2.1.

Experimental Setup

A full-scale driving simulator in a simulated cockpit was used in this study for the participants to be immersed in the pilot’s perspective while observing the display. Participants were asked to sit in the driver’s seat and fasten their seat belts to ensure that the horizontal distance between the eye position and display was 900 mm. The eye position height of each subject was maintained at 1200 mm by adjusting the seat height.

The display is embedded in the analog operation panel at an angle of 120 deg to the horizontal plane. Six high-power LED floodlights outside the left-wing porthole of the aircraft were used to produce five ambient lighting conditions. An extra metal halide lamp in the lower right corner of the cockpit was used to achieve the two highest illumination conditions and enhance the display surface uniformity. No direct glare or secondary reflection glare is caused by these lamps in the view of the observers. The layout of the simulated cockpit is shown in Fig. 1.

Fig. 1

Layout of the simulated cockpit used in the experiment.

2.2.

Lighting Conditions

According to the Weber–Fechner law, human psychological perception is proportional to the logarithm of physical stimulation. Hence, the ambient illumination and display luminance values adopted in this study are uniformly distributed on a logarithmic scale over a wide range. The ambient illumination was set to 1, 10, 100, 1000, and 2700 lx. The use of 2700 l’th instead of 10,000 l’th is owing to the reduction of the ambient illumination by the light shield of the display, the driver’s sunglasses in a driving cockpit, and the limitations of the laboratory equipment. The display luminance is set to 1, 10, 100, 1000, and 316 (equals to ${10}^{2.5}$) $\mathrm{cd}/{\mathrm{m}}^2$. The value of $316\mathrm{cd}/{\mathrm{m}}^2$ served as an interpolation condition to improve the precision of the data structure.

A detailed physical parameter list is given in Table 1. The ambient illumination and optotype luminance are defined as variables, and the display contrast, line width, color of the optotypes, and color temperature of the ambient light remain constant.

Table 1

Physical parameters for experimental lighting conditions.

Some previous studies have shown that reading performance is better for dark text against a light background (positive polarity) than for light text against a dark background (negative polarity).^18–20 However, another study showed that display polarity does not affect readability.²¹ The proofreading speed and accuracy were unaffected by the display polarity. Furthermore, participants’ preferences showed no significant differences in display polarity.²² Therefore, the display is set to negative polarity in this experiment, allowing improved fidelity to the aircraft cockpit or driver’s cab display.

The ambient illuminance is defined as the mean of the illuminance measured at the four corners and center position on the display’s surface.²³ Illuminance uniformity of the display surface in each condition is obtained by dividing the minimum value of the 5 test points by the average value, which is $>0.85$ in each condition. The display luminance is defined as the optotype luminance in a visual task. The contrast ratio is calculated by dividing the optotype luminance by the background luminance of the display. In addition, the optotype line width is calculated using the geometric relationship between the optotype line width on the display and the eye position. The color temperature refers to a cloudless sky, which is equivalent to 6500 K.²⁴

The illuminance and optotype luminance are measured using an illuminance meter (CL-200A, Konica Minolta, Japan) and luminance meter (CS-200, Konica Minolta, Japan), respectively.

2.3.

Experimental Process

Ten subjects participated in this study are undergraduates or graduate students in Fudan University; participants comprised of equal numbers of male and female subjects (average age, 24.20; range, 22 to 26 years). A within-subject design is employed in this experiment, and subjects are asked to complete all conditions in a random order. The sample size is estimated from the results of the pilot study, including the standard deviation and the difference between groups. The confidence level is 95%, and the power of the test is 80%. In addition, about 25 sessions are conducted in five replications to avoid the VF physically caused by the long-time experiment while providing enough time for the participants to fill in the rating scales.^4,17,25 Participants have normal orthoptists and no other eye diseases. The Ethics Committee of the School of Life Science, Fudan University, approved all procedures (No. BE2042). All participants provided written informed consent before the experiment.

Before the start of the experiment, training on measurement exercises and questionnaire explanations are conducted to enhance the stability of the experiment results. Subsequently, the experimental sessions are executed. Figure 2 shows the timetable for each experimental session, which lasts 15 min. Its description and duration are summarized as follows:

Fig. 2

Test procedure for the experiment: the column bar rectangle represents the visual searching task period; the darker grey gridding rectangle represents the period when the visual fatigue scale is conducted; the diagonal striped rectangles represent the periods when the visual comfort scale is collected.

