Perceptual brightness characterization method and system based on display brightness and chromaticity distribution

Abstract

The invention discloses a perception brightness characterization method and system based on display brightness and chromaticity distribution, and belongs to the technical field of information display. The method comprises the following steps: in a darkroom environment, recording perceived brightness rating and pupil diameter of a display picture by an observer through an eye tracker, and collecting brightness and chromaticity data of each pixel point of a screen at the same time; defining the equivalent brightness of the color stimulation as the gray brightness for