International Journal of Industrial Ergonomics 38 (2008) 910–920 Applying aesthetics measurement to product design Shih-Wen Hsiao , Fu-Yuan Chiu, Chong Shian Chen Department of Industrial Design, National Cheng Kung University, Tainan 70101, Taiwan, ROC Received 14 August 2006; received in revised form 23 December 2007; accepted 2 February 2008 Available online 9 April 2008 Abstract In the highly competitive market, varying product color to change its image is one of the best solutions to improve the product competitiveness. In this paper, the relationships among the product image, color area, and aesthetic measurement of the product are studied. The pixels of an area of color are used to obtain the proportionate relationship between different colored areas in a given solid visual angle. Based on the relationship among the Hue, Value, Chroma and colored area proposed by Munsell, the other factors are integrated to set up one formula for evaluating the aesthetic degree of color matching. The aesthetics measurement is considered to be influenced by the color environments, color areas, component colors and display angles of the product. The color planning for developing a cell phone was performed based on this model. The experimental results verified this model can be used for color planning in product design. r 2008 Elsevier B.V. All rights reserved. Keywords: Color harmony; Color combination; Aesthetic measurement; Color area; Color image 1. Introduction Customers usually obtain their first impression of a product from visual stimuli, including the form (Hsiao, 1994a, 1997), color (Hsiao, 1994b, c), and material (Hao et al., 2001). If these three stimuli are well coordinated, the product is more greatly appreciated (Erik and Kwaku, 2000). In the recent decades, several basic color harmonization evaluated methods were proposed such as the aesthetic measure (Birkhoff, 1933; Moon and Spencer, 1944), monochromatic harmonization (Shen et al., 2000), as well as color combination with color area ratio (Ou et al., 2004). Most studies use image processing methods (Lee et al., 2005; Han and Ma, 2002), or a color quantification process to transform the traditional color system into a digital color space (Sirisathitkul et al., 2004; Mattikalli, 1997), for analyzing on computers with the applications of Fuzzy theory (Zhang et al., 1998; Kim et al., 2004), Kansei engineering (Temponi et al., 1999) and Neural Generic Algorithm, etc. The color planning for a product was usually performed based on color psychology. Thus, the merits of color combinations for a product were subjectively evaluated with designers’ experiences (Nagamachi, 1995; Feraund et al., 2001), rather than applying the objective quantifica- tion approach of aesthetics measure. In addition, most products were all sampled with one front view or an oblique image of the product (Chuang and Ou, 2001; Hsieh and Fan, 2000). Using a single view on the product in sampling may cause problems. Thus, the theory of image segmentation (Tanaka et al., 2004) is used to determine the color areas matched on the pro- duct from different angles, and further, to acquire the aesthetic measure of the product such that this system, the product color can be matched with a complete and objective way. With the assistance of this method, a good color planning for the designed product can be done to satisfy the customer demands. 2. Outline of the design model The procedure for performing this design model includes the following steps. (1) Decide the objective product to be designed. (2) Collect the existing products in the market. ARTICLE IN PRESS www.elsevier.com/locate/ergon 0169-8141/$ - see front matter r 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.ergon.2008.02.009 Corresponding author. Tel.: +886 6 2757575x54330; fax: +886 6 2746088. E-mail addresses: [email protected], [email protected] (S.-W. Hsiao).
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doi:10.1016/j.er
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International Journal of Industrial Ergonomics 38 (2008) 910–920
www.elsevier.com/locate/ergon
Applying aesthetics measurement to product design
Shih-Wen Hsiao�, Fu-Yuan Chiu, Chong Shian Chen
Department of Industrial Design, National Cheng Kung University, Tainan 70101, Taiwan, ROC
Received 14 August 2006; received in revised form 23 December 2007; accepted 2 February 2008
Available online 9 April 2008
Abstract
In the highly competitive market, varying product color to change its image is one of the best solutions to improve the product
competitiveness. In this paper, the relationships among the product image, color area, and aesthetic measurement of the product are studied.
The pixels of an area of color are used to obtain the proportionate relationship between different colored areas in a given solid visual angle.
Based on the relationship among the Hue, Value, Chroma and colored area proposed by Munsell, the other factors are integrated to set up
one formula for evaluating the aesthetic degree of color matching. The aesthetics measurement is considered to be influenced by the color
environments, color areas, component colors and display angles of the product. The color planning for developing a cell phone was
performed based on this model. The experimental results verified this model can be used for color planning in product design.
r 2008 Elsevier B.V. All rights reserved.