For each session, dark adaptation is arranged to avoid successive contrast effects and VF.²⁶ The 3 min-light adaptation is to make the retina and pupil adapt to the next light condition and in a stable response state.^27–29

Each participant is required to participate in all 25 conditions. In total, 62.5 h were spent in experimental sessions: 25 lighting conditions × 10 participants × 15 min per session.

2.4.

Measurements

2.4.1.

Visual performance

VP means the quality of performance of the visual system of an observer related to central and peripheral vision (CIE S 017:2011).³⁰ The Anfimov table is a classical method used to investigate visual searching performance.^31–34 A graphical user interface (GUI) is programmed (MATLAB, Mathworks) using an automatically generated Anfimov table, which consists of eight letters (A, B, C, B, E, H, K, N, and X). There are a total of 1200 letters in this task, and each letter randomly appears 150 times, as shown in Fig. 3. The participants are asked to cancel the “B” behind “H” by clicking it. The program records the hit rate, false-alarm rate, and duration of the task at the end of each test. In addition, the index of mental capacity (IMC)³⁵ is employed to represent visual capability, as shown in Eq. (1):

Eq. (1)

$$IMC=M/T\times (n-a)/N,$$

where $T$ denotes the time cost, $M$ represents the total letter count, $N$ represents the number of letters that should be deleted, $n$ denotes the number of letters deleted correctly, and $a$ denotes the number of letters wrongly deleted and those that were missed. Finally, the $IMC$ is calculated as the VP score.

Fig. 3

GUI interface of Anfimov table software: The Amfimov table is located in the center of the screen. The character line width is 0.6 mrad according to the test point at the fixed eye position.

3.1.

Visual Performance

The IMC value is used to represent the VP. The calculation process of the IMC is described in Sec. 2.4.1. Then, an ANOVA was employed to investigate the relationship between VP and lighting conditions. Levene’s test reveals the equality of error variances. Table 4 gives the ANOVA results for different dimensions.

Table 4

ANOVA results of VP.

Table 5

ANOVA results for VC.

Table 6

ANOVA results of VF.

Considering the VP as a dependent variable, the ANOVA with the ambient illuminance and display luminance as independent variables show a statistically significant effect of ambient illuminance ($F=2.612$, $p=0.037$) and display luminance ($F=51.618$, $p<0.001$). In addition, the analysis does not show a significant interactive effect between illuminance and luminance on VP ($F=0.740$, $p=0.733$).

The mean and standard error of the VP under different conditions are shown in Fig. 4. It can be observed that the relationship between ambient illuminance and VP is parabolic under low display luminance conditions. Moreover, the VP exhibits a monotonous increase under high display luminance conditions. As the luminance increases, the growth rate of the VP simultaneously decreases. For different luminance under the same illuminance, the VP exhibits nonlinear monotonic increases in the high luminance area with the appearance of response compression.

Fig. 4

Mean and standard error of mean (SEM) of the VP with ambient illuminance as the independent variable for five values of display luminance.

3.2.

Visual Comfort

For the VC, the homogeneity of its variance is tested using Levene’s test. The ANOVA, with the ambient illuminance and display luminance as the independent variable and VC as the dependent variable, shows the main effects of ambient illuminance ($F=9.827$, $p<0.001$) and display luminance ($F=17.005$, $p<0.001$). It also presents the significant interactive effect between illuminance and luminance ($F=3.547$, $p<0.001$). It reveals that different combinations of ambient illuminance and display luminance could significantly affect the VC level. Levene’s test reveals the equality of error variances. Table 5 lists the ANOVA results for different dimensions.

The mean and standard error of the VC under different conditions are shown in Fig. 5. It can be observed that for the aspects of illuminance and luminance, the comfort level exhibits an upward trend first and then a downward trend; the upward and downward speeds are not consistent. In addition, as the luminance level increases, the peak of the VC curve keeps shifting to the right side of the diagram, which suggests that the optimal illuminance out of VC outcomes increases.

Fig. 5

Mean and standard error (SEM) of the VC with ambient illuminance as the independent variable for five values of display luminance.

3.3.

