 13. The data of monochrome color combination.

Figure 9. The data of typical color combinations. [18]Consequently, black and white colors collectively constitutemost of the game's concept art. Because of the large proportionof black and white, we created additional tables for this type ofsample for color analysis. After removing the proportion of black
and white colors, we generated Figure 13. Overall, red comprises
14.61% of the entire composition. However, when narrowing our
focus to the visual center, the proportion of red significantly
escalates to 49.39%. In this specific method of color combination,
the singular color used can be any hue, yet it predominantly
appears exclusively within the visual center, assuming aconsiderably higher proportion in the visual center.

120
100

80
60
40
20
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

5.3 Balance

Figure 10. The data of typical color combinations.

5.2 Monochrome
The second color combination, monochrome, accounted for33.82% of the entire sample. This particular type predominantly
employs black, white, and gray as the primary colors, which
dominate a significant portion of the concept art. Subsequently, a
singular color is utilized to accentuate the visual center. Figure
11 illustrates the distribution of colors, indicating that black
accounted for 55.50%, while white accounted for 18.17%.

The final color combination, referred to as "balanced,"accounted for 29.75% of the analyzed sample. This particular
combination did not exhibit strong discernibility of the central
visual area within our previous investigation. Thereby there is no
red line to highlight the visual center area. As shown in Figure 14,
this type of game concept art, they are predominantly based on
specific objects to create a visual center such as characters ormonsters. In the case of this color combination, the concept art
displays a relatively equitable distribution of colors. For instance,
as depicted in Figure 15, blue comprises 27.16% of thecomposition, red accounts for 23.78%, and orange represents
27.29%. This balanced color combination is characterized by anear parity in color proportions.

Figure 11. The data of monochrome color combination [19].
120

Figure 14. The data of balanced color combination [20].
120

100

100

80

80

60

60

40

40

20

20

0

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Figure 12. The data of monochrome color combination.

Figure 15. The data of balanced color combination.
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5.4 Correlation

Reference

Figure 16. One of the experiment examples.
We use an experiment to calculate the positive correlation
between color changes and visual center. Fifty participants wererecruited for the purpose of discerning the visual center in each
image. To facilitate this task, an interactive application wasdeveloped by us using Python. Participants were able to designate
the visual center of each image through a manual clicking screen.
Subsequently, we merge all the results together. As shown in
Figure 16, the regions where the dots exhibit the greatest density
serve as the visual center. and we compared these results with theconclusions in this paper and judged them true if they matched,or false if they did not match. In the end, the number of samples
judged true in all samples of typical and monochrome reached85.71% of all samples.

6. Conclusion and future work
In the culmination of this investigation, we have meticulously
examined 1600 local regions within the game concept art,
resulting in the identification of three primary color combinations.
Furthermore, we have successfully discerned and characterized
the attributes of these combinations, encompassing proportion,
saturation, lightness, and hue. Additionally, we have empiricallyverified the association between color contrast and the visual
center, establishing a positive correlation wherein heightenedcolor contrast corresponds to the visual center.
Subsequent to the aforementioned conclusions, we
acknowledge several limitations in this study. The analysis ofcolorful, cartoon-style samples poses challenges in drawing
relatively accurate inferences due to their inherent complexityand vividness. Additionally, it is plausible that inadvertent
segmentation of crucial areas during the image segmentation
process may lead to inaccuracies in the analysis results.
Moving forward, we intend to pursue more extensive researchin two key areas. Firstly, we aim to optimize the outcomes of ourcurrent experiments. Given the absence of a definitive standard
in painting or art, we endeavor to refine the range of color values,
thereby enhancing the practical applicability of our findings
within the realms of painting instruction and AI-based painting
systems. Secondly, our focus will shift towards investigating
additional factors that influence viewers' attention in the context
of digital artwork. For instance, we shall explore the impact ofspecific objects or diverse compositional arrangements indirecting viewers' gaze toward the visual center.

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Tiancheng San is A Ph.D. student at the Tokyo University ofTechnology (TUT)， majoring in the School of Media Science.
He received his master's degree at Full Sail University in 2018.
He worked at Zygobot studio as a 2D concept artist. Now hefocuses on the research about the color in game 2D artworks.

Yoshihisa Kanematsu received his Ph.D. degree at Tokyo
University of Technology (TUT) in 2015. Currently, he is anassistant professor at the School of Media Science, TUT. His
research interests lie in production technology including
directing method, scenario writing and character making foranimation and movies.

Koji Mikami worked at Nissho Iwai Corporation and MK
Company as a producer. In 1998, he established "Creative Lab."at the Katayanagi Advanced Research Institute of Tokyo
University of Technology (TUT), where research on Animation,Game and CG production technology are conducted. Currently,
he is a professor at the School of Media Science, TUT. He
received his Ph.D. degree at KEIO University in 2008. Now his
research interests lie in game design and production technology
for game and animation.
s.

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