Evaluation of the color aesthetics of fine wood based on perceptual cognition

Abstract

In order to study the aesthetic preferences of individuals in terms of wood color, the authors explored the preference for red sandalwood and wenge wood of different hues and lightness values through a combination of an eye movement technique and subjective evaluation. The experimental results showed that: (1) sex factors had a significant effect on the eye movement indexes in a modern aesthetic preference experiment; (2) the preferences of the subjects varied slightly with different wood types but in a lower range; and (3) the effective eye movement indexes in this study were fixation duration, number of fixations, and number of last-sampling positions; in addition, there were differences in the effective eye movement indicators in different experiments. The subjects preferred a low lightness value or color of the chair.

Full Article

Evaluation of the Color Aesthetics of Fine Wood Based on Perceptual Cognition

Na Yu,* Jun Wang, Lian Hong, Beibei Tao, and Chong Zhang

In order to study the aesthetic preferences of individuals in terms of wood color, the authors explored the preference for red sandalwood and wenge wood of different hues and lightness values through a combination of an eye movement technique and subjective evaluation. The experimental results showed that: (1) sex factors had a significant effect on the eye movement indexes in a modern aesthetic preference experiment; (2) the preferences of the subjects varied slightly with different wood types but in a lower range; and (3) the effective eye movement indexes in this study were fixation duration, number of fixations, and number of last-sampling positions; in addition, there were differences in the effective eye movement indicators in different experiments. The subjects preferred a low lightness value or color of the chair.

Keywords: Wood color preference; Hue; Lightness; Eye movement technique; Kansei cognition

Contact information: College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037 P. R. China; *Corresponding author: yuna@njfu.edu.cn

INTRODUCTION

Wood, which is an excellent renewable material, has been widely used in the construction, home, and packaging industries due to its excellent natural properties. Nowadays, with the increased awareness of health and environmental protections, wood products, e.g., wood flooring, furniture, and boxes, are inseparable from the day to day lives of people. The visual impression is important in the perception of product quality, and the color is the most intuitive expression of visual properties. Therefore, it is important to determine the visual impression data of wood color.

Color is a phenomenon of human vision, and it is also a primary form of human access to information. In addition to giving people a visual aesthetic feeling, color also has profound importance in the cognition and emotion of individuals. Brogaard and Gatzia (2017) believe that the experience of color is related to factors of the cognitive system. Stone and English (1998) showed that different color environments in a workplace affected different levels of alertness in terms of work performance. Different colors have different effects on learning efficiency, i.e., the physiological and emotional states during learning (Ayash et al. 2016). Sutton and Altarriba (2016) pointed out that red is most often associated with negative emotions and emotionally loaded words, while yellow and white are associated with positive emotions and emotionally loaded words, respectively. In addition to the color itself, the hue, lightness, and saturation of a color stimulation also have a substantial correlation with the experiences of happiness, sadness, fear, and calm in an individual (Geslin et al. 2016). Color affects the aesthetic preferences and emotional psychology of an individual. Therefore, based on the emotional feeling of the user, the study of human visual physiological and psychological affects color review. Beauty preferences and exploring the psychological implications of the visual color hidden in the design can better establish a set of color application standards that meets the needs of people in the process of interactive design.

Recently, relevant research on wood color has focused the following topics: the effects of changes in color (Liu et al. 2017; Reinprecht et al. 2018) during wood aging, wood modifications including heat treatment (Aydemir et al. 2012), photodegradation (Timar et al. 2016), and impregnation (Zhang et al. 2010), as well as the aesthetic evaluation of the wood color itself. However, the study of wood color evaluation primarily has focused on the visual physical quantity of wood (Yang et al. 2012; He et al. 2016), the surface characteristics (Broman 2001), and its emotional characteristics (Broman 2001; Fujisaki et al. 2015). The point measurement method using a colorimeter or spectrometer is usually used to measure wood color (Varodi et al. 2017), while using the wood color calculation method to complete the transformation of the L*, a*, and b* in the L*a*b* space to the Munsell color space to find the corresponding wood color (Liu et al. 2013), and using the sense isometric method to study the relationship between the visual physical quantity and psychology (Liu et al. 1995). Few studies have quantitatively analyzed aesthetic preferences for wood color based on perceptual cognition to make it more accurately meet the perceptual needs of individuals. Therefore, it is extremely important to find an accurate way to evaluate the aesthetic preference of wood color.