Visual Fatigue

An ANOVA is conducted on VF for ambient illuminance and display luminance. Prior to this, Levene’s test is conducted, and the results reveal the equality of error variances. The ANOVA shows no main effect of the ambient illuminance on VF ($F=0.875$, $p=0.480$); however, the display luminance had statistically significant effects on VF ($F=10.547$, $p<0.001$). It also shows a significant interactive effect between illuminance and luminance on VF ($F=3.967$, $p<0.001$). This indicates that different combinations of ambient illuminance and display luminance may significantly affect VF. Levene’s test reveals the equality of error variances. Table 6 lists the ANOVA results for different dimensions.

The mean and standard error of the VF under different conditions are shown in Fig. 6. The three curves of 10, 100, and $316\mathrm{cd}/{\mathrm{m}}^2$ show a consistent trend of declining first and then rising. In addition, this trend is asymmetric in the rising and declining sides. It should be noted that the curve of $1\mathrm{cd}/{\mathrm{m}}^2$ increases with an increase in illuminance, which indicated that the fatigue increases. Moreover, the curve of $1000\mathrm{cd}/{\mathrm{m}}^2$ decreases with an increase in illuminance; i.e., the degree of fatigue decreases.

Fig. 6

Mean and standard error (SEM) of the VF with ambient illuminance as the independent variable for five values of display luminance.

4.1.

Step1: Establishment of Sub-Model of each Dimension

According to the Weber–Fechner law, the models are established in the logarithmic system of physical quantities to better match human psychological perception.

4.1.1.

Visual performance model

Some critical prior knowledge about the $VP$ model was gained in previous studies. Rea and Ouellette⁴³ verified a significant linear relationship between VP and the logarithm of retinal illumination. In this study, the relationship between ambient illuminance and VP is parabolic and a monotonous increase under low and high display luminance conditions, respectively. Therefore, it can be assumed that the monotonicity under high luminance is owing to the parameter values being on one side of the parabolic peak. Thus, the relationship between the logarithm of the ambient illuminance and the VP is parabolic. In addition, the response compression of the logarithm of the luminance for VP was confirmed in another study,⁴⁴ which prompted us to conduct the logarithmic transformation based on the logarithm of the luminance. Hence, a two-degree polynomial is used to estimate VP using a least-squares criterion, the illumination value between 1 and 2700 lx, and the luminance value between 1 and $1000\mathrm{cd}/{\mathrm{m}}^2$. The polynomial fit is a classical method in psychophysical evaluation with a greater role on issues of design and inference.⁴⁵ The proposed $VP$ model is shown in Eq. (2):

Eq. (2)

$$VP=a_{00}+a_{10}{E}^{\prime }+a_{01}{\log }_{10}{L}^{\prime }+a_{20}{E}^{\prime 2}+a_{11}{E}^{\prime }{\log }_{10}{L}^{\prime },$$

where $VP$ represents the visual performance, $E^{\prime }$ is the logarithm of the ambient illuminance value, and $L^{\prime }$ is the logarithm of the display luminance value. The coefficient of each term is shown in Table 7. Figure 8 shows a graphical expression of the $VP$ as a function of illuminance and luminance.

Table 7

The coefficients of VP, VC, and VF model

Model	a00	a10	a01	a20	a11.	a02	a30	a21	a12	a03
VP	0.5854	0.04972	0.07283	$-0.01249$	0.008367	—	—	—	—	—
VC	23.01	5.097	17.95	1.475	$-0.070$	$-1.066$	$-1.728$	2.275	$-0.5689$	$-1.625$
VF	11.76	$-0.3762$	$-2.297$	0.1498	0.1499	$-1.035$	0.3363	$-0.653$	0.0664	0.7465

Fig. 8

Curved surface of the VP model. VP is plotted as a function of illuminance and luminance, and both scaled logarithmically.

The $VP$ model exhibits a good fitting degree with $R^2=0.90$, and the Pearson correlation coefficient is 0.87. The model reveals that the matching degree of display luminance and ambient illuminance impacts VP, even when the contrast is the same. For example, an excessively bright surrounding illuminance is not conducive to VP, even if the readability does not change.

4.1.2.