Keywords: Color harmony; Color combination; Aesthetic measurement; Color area; Color image
1. Introduction
Customers usually obtain their first impression of aproduct from visual stimuli, including the form (Hsiao,1994a, 1997), color (Hsiao, 1994b, c), and material (Haoet al., 2001). If these three stimuli are well coordinated, theproduct is more greatly appreciated (Erik and Kwaku, 2000).
In the recent decades, several basic color harmonizationevaluated methods were proposed such as the aestheticmeasure (Birkhoff, 1933; Moon and Spencer, 1944),monochromatic harmonization (Shen et al., 2000), as wellas color combination with color area ratio (Ou et al., 2004).
Most studies use image processing methods (Lee et al.,2005; Han and Ma, 2002), or a color quantification processto transform the traditional color system into a digitalcolor space (Sirisathitkul et al., 2004; Mattikalli, 1997), foranalyzing on computers with the applications of Fuzzy theory(Zhang et al., 1998; Kim et al., 2004), Kansei engineering(Temponi et al., 1999) and Neural Generic Algorithm, etc.
The color planning for a product was usually performedbased on color psychology. Thus, the merits of color
e front matter r 2008 Elsevier B.V. All rights reserved.
combinations for a product were subjectively evaluatedwith designers’ experiences (Nagamachi, 1995; Feraundet al., 2001), rather than applying the objective quantifica-tion approach of aesthetics measure.In addition, most products were all sampled with one
front view or an oblique image of the product (Chuang andOu, 2001; Hsieh and Fan, 2000). Using a single view onthe product in sampling may cause problems. Thus, thetheory of image segmentation (Tanaka et al., 2004) isused to determine the color areas matched on the pro-duct from different angles, and further, to acquire theaesthetic measure of the product such that this system, theproduct color can be matched with a complete andobjective way.With the assistance of this method, a good color
planning for the designed product can be done to satisfythe customer demands.
2. Outline of the design model
The procedure for performing this design model includesthe following steps.
(1)
Decide the objective product to be designed. (2) Collect the existing products in the market.
ARTICLE IN PRESSS.-W. Hsiao et al. / International Journal of Industrial Ergonomics 38 (2008) 910–920 911
(3)
Tab
The
1
2
3
4
5
6
7
8
Select the adequate image words using questionnaires,and identify the images that consumers project onto theproduct by means of the semantic differential method.
(4)
Divide the product into several elements (parts) basedon their function and construct the basic formcategories for each element using a morphological chart.
(5)
Construct the basic Solid Model for parts by using aparameter design software.
(6)
Search out function carriers for parts by inputtingimage word(s) to present the optimum suggestion ofcolor Harmony.
3. Implementation methods and procedures
Based on the above outline, the implementation proce-dures are described in detail in the following sections, inwhich the color planning for cell-phone design is taken asan example to describe the implementation procedures.
The purpose of this study is to establish a model ofaesthetics measurement for product color matching. Toreach the goal, the relations among the aestheticsmeasurement, color area matched and consumer percep-tion images for a product should be constructed first.
3.1. The selection of imagery vocabulary
The study imposes a two-phase image-semantic surveyon the colors of cell phones. One hundred words that canbe used to describe the color images of cell phones werecollected first. After eliminating some synonyms, 32 pairsof opposite image words were selected (see Table 1).
A questionnaire was designed based on the 32 pairs ofimage words. Thirty design-major students aged between18 and 28 years who had normal sense of distinguishingcolors (i.e. who were not color-blind), were selected as thesubjects. The male/female ratio was 50:50. The subjectswere asked to pick out the top 10 image word pairs fordescribing the image of a cell phone. After the survey, thetop three image word pairs discovered were:
The subjects were asked to give four symbolic colors tothe above six image words with the 129 color charts ofPCCS (Practical Color Co-ordinate System) issued byJapan Color Research Institute. The selected samples andtheir HVC values are shown in Table 2. Here, HVC is theabbreviation of the three attributes of a color used inMunsell color system, which represent Hue, Value, andChroma, respectively. Hue is the basic quality of the colorand there are 10 hues—red (R), yellow red (YR), yellow(Y), green yellow (GY), green (G), blue green (BG), blue(B), purple blue (PB), purple (P), and red purple (RP) in theMunsell’ hue circle. Value is a measure of the colorlightness, and Chroma is a measure of the color saturation.
3.2. Construction of the aesthetics measurement formula
To evaluate the aesthetics measurement of a coloredproduct, the calculation formula should be constructedfirst. In this study, we try to construct the aestheticsmeasurement formula based on the colors matched onproduct with Munsell color system.The procedures for constructing the formula are
described in detail in the following subsections.