Kansei engineering is used to quantify the feelings and needs of individuals, to use rational thinking to solve perceptual problems, to make qualitative and quantitative analysis of uncontrollable perceptual problems, and to make the original Kansei engineering needs of emptiness become rational and evidence-based. Currently, Kansei engineering is used not only in packaging and modeling design (Djatna and Kurniati 2015; Vieira et al. 2017), web design (Fu et al. 2016), and visual comfort evaluation (Korsavi et al. 2016; Buratti et al. 2018; Liu and Kang 2018), but it has also achieved many accomplishments in terms of the study of color aesthetics. Manav (2017) asked 170 color non-experts to match the most appropriate adjectives with a given street view to study urban color-emotional design color schemes. Hsiao and Yang (2016) used a questionnaire to select 10 product images and calculated a three-color harmonious aesthetic measurement based on aesthetic measurement theory to predict the color trend design system that meets market demands. Among them, the semantic differential (SD) method was one of the important methods used in Kansei engineering. A psychometric measurement was carried out through a language scale to obtain the perceptual cognition of the subject, and the basic data for the subsequent Kansei engineering analysis was constructed to determine the bias of the tester on “inductive demand” and is particularly applicable to color trend research. Shieh and Yeh (2015) used the SD method and preference evaluation questionnaire to investigate the aesthetic Kansei of the subject for the color of various sports shoes. Miyoshi et al. (2015) used the SD method to evaluate the best blue primary colors from four different blue colors (430 nm, 450 nm, 470 nm, and 480 nm) from the perspective of an inductive engineering evaluation. Tama et al. (2015) used the Kansei engineering SD method to measure the color emotion of each color scheme and obtain the color scheme that can represent the “inductive demand” and its application situation. Therefore, based on the study of color aesthetic preferences obtained in the study of perceptual engineering, it not only can satisfy the validity of scientific calculation, but also it can satisfy the perception of the subjective experiences of the individual.

Eye-tracking movement technology reflects the cognitive process and can predict the psychomotor performance. Currently, eye movement technology, through the quantitative analysis of physiological data, has been used not only in advertising evaluation (Xing and Qiang 2009), appearance design (Mamassian 2016), and interface design evaluation (Higa et al. 2006), but also in color research. Lee et al. (2013) used an eight natural color system (NCS) as stimuli to identify eye movement in experiments and eye movement indicators, e.g., gaze duration, and showed a correlation between gaze time and color preference ranking and eye movement patterns. Mirijovsky and Polpelka (2016) studied 24 subjects via an eye movement technique to find the most realistic and optimal color settings. Ho et al. (2015) explored the visual effects of different color assignments on subjective color preferences through eye tracking technology. The results showed that the average pupil size determined via eye movement indicators can effectively reflect the difference between color preference and visual comfort. Liu et al. (2015) analyzed the number of gazes and gaze duration of college students watching different wardrobe combinations through eye movement technology and indicated that subjects of this age group prefer samples with bright colors.

Previous studies usually have investigated the relationship between form and perceptual cognition or the relationship between morphology and eye movement. Traditional perceptual evaluation involves subjective assessment of the overall form, but it does not clarify the effects of different parts on the perceptual evaluation of the individual. The microscopic analysis of eye movements may make up for this defect in subjective evaluation. Hsu et al. (2017) had the subjects carry out eight SD evaluations for 16 chairs and used an eye movement tracking system to analyze the changes in the fixation points of the participants as they performed various perceptual assessments. A correlation between eye movements and perceptual evaluation was observed. Therefore, the authors combined the two methods to carry out the present research. Through eye movement experiments and a questionnaire survey, Choi et al. (2012) evaluated the effects of the attention of the consumer on the label information and discussed the effect of over-the-counter drug packaging design on consumer risk judgment. Köhler et al. (2014) hypothesized that by using eye movement tracking, the traditional Kansei engineering method can be extended to evaluate designs and to obtain objective data for customer product perception and evaluation. Muslim et al. (2013) studied the effects of four newspaper advertisement combinations via eye-movement techniques and questionnaires. The study found that color factors were effective in their experiment. Wang et al. (2018) combined eye tracking technology with subjective evaluation to study the visual perception of nine different cardboard products. Ho et al. (2015) explored the visual effects of different color assignments and subjective color preferences through eye tracking technology supplemented by questionnaires before and after testing. Jia and Niu (2017) studied the aesthetic characteristics of 22 kinds of wood commonly used in Ming style furniture through a combination of a questionnaire survey and an eye tracking test and explored the basic laws of the aesthetic trend of material color. This provided a basis for the author’s quantitative study of the aesthetics of wood.