Visual comfort model

From the data trend of rising first and then falling at different speeds, three-degree polynomials are used to estimate VC using a least-squares criterion, an illumination value between 1 and 2700 l×, and a luminance value between 1 and $1000\mathrm{cd}/{\mathrm{m}}^2$. The proposed $VP$ model is shown in Eq. (3):

Eq. (3)

$$VC=a_{00}+a_{10}{E}^{\prime }+a_{01}{L}^{\prime }+a_{20}{E}^{\prime 2}+a_{11}{E}^{\prime }{L}^{\prime }+a_{02}{L}^{\prime 2}+a_{30}{E}^{\prime 3}+a_{21}{E}^{\prime 2}{L}^{\prime }+a_{12}{E}^{\prime }{L}^{\prime 2}+a_{03}{L}^{\prime 3},$$

where $VC$ represents the visual comfort, $E^{\prime }$ is the logarithm of the ambient illuminance value, and $L^{\prime }$ is the logarithm of the display luminance value. The coefficient of each term is shown in Table 7. Figure 9 shows a graphical expression of $VC$ as a function of illuminance and luminance.

Fig. 9

Curved surface of the VC model. VC is plotted as a function of illuminance and luminance, and both scaled logarithmically.

The $VC$ model shows an excellent fitting degree with $R^2=0.95$, and the Pearson correlation coefficient is 0.92. The model verifies that the comfortable display luminance increases as the ambient illuminance increases. When there is a mismatch between the ambient illuminance and the display brightness, the user’s comfort level will decrease. This mismatch includes high-luminance displays under dim ambient illuminance and low-luminance displays under bright ambient illuminance.

4.1.3.

Visual fatigue model

For VF, the three curves of 10, 100, and $316\mathrm{cd}/{\mathrm{m}}^2$ exhibit the three-degree polynomial features with a consistent trend of first decreasing and then rising. Moreover, the curves are asymmetric in the rising and falling sides. However, the 1 and $1000\mathrm{cd}/{\mathrm{m}}^2$ curves present a monotonous trend, suggesting that the valleys are not in the parameter range. Hence, three-degree polynomials are also used to estimate VF using a least-squares criterion, the illumination value between 1 and 2700 lx, and the luminance value between 1 and $1000\mathrm{cd}/{\mathrm{m}}^2$. The proposed $VF$ model is shown in Eq. (4):

Eq. (4)

$$VF=a_{00}+a_{10}{E}^{\prime }+a_{01}{L}^{\prime }+a_{20}{E}^{\prime 2}+a_{11}{E}^{\prime }{L}^{\prime }+a_{02}{L}^{\prime 2}+a_{30}{E}^{\prime 3}+a_{21}{E}^{\prime 2}{L}^{\prime }+a_{12}{E}^{\prime }{L}^{\prime 2}+a_{03}{L}^{\prime 3},$$

where $VF$ denotes the visual fatigue, $E^{\prime }$ is the logarithm of the ambient illuminance value, and $L^{\prime }$ is the logarithm of the display luminance value. The coefficient of each term is shown in Table 7. Figure 10 shows a graphical expression of $VF$ as a function of illuminance and luminance.

Fig. 10

Curved surface of the VF model. VF is plotted as a function of illuminance and luminance, and both scaled logarithmically.

The $VF$ model shows an excellent fitting degree with $R^2=0.96$, and the Pearson correlation coefficient is 0.94. The model indicates that an appropriate matching relationship between ambient illuminance and the display luminance led to a low degree of VF, as shown in the green area in Fig. 10. However, when they did not match, severe VF was caused.

4.2.

Step2: Total Scores Evaluated by Analytic Hierarchy Process

AHP is employed to construct an evaluation system by calculating the weight of each dimension in the TS. First, a hierarchical model is built, as shown in Fig. 7. Optimal display luminance is the target layer of the model, and the indicator layer consists of 3D: VP, VC, and VF. The casing layer consists of conditions with different illuminance-luminance combinations.

The data must be preprocessed to be dimensionless and formed the relative response values⁴⁴ by dividing the absolute response value by the maximum response value (within the parameters of this experiment), before entering the model. The preprocessing functions are shown in Eqs. (5)–(7):

According to this framework, $\mathbf{A}$ is defined as the index matrix, which consists of the relative VP score $V{P}^R$, relative VC score $V{C}^R$, and relative VF score $V{F}^R$. In addition, the weights corresponding to the three indicators are $\alpha$, $\beta$, and $\gamma$, which is defined as the weight matrix $\mathbf{W}$. Finally, the product of $A$ and $w$ is the $TS$. Thus, the $TS$ of each condition can be expressed as

To obtain the weight matrix $\mathbf{W}$, a paired comparison matrix $\mathbf{X}$ is constructed; the element $x_{ij}$ in the matrix represents the ratio of the degree of influence of the $i$’th element of $\mathbf{A}$ to the target layer relative to the $j$’th element:⁴⁶