3.2.1. The image values of hues
As we know that the image of a product is alwaysaffected by the hue of a color used on a product. Here, wetake the top one image (Female) in Table 2 as an exampleto describe how to evaluate the image values of Hues ontothe product. First, the hues on the ‘‘Munsell’s hue circle’’are divided into 100 equal segments (Moon and Spencer,1944). From Table 2, we see that Hue ‘‘10R’’ fits the imageword ‘‘Female’’ best, so the image value of Hue ‘‘10R’’ istaken as 1 (Uh ¼ 1) for the image word—Female. On theother hand, Hue ‘‘5B’’ fits the image ‘‘Male’’ best, so theimage value of Hue ‘‘5B’’ on the image of Female is takenas 0 (Uh ¼ 0). Thus, the image values for Hues ‘‘10R’’ and‘‘5B’’ on the image ‘‘Female’’ are given as 1 and 0,respectively, as shown in Fig. 1. The image values for othercolors on the image Female can be obtained as follows: theimage values for the colors between Hue ‘‘10R’’ and Hue‘‘5B’’ are divided into two sections. One is in the rangestarting from Hue ‘‘10R’’ to Hue ‘‘5B,’’ clockwise. The
C D
Boring–Interesting Futuristic–Classical
Strong–Fragile Prospective–Retrospective
Old–Young Monotonous–Various
Peaceful–Lively Impractical–Practical
Crazy–Elegant Rude–Detailed
Steady–Frivolous Excited–Quiet
Congruous–Incongruous Poor–Impressive
Formal–Leisurable Popular–Personality
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Table 2
The top four colors fitting the given three pairs image words
1 2 3 4
a (Female) 10R 8.5/5 4R 7/8 6RP 6.5/7.5 4R 7/12
b (Male) 5B 3.5/8 3PB 2/5 5BG 3/4.5 9PB 2/5
c (Futuristic) 3G 6.5/9 5BG 7/6 5B 5.5/8.5 3G 7.5/6
d (Classical) 7P 2.5/9.5 6RP 2.5/5.5 4R 3.5/11.5 9PB 2.5/9.5
e (Valuable) 3PB 2.5/9.5 5Y4.5/2.5 9PB 5/10 5B 3/8
f (Plain) 3GY 7.5/2 3G 7/2 5Y 7.5/2 3GY 8.5/3
Fig. 1. The distribution for hues on the Munsell’s Hue circle.
S.-W. Hsiao et al. / International Journal of Industrial Ergonomics 38 (2008) 910–920912
other is in the range starting from Hue ‘‘10R’’ to Hue ‘‘5B’’counterclockwise. The image values for the colors in rangeI are given from 1 to 0 clockwise by dividing into 55 equalsegments, while those for colors in range II are given from1 to 0 counterclockwise by diving into 45 equal segments.For example, the given image value for the colorHV/C ¼ 4YR 7/12 on the image of ‘‘Female’’ is taken asUh ¼ 51/55 ¼ 0.93, while that for the color HV/C ¼ 6PB2/5 is taken as Uh ¼ 11/45 ¼ 0.24.
3.2.2. The image values of Values
The image value of a Value onto to the product isdefined with the same approach used for a Hue. In Table 2,the Value of the top one color for the image ‘‘Female’’ is8.5, so the image value is given as 1 (Uv ¼ 1) when Value is8.5 (see Fig. 2). On the contrary, for the color with Value3.5 is considered as the image corresponding to Male andthe image value of this Value for the image ‘‘Female’’ is
taken as 0 (Uv ¼ 0) shown in Fig. 2. To evaluate the imagevalues for colors with different Values, the Values shown inFig. 2 are divided into two parts. The part I is from Value3.5 (Uv ¼ 0) to Value 8.5 (Uv ¼ 1), the part II includes twoportions, one is from Value 8.5 (Uv ¼ 1) to Value 10.0, andthe other is from Value 0.0 to Value 3.5 (Uv ¼ 0). In part I,the Values are divided into 10 equal segments from Value3.5 (Uv ¼ 0) to Value 8.5 (Uv ¼ 1). So for a color with theValue located in this range, the image value for this colorcan be calculated proportionally based on the scale. Forexample, the image value for the color HV/C ¼ 2RP 6/9 onthe image of ‘‘Female’’ is taken as Uv ¼ 5/10 ¼ 0.5. Whilefor a color with the Value located in part II, the imagevalues are divided into 10 segments from value 8.5 (Uv ¼ 1)to Value 10.0 and from Value 0.0 to Value 3.5 (Uv ¼ 0).And the image value can be calculated proportionallybased on scale 10. For example, the image value for thecolor HV/C ¼ 2Y 9/5 on the image of ‘‘Female’’ is taken asUv ¼ 9/10 ¼ 0.9.
3.3. The relation among Value, Chroma, and color area
Moon and Spencer (1944) pointed out that colors with ahigher Value and Chroma must take up a smaller area thanthose colors with a lower Value and Chroma in order toobtain a better aesthetics measurement in color harmoni-zation. The relation among Value, Chroma and color areais shown in Eq. (1). From the equation, we can see that thecolor area is reciprocal to the product of Value andChroma:
VA � CA
VB � CB¼
AB
AA, (1)
where VA is the Value of color A; VB is the Value of colorB; CA is the Chroma of color A; CB is the Chroma of colorB; AA is the area of color A; and AB is the area of color B.Based on this equation, it is concluded that when the
Values and Chromas of colors used in color planning for a
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Fig. 2. The scale of ‘‘Female’’ image for Munsell Values.
S.-W. Hsiao et al. / International Journal of Industrial Ergonomics 38 (2008) 910–920 913
product are different, the color areas should be regulated toget the same aesthetics measurement for the designedproduct.
3.4. The acquisition of product color areas
The color area will influence the aesthetics measurementof a product as mentioned previously, and a feasiblemethod for calculating the color area has been proposed(Tanaka et al., 2004; Lee and Han, 2005). In this study, wetake the ratio of pixels in each color area as the ratio ofareas. An image processing software was used to calculatethe pixels and the average values of RGB for the color oneach area. The pixel value of the product, At, is taken as thenumber of pixels really occupied by the product image inthe picture. With this approach, the pixel value of eachcomponent can be calculated, and the relation of compo-nent areas is shown in Eqs. (2) and (3):
At ¼ A1 þ A2 þ A3 þ � � � þ An, (2)
Ar ¼Ai
Aj
, (3)
where At is the total area of entire product, An is the area ofcomponent n; Ar is the area ratio of component i withrespect to component j, Ai is the area of component i, andAj is the area of component j.
3.5. Derivation of the equation of aesthetics measurement in
color matching for a product
The aesthetics measurement of a product varies with thecolors used for different components of the product. Tounderstand the influence of Hue and Value of a color onthe aesthetics measurement of a product matched withcolors, the study employs Uh and Uv introduced in theprevious section to define the image perception of aconsumer reflects on the product—Ui, as shown in Eq. (4).
Ui ¼Uhi
for a chromatic color;
Uvifor an chromatic color;
((4)
where Ui is the contribution of the color on component i tothe image perception of the product, Uhi
is the contributionof the Hue on component i to the image perception of theproduct, and Uvi
is the contribution of the value on
component i to the image perception of the product,1pipn, and n is the number of colors used for the product.The aesthetics measurement of a product is not only
influenced by Uh and Uv but also by the Value and Chromaof the used colors. According to Eq. (1), color area isreciprocal to the multiplication of Value and Chroma.However, the color area has a direct influence on theaesthetics measurements for a product matched withcolors, which means that the multiplication of Value andChroma of a color is reciprocal to the aesthetics measure-ment of the product. Based on this relation, the factorof the Value and Chroma of a color contributed tothe aesthetics measurement of a product, Qi, is defined inEq. (5).
Qi ¼
ð1=ViCiÞPn
i¼1ð1=ViCiÞ
for a chromatic color;
ð1=ViÞPn
i¼1ð1=ViÞ
for an chromatic color;
8><>: (5)
where Qi is the contribution of the Value and Chroma ofthe color on component i to the perception of the product,1pipn; Vi is the Value of the color on component i, Ci isthe Chroma of the color on component i, and n is thenumber of colors used for the product.To calculate the influence of color area on the aesthetics
measurement of a product, the ratio relation amongcomponent color areas (acquired from Eq. (3)) is used todetermine Pi, the ratio between each area and the largestarea, as shown in Eq. (6).
Pi ¼Ai
max A�
, (6)
where Pi is the ratio between the largest area and the areaof the color on component i, 1pipn; Ai is the area ratio ofthe color on component i, and n is the number of colorsused for the product.After Ui, Qi, and Pi are acquired, the aesthetics
measurement of a product, Mp, can be found as shownin Eq. (7).
Mp ¼
Pni¼1ðUi þQiÞP
n, (7)
where Mp is the aesthetics measurement of a product, and n
is the number of colors used for the product.
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Table 3
The code of each component
No. Part of the product Code No. Part of the product Code
1 Case-Front-Top CFT 5 Case-Front-Down CFD
2 Case-Back-Top CBT 6 Case-Back-Down CBD
3 Case-Side CS 7 Screen S
4 Function Key FK 8 Numeral Key NK
Fig. 4. The coordinate system set on the 3D model of a cell phone.