Hue is the primary characteristic of color and the most accurate standard to distinguish different colors. Lightness refers to the diffuse reflectance of the color. Kearney (1966) used a paired comparison method to determine the tone preferences of three different hue levels and three levels of lightness; the results showed that preference was influenced by the color phase and is not affected the lightness. Gong et al. (2017) used 18 words to study the correlation between color emotion and color preference from the aspects of color, chromaticity, and lightness, and found that color plays a more important role than color and lightness. Shamey et al. (2015) measured 56 normal subjects to evaluate approximately 27 different patches of gray color through psychophysical experiments. The results showed that the perception of a gray scale was affected by the hue. Therefore, this paper explores the effective eye movement index and aesthetic preference of the subjects in observing different wood colors in terms of hue and lightness. First, the objective physiological data of the visual cognition of the subjects were extracted via an eye movement technique. Then, the eye movement indexes were selected according to subjective evaluation, and the aesthetic preference of the subjects for different wood colors were judged in order to improve the color aesthetic evaluation and provide a more effective methodology.

EXPERIMENTAL

Materials and Methods

In this study, red sandalwood and wenge wood were used as the experimental materials. Red sandalwood has a fine and uniform texture, and the wood grain of wenge wood has clear of color and lightness.

This paper divides color and lightness into five grades and explores the aesthetic preference of modern people for fine wood colors by combining eye movement and subjective evaluation.

Experimental subject

The subjects were primarily college students, aged 22 years old to 25 years old, with good health and normal eyesight. The number of participants was 32, including 16 males and 16 females.

Laboratory apparatus

In this experiment, a Tobii 1750 eye movement tracker was used with the Clearview 2.70 program to record, analyze, and derive the eye movement index data during the experiment. The accuracy of the eye movement instrument was 0.5 degrees, and the sampling rate was 50 Hz.

Experimental materials

The experimental materials were divided into pre-experimental materials and formal experimental materials. The pre-experimental materials were selected from two different shades of cabinets, as shown in Figs. 1 and 2. The official experimental materials used four images, obtained by toning the wood with two lightness values and two hue values, which were the lightness of red sandalwood, the hue group of red sandalwood, the lightness group of wenge wood, and the hue group of wenge wood (as shown in Figs. 3 to 6).

The two kinds of wood samples were scanned, and pictures of the wood grains were obtained. Then the wood images obtained were directly processed in Photoshop according to the measured value (V), hue (H), and saturation (S) values of the wood color (the hue of sandalwood is 4.08 YR, the lightness is 1.81, and the saturation is 11.03; the hue of wenge wood is 8.53 YR, the lightness is 2.74, and the saturation is 6.57), which were used as the two basic images. The hue and lightness change thresholds of the two basic pictures were divided equally into five levels and attached to a Su-style chair; therefore, the five levels of pictures A, B, C, D, and E were obtained. In order to reduce the effects of the visual background on the observation of the picture, the five chairs were placed on a white background (1280 pixels x 1024 pixels), forming a large picture. To ensure that each chair in the larger picture appears the same number of times on the upper left, the five chairs were placed in turn at the top left position in the picture once in a row (the other modeling factors did not change). The specific changes in lightness and hue were as follows:

Fig. 1. Pre-experimental material	Fig. 2. Pre-experimental material

Lightness group (as shown in Fig. 3 and 4): each red sandalwood chair was labeled according to its lightness value. The chairs with a lightness value of 0 (the lightness of the original map was 1.81) were labeled A, the chairs with a lightness value of -10 were labeled B, the chairs with a lightness value of -5 were labeled C, the chairs with a lightness value of 5 were labeled D, and the chairs with a lightness value of 10 were labeled E. The labeling of the wenge wood chairs (the lightness of the original map was 2.74) followed the same protocols as the red sandalwood samples.

Hue group (as shown in Figs. 5 and 6): each red sandalwood chair was labeled according to its hue value. The chairs with a hue value of 0 (the hue of the original map was 4.08 YR) were labeled A, the chairs with a hue value of -10 were labeled B, the chairs with a hue value of -5 were labeled C, the chairs with a hue value of 5 were labeled D, and the chairs with a hue value of 10 were labeled E. The labeling of the wenge wood chairs (the lightness of the original map is 8.53 YR) followed the same protocols as the red sandalwood samples.

Fig. 3. The lightness values of red sandalwood. The lightness of the original map was 1.81, and the values of A, B, C, D, and E are 0, -10, -5, 5, and 10, respectively,

Fig. 4. The lightness values of wenge wood. The lightness of the original map was 2.74, and the values of A, B, C, D, and E are 0, -10, -5, 5, and 10, respectively.