The pairwise comparison matrix data are obtained through expert investigation weight method³⁷ by employing 33 pilots from Airlines Company. All the pilots have more than 100 h of flight experience. The expert investigation questionnaire consisted of six items. A rating of 1/9-1–9 is used for each item,^42,46 where “1” means the $i$’th element has the same influence as the $j$’th element, “1/9” means that the $j$’th element has a stronger influence than the $i$’th element, and “9” means that the $i$’th element has a stronger influence than the $j$’th element. The pilots were asked to answer the questionnaire according to their feelings during flight. The composition of the expert investigation questionnaire is shown in Table 8.

Table 8

Composition of the expert investigation questionnaire.

The consistency of the questionnaires was tested by the consistency ratio.^42,46 For those 18 questionnaires, whose CR were smaller than 0.1, were adopted for further determining the weight vector matrix by the characteristic root method.⁴⁷ The mean results of the weights of each dimension are shown in Table 9.

Table 9

Weights of each dimension by expert investigation weight method.

The TS is calculated using Eq. (8). It can be observed from Fig. 11 that the areas with higher TSs are approximately distributed on the diagonal of the logarithmic coordinate system. This implies that with an increase in ambient illuminance, the display luminance should be correspondingly increased to fulfill the comprehensive needs of the observer in 3D.

Fig. 11

Curved surface of the TS model. The TS is plotted as a function of illuminance and luminance, and both scaled logarithmically.

4.3.

Step3: Exponential Fitting of the Illuminance-Luminance Dimming Model

The TS [based on Eq. (8)] obtained in the second step is the basis for identifying the optimal combination of illuminance and luminance. The 0-1 standardization of $TS$ different luminance under each illuminance is processed to find the relatively optimal luminance for each illuminance. The standardized results are shown in Fig. 12, and an area with a relative TS $(\mathrm{RTS})>0.9$ is defined as the optimal area. Moreover, an exponential fitting is performed for the highest RTS ($\mathrm{RTS}=1$) in illuminance-luminance dimensions, and the dimming model is built as follows:

Fig. 12

Recommended display diming model in this experiment. The white area in the middle of the graph indicates that the RTS is $>0.9$; thus, it is defined as the optimal area. The black curve in the optimal area is the dimming model [based on Eqs. (4)–(9), $\mathrm{RTS}=1$]. The gradient area on both sides is divided according to the contour line of the RTS. A darker color indicates a lower RTS.

6.1.

Effects of Ambient Illuminance and Display Luminance on each Dimension

For the VP part, the results of this study show that both the display luminance and ambient illuminance have significant impacts on VP, even when the contrast is the same. The relationship between the logarithm of the ambient illuminance and the VP is parabolic. This phenomenon may be owing to the change in the contrast threshold of the human eye with ambient lighting intensity. This implies that although the contrast of the display remains constant, the VP declines when the ambient illuminance increases and the threshold increment of the contrast decreases because the contrast threshold also increases. For a different luminance under the same illuminance, the VP exhibits an increase with an increase in luminance. Additionally, it reveals the response compression in the high-illuminance area. In previous studies, only the ambient illumination or display luminance was used to find the optimal solution under particular conditions.^4,5 For example, Shen et al.⁴ showed that search speed on an electronic paper display increased with an increase in illumination from 300 lx (45.6 s), 700 lx (44.18 s) to 1500 lx (43.24 s). Lin⁵ showed that illumination intensity significantly affects character identification performance, which was better at 500 lx and 800 lx than 200 lx. Lin and Huang²⁶ study proved that the normal white ambient lighting condition with 500 lx was the optimal condition accompanied by a display with a primary background. However, these optimal results were obtained under specific illuminance or luminance conditions. In a real application environment, the environmental illuminance is often changed within a range; thus, these results cannot fully support the dimming of the display. Lin and Huang¹⁴ study in 2006 attempted to solve this problem. The variable was set as the combination of screen luminance and ambient illumination, display luminance ranged from 3.1 to $200.0\mathrm{cd}/{\mathrm{m}}^2$, and the ambient illumination from 200 to 800 lx. The study indicated that character identification under relatively high ambient illumination is more affected by the background luminance of the screen than by the contrast ratio or contrast sensitivity. In contrast, the parameter range of the present study is broader (the display luminance and ambient illumination range from 1 to $1000\mathrm{cd}/{\mathrm{m}}^2$ and 1 to 2700 lx, respectively), and the parameters are set equidistantly in logarithmic coordinates according to the human body’s perception characteristics of physical quantities. Our study shows the consistent conclusion that the readability of negative displays is affected by the display luminance when the contrast remains constant and obtains optimal display luminance, which can induce the best VP under different ambient illumination.