S.-W. Hsiao et al. / International Journal of Industrial Ergonomics 38 (2008) 910–920914
3.6. The calculation program of aesthetics measurement for
product color matching
The study develops a calculation program based onVisual Basic.net with the formulas derived in the previoussection. The operation interface is shown in Fig. 3. Theprogram can process the calculation of the aestheticsmeasurement of a product (Mp) with five colors at most. Asfor products without colors, they can be processed byinputting their Value and pixel values.
4. Experimental verification
The aesthetics measurements for product color matchingobtained in the last section were used for color planning inthe development of products. A case study is taken toexplain both the application methods and procedures asfollows.
4.1. The selection of target products
The development of a cell phone is taken as an example todescribe the methodology. With the aid of Pro/EngineerWildfire 2.0, a three-dimensional model, and a front viewimage is used as an origin of the observation, which then turns301 with respect to the X-axis and 151 to the Y-axis (Fig. 4).
4.2. The analysis of cell-phone components
According to function, position and production process,the components of the product are divided into differentcolor parts (see Table 3). In color planning, different colorsare filled in according to the parts shown in Fig. 5, and theaverage value of RGB are then taken from the 3D model ofeach component in the scene for calculating the aestheticsmeasurement of the product. The average RGB values areusually varied with the variation in light, shade, andprimary colors.
Fig. 3. The interface for calculating
4.3. Restrictions of the study
The color planning of the cell phone is performed basedon the aesthetics measurement of the color matching givenin Eq. (7). To make the color planning fits the cell phonesavailable in the current market, the study has the followingrestrictions:
1.
M
Colors for the components CFT, CBT, and S of the cellphone are fixed, and which are not taken intoconsideration in calculating the aesthetics measurementof color matching;
p value for color matching.
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Fig. 5. The location of each component.
Fig. 6. The interface for the aesthetics computation system.
Fig. 7. An output 3D model based on the given parameters.
S.-W. Hsiao et al. / International Journal of Industrial Ergonomics 38 (2008) 910–920 915
2.
Only the colors of cell-phone components CFD, CBD,CS, and NK are changed;
3.
The number of product colors is restricted to 3 at most; 4.
Fig. 8. Flow chart for the operation system.
The Munsell’s Value 7.5 is taken to represent the silvercolor used in the product component (color g1).
4.4. The color aesthetics computation system of components
development
A color aesthetics computation system was developedusing Visual Basic.net. The operation system is shown inFig. 6. With this system, the parameters of the observationangle, and color charts of image words, HVC values andpixels values of component areas can be given. The operationcore comes from the aesthetics measurement evaluatingformula derived previously. After giving the requiredparameters, different Pro/Engineer files will be outputaccording to the viewing angle and matched colors ofcomponents to help the designer to revise the design (Fig. 7).The flow chart for the operation system is shown in Fig. 8.
4.5. The relation between color charts having different
images and the aesthetics measurement of products
In this case, the image word—Female is taken as thetarget image of a cell phone for color planning. Therefore,if a product gets a higher aesthetic measurement, it isconsidered to be a product more suitable or preferred fora female customer. To examine the accuracy of thecalculated result, we put the top two symbolic color chartsamong the six image words obtained in Section 2 intoproduct components with a two- or three-color pattern.The calculated aesthetics measurements for the productcolor matching are shown in Table 4.
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Table 4 (continued )
Rendering No. Code Color
chart
Pixels Mp
S.-W. Hsiao et al. / International Journal of Industrial Ergonomics 38 (2008) 910–920916
Table 5 is an induction of the data acquired fromTable 4. It is indicated that the image word—Female hasthe highest aesthetics measurement of product colormatching in two- and three-color patterns. Contrarily,
Table 4
The Mp values for three pairs of image words
Rendering No. Code Color
chart
Pixels Mp
n2-a1g1 CFD g1 53498 0.60
CBD g1 15949
(Female) CS a1 30031
FK&NK a1 23240
n2-b1g1 CFD g1 53498 0.45
CBD g1 15949
(Male) CS b1 30031
FK&NK b1 23240
n2-c1g1 CFD g1 53498 0.49
CBD g1 15949
(Futuristic) CS c1 30031
FK&NK c1 23240
n2-d1g1 CFD g1 53498 0.49
CBD g1 15949
(Classical) CS d1 30031
FK&NK d1 23240
n2-e1g1 CFD g1 53498 0.46
CBD g1 15949
(Fancy) CS e1 30031
FK&NK e1 23240
n2-f1g1 CFD g1 53498 0.51
CBD g1 15949
(Cheap) CS f1 30031
FK&NK f1 23240
n3-a1a2g1 CFD g1 53498 0.59
CBD g1 15949
(Female) CS a2 30031
FK&NK a1 23240
n3-b1b2g1 CFD g1 53498 0.46
CBD g1 15949
(Male) CS b2 30031
FK&NK b1 23240
n3-c1c2g1 CFD g1 53498 0.47
CBD g1 15949
(Futuristic) CS c2 30031
FK&NK c1 23240
n3-d1d2g1 CFD g1 53498 0.54
CBD g1 15949
(Classical) CS d2 30031
FK&NK d1 23240
n3-e1e2g1 CFD g1 53498 0.48
CBD g1 15949
(Fancy) CS e2 30031
FK&NK e1 23240
n3-f1f2g1 CFD g1 53498 0.50
CBD g1 15949
(Cheap) CS f2 30031
FK&NK f1 23240
Table 5
The rank of Mp values for the three pairs of image words
Image n ¼ 2 n ¼ 3 Total Rank
Female 0.60 0.59 0.60 1
Male 0.45 0.46 0.46 6
Futuristic 0.49 0.47 0.48 4
Classical 0.49 0.54 0.52 2
Fancy 0.46 0.48 0.47 5
Plain 0.51 0.50 0.51 3
the opposite image word—Male has the lowest aestheticsmeasurement of the product color matching. These resultsshow that the aesthetics measurement obtained with thesystem, conforms to the image word—Female.
4.6. Front panel design based on the image word—Female
According to Eq. (7), the size of the area occupied by acomponent is the key factor affecting the aestheticsmeasurement of product color matching. In the study,the front panel (component CFD) is the component thattakes up the largest proportion of the picture. We then putfour colors that fit the image word—Female, colors a1, a2,a3 and a4 as shown in Table 2, onto product componentsaccording to a two-color/three-color pattern (Tables 6and 7). The results obtained by matching with two colorsare shown in Table 6, while those matched with threecolors are shown in Table 7.Table 8 is an induction of Tables 6 and 7, and it shows
that when using a two-color pattern, the front panel hasthe highest aesthetics measurement when colors a1 and a3are used. In the three-color pattern, the front panel has thehighest aesthetics measurement when color a1 is used. Ingeneral, colors a1 and a3 are recommended colors for frontpanels in the system.
4.7. Using the image word—Female on keypad design
Eq. (7) indicates that the color area of each component isthe most important factor of influencing on the aesthetics
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Table 6
The Mp values of the image matched with two colors
Rendering Code Code Color
chart
Pixels Mp
n2-a1-01 CFD a1 53498 0.61
CBD a1 15949
CS a1 28918
FK&NK g1 24495
n2-a1-02 CFD a1 53498 0.51
CBD a1 15949
CS g1 28918
FK&NK g1 24495
n2-a2-01 CFD a2 53498 0.56
CBD a2 15949
CS a2 28918
FK&NK g1 24495
n2-a2-02 CFD a2 53498 0.52
CBD a2 15949
CS g1 28918
FK&NK g1 24495
n2-a3-01 CFD a3 53498 0.58
CBD a3 15949
CS a3 28918
FK&NK g1 24495
n2-a3-02 CFD a3 53498 0.53
CBD a3 15949
CS g1 28918
FK&NK g1 24495
n2-a4-01 CFD a4 53498 0.49
CBD a4 15949
CS a4 28918
FK&NK g1 24495
n2-a4-02 CFD a4 53498 0.41
CBD a4 15949
CS g1 28918
FK&NK g1 24495
Table 7
The Mp values of the image matched with three colors
Rendering Code Code Color
chart
Pixels Mp
n3-a1-01 CFD a1 53498 0.49
CBD a1 15949
CS a2 28918
FK&NK g1 24495
n3-a1-02 CFD a1 53498 0.51