For the VC part, the present study identifies that the ambient illuminance and display luminance reveal interaction effects on VC. Wang et al.²⁵ collected the subjective comfort evaluation of subjects observing three-dimensional pictures. They indicated that the “highest luminance” of $47.3\mathrm{cd}/{\mathrm{m}}^2$ performed better than the low luminance ($0.38\mathrm{cd}/{\mathrm{m}}^2$) for ambient illuminance values of both 55 lx and 300 lx. Based on the luminance level in their study, they inferred that a brighter display luminance creates a more comfortable feeling for viewers. However, this conclusion is one-sided because they did not set a high range for the display luminance; thus, the negative effect of excessively bright luminance was not identified. Huang and Menozzi⁴⁹ showed that discomfort glare impairs peripheral VP in the attending stimulus in a virtual reality environment. When the display luminance is too high, it becomes a glare light source, which affects the comfort of the visual environment. The discomfort caused by the low luminance of the display is primarily owing to the anxiety over not being able to find a target quickly.⁵⁰ Na and Suk¹⁶ showed that people preferred a smartphone screen at $40\mathrm{cd}/{\mathrm{m}}^2$ with an ambient illuminance below 1 lx. This is consistent with the optimal area in Fig. 11. However, $55\mathrm{cd}/{\mathrm{m}}^2$ is the optimal value when compared with the recommended dimming curve in this study, and the difference may be caused by the experimental design and data analysis. They did not set an intermediate value between 40 and $70\mathrm{cd}/{\mathrm{m}}^2$, and concluded by directly using the relative optimal luminance among these values. In addition, Lin et al.⁵¹ investigated the impact of overall display luminance on VC in long-distance (5 m) observation. The results showed that the relationship between the comfort rating and luminance is parabolic. This is very similar to the relationship identified in this study. The difference is that this study employs a third-degree polynomial to fit the luminance and VC because the comfort rating declines faster when the luminance is higher than the optimal value. This suggests that an excessively bright display can cause the glare phenomenon to affect VC. In general, this study explores the optimal combination of display luminance and ambient illuminance on a larger scale, and forms a quantitative VC model on this basis.

For the VF part, this experiment indicates that the display luminance primarily affects VF. Lee and Whang⁶ showed that VF increased slowly with an increase in the display luminance, particularly when the display luminance is low. However, at a high luminance level, for which the luminance of still images was higher than $400\mathrm{cd}/{\mathrm{m}}^2$, the viewer felt intense VF. However, Chang et al.⁵² indicated that the display medium and ambient illuminance had no significant effects on participants’ subjective VF. These differences may have occurred because the displays employed in the experiments were all electronic paper displays, which are not self-luminous devices.

6.2.

Wide-Range Dimming Model based on Three Dimensions

According to the results of each dimension, it can be observed that the optimal conditions for the 3D are varied. For example, high display luminance can contribute to VP; however, it simultaneously causes severe VF and low VC. This regular can be explained by the Pearson correlation analysis results of the data in this study, which shows that a positive correlation between VP and VC with $r=0.351$ ($p<0.01$), a negative correlation between VP and VF with $r=-0.188$ ($p<0.01$), and a negative correlation between VC and VF with $r=-0.551$ ($p<0.01$). Although the results show significant meaning ($p<0.01$), it is not a high correlation degree; that means exploring the optimal combination is a complex optimization problem and all the 3D, including VP, comfort levels, and VF, should be considered for the dimming model.

In this study, sub-models of each dimension are constructed to clarify their characteristics. Moreover, a comprehensive optimal display dimming model is built in the illuminance-luminance dimension by calculating the weight of each dimension in the TS. This study provides a basis for solving the problem of complex light environment matching.

However, this study is based on the negative polarity display, and the applicability of the positive polarity display needs to be further investigated. The parameter range can also be expanded in future studies. In addition, the participants in this study are in a small age range from 22 to 26. Due to the ocular structural changes by age,⁵³ a larger age range inclusion criterion should be considered in future studies.