CBD a1 15949
CS a3 28918
FK&NK g1 24495
Table 7 (continued )
Rendering Code Code Color
chart
Pixels Mp
n3-a1-03 CFD a1 53498 0.49
CBD a1 15949
CS a4 28918
FK&NK g1 24495
n3-a2-01 CFD a2 53498 0.49
CBD a2 15949
CS a1 28918
FK&NK g1 24495
n3-a2-02 CFD a2 53498 0.47
CBD a2 15949
CS a3 28918
FK&NK g1 24495
n3-a2-03 CFD a2 53498 0.45
CBD a2 15949
CS a4 28918
FK&NK g1 24495
n3-a3-01 CFD a3 53498 0.52
CBD a3 15949
CS a1 28918
FK&NK g1 24495
n3-a3-02 CFD a3 53498 0.48
CBD a3 15949
CS a2 28918
FK&NK g1 24495
n3-a3-03 CFD a3 53498 0.48
CBD a3 15949
CS a4 28918
FK&NK g1 24495
n3-a4-01 CFD a4 53498 0.51
CBD a4 15949
CS a1 28918
FK&NK g1 24495
n3-a4-02 CFD a4 53498 0.47
CBD a4 15949
CS a2 28918
FK&NK g1 24495
n3-a4-03 CFD a4 53498 0.49
CBD a4 15949
CS a3 28918
FK&NK g1 24495
S.-W. Hsiao et al. / International Journal of Industrial Ergonomics 38 (2008) 910–920 917
measurement in product color matching. The keypad(components FK and NK) is the component that takesup the second largest proportion of the picture, so the sameapproach used in the previous section is employed to the
The Mp values with achromatic CFD and CBD components
Rendering Code Code Color
chart
Pixels Mp
n2-k-a1-01 CFD g1 53498 0.58
CBD g1 15949
CS 4.11R 28918
FK&NK 8.26R 24495
n2-k-a1-02 CFD g1 53498 0.59
CBD g1 15949
CS 8.61RP 28918
FK&NK 8.26R 24495
n2-k-a1-03 CFD g1 53498 0.56
CBD g1 15949
CS 5.53R 28918
FK&NK 8.26R 24495
n2-k-a2-01 CFD g1 53498 0.59
CBD g1 15949
CS 3.46R 28918
FK&NK 2.51R 24495
n2-k-a2-02 CFD g1 53498 0.58
CBD g1 15949
CS 8.61RP 28918
FK&NK 2.51R 24495
n2-k-a2-03 CFD g1 53498 0.55
CBD g1 15949
CS 5.53R 28918
FK&NK 2.51R 24495
n2-k-a3-01 CFD g1 53498 0.59
CBD g1 15949
CS 3.46R 28918
FK&NK 7.61RP 24495
n2-k-a3-02 CFD g1 53498 0.58
CBD g1 15949
CS 4.11R 28918
FK&NK 7.61RP 24495
Table 9 (continued )
Rendering Code Code Color
chart
Pixels Mp
n2-k-a3-03 CFD g1 53498 0.56
CBD g1 15949
CS 5.53R 28918
FK&NK 7.61RP 24495
n2-k-a4-01 CFD g1 53498 0.51
CBD g1 15949
CS 3.46R 28918
FK&NK 4.10R 24495
n2-k-a4-02 CFD g1 53498 0.56
CBD g1 15949
CS 4.11R 28918
FK&NK 4.10R 24495
n2-k-a4-03 CFD g1 53498 0.57
CBD g1 15949
CS 8.61RP 28918
FK&NK 4.10R 24495
Table 10
The Mp values with chromatic CFD and CBD components
Rendering Code Code Color
chart
Pixels Mp
n3-k-a1-01 CFD a2 53498 0.58
CBD a2 15949
CS g1 28918
FK&NK a1 24495
n3-k-a1-02 CFD a3 53498 0.61
CBD a3 15949
CS g1 28918
FK&NK a1 24495
n3-k-a1-03 CFD a4 53498 0.50
CBD a4 15949
CS g1 28918
FK&NK a1 24495
n3-k-a2-01 CFD a1 53498 0.67
CBD a1 15949
CS g1 28918
FK&NK a2 24495
n3-k-a2-02 CFD a3 53498 0.64
CBD a3 15949
CS g1 28918
FK&NK a2 24495
n3-k-a2-03 CFD a4 53498 0.56
CBD a4 15949
CS g1 28918
FK&NK a2 24495
S.-W. Hsiao et al. / International Journal of Industrial Ergonomics 38 (2008) 910–920918
ARTICLE IN PRESS
Table 10 (continued )
Rendering Code Code Color
chart
Pixels Mp
n3-k-a3-01 CFD a1 53498 0.67
CBD a1 15949
CS g1 28918
FK&NK a3 24495
n3-k-a3-02 CFD a2 53498 0.60
CBD a2 15949
CS g1 28918
FK&NK a3 24495
n3-k-a3-03 CFD a4 53498 0.56
CBD a4 15949
CS g1 28918
FK&NK a3 24495
n3-k-a4-01 CFD a1 53498 0.66
CBD a1 15949
CS g1 28918
FK&NK a4 24495
n3-k-a4-02 CFD a2 53498 0.58
CBD a2 15949
CS g1 28918
FK&NK a4 24495
n3-k-a4-03 CFD a3 53498 0.63
CBD a3 15949
CS g1 28918
FK&NK a4 24495
Table 11
The rank of Mp values for varied CFD components
CFD FK&NK Mean Maximum Minimum Rank
Achromatic color a1 0.58 0.59 0.56 1
a2 0.57 0.59 0.55 2
a3 0.58 0.59 0.56 1
a4 0.55 0.57 0.51 3
Total 0.57 0.59 0.51
Chromatic color a1 0.56 0.61 0.5 3
a2 0.62 0.67 0.56 1
a3 0.61 0.67 0.56 2
a4 0.62 0.66 0.58 1
Total 0.61 0.67 0.5
Table 12
The Pearson analysis for theoretical and experimental
MP System People
n3-k-a1-01 0.58 0.834
n3-k-a1-02 0.61 0.866
n3-k-a1-03 0.5 0.754
n3-k-a2-01 0.67 0.88
n3-k-a2-02 0.64 0.806
n3-k-a2-03 0.56 0.786
n3-k-a3-01 0.67 0.92
n3-k-a3-02 0.6 0.814
n3-k-a3-03 0.56 0.786
n3-k-a4-01 0.66 0.846
n3-k-a4-02 0.58 0.74
n3-k-a4-03 0.63 0.814
Pearson correlation 1 0.775
S.-W. Hsiao et al. / International Journal of Industrial Ergonomics 38 (2008) 910–920 919
keypad design. Taking the four colors (a1, a2, a3 and a4)that fit the image word—Female onto product componentsaccording to two-color and three-color patterns. Theresults are shown in Tables 9 and 10. The results for thatcomponent CFD is colored with the achromatic color g1are shown in Table 9, while those for component CFDcolored with the other three colors fitting the image word—Female are shown in Table 10.
Table 11 is an induction of Table 9 and 10; the resultsshow that if the component CFD is achromatic, the keypadhas the highest aesthetics measurement when colors a1 anda3 are used. If the component CFD is chromatic, thekeypad has the highest aesthetics measurement when colorsa2 and a3 are used. Generally, the four color charts fittingthe image word—Female have a higher aesthetics measure-ment when the component CFD is chromatic, and theproduct has the lowest average aesthetics measurementwhen the component CFD is achromatic and the keypad(components FK and NK) is colored with a4. On thecontrary, the product has the largest average aestheticsmeasurements when the component CFD is chromatic andthe keypad is colored with a4. The result proves the color
harmonizing law that the higher the Value and Chroma,the smaller the area required.
4.8. Pearson analysis for the experimental results
Comparing the aesthetics measurements (MP) shown inTables 9 and 10, it shows that the MP values obtained withthree-color matching (Table 10) are higher than those obtainedwith two-color matching (Table 9). So the samples with three-color matching (Table 10) are used for questionnaireinvestigation. Thirty women aged between 20 and 30 yearswere invited to fill out questionnaires by giving their personalpreferences ranked from 1 to 5. The study transforms the scoreof every sample into a percentage by using Analysis of Pearsoncorrelation (see Eq. (8)). The analyzed coefficient is shown inTable 12 and a scatter plot is made in Fig. 9. The results showthat the Pearson correlation R is 0.775 with the contributionrate R2
¼ 0.600, which shows the two are highly related and itdemonstrates that the aesthetics measurement formula pro-posed in this study satisfies the expectation of target users:
R ¼
PðX � X ÞðY � Y ÞffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiPðX � X Þ2
PðY � Y Þ2
q . (8)
ARTICLE IN PRESS
1.00
1.00
0.80 y = 0.7774x + 0.3501R2 = 0.6
0.80
0.60
0.60
0.40
0.40Average score by people
Pearson correlation = 0.775
MP
valu
e by
sys
tem
0.20
0.200.00
0.00
Fig. 9. The scatter plot of theoretical and experimental results.
S.-W. Hsiao et al. / International Journal of Industrial Ergonomics 38 (2008) 910–920920
5. Conclusion
In new product development, varying product images bychanging their colors or forms is the strategy having lowcost and high performance. In this study, an aestheticsmeasurement evaluation method conducted with the arearatios and the color used for the product components isproposed.
In this model, the color area ratios in differentcomponents of a product in different angles are acquiredby measuring the pixels on the 3D product model.The aesthetics measurement is considered to be influencedby the color environments, color areas, componentcolors and display angles of the product. An aestheticsmeasurement formula is proposed based on the above-mentioned parameters. The color planning for developing acell phone was performed based on this model. Thefeasibility of applying the model to select a good colorplanning for product design was also proved experimen-tally. Though the development of a cell phone is taken asan example, this method can also be used to develop otherproducts.
Acknowledgment
The authors are grateful to the National Science Councilof the Republic of China for supporting this research undergrant NSC94-2213-E006-057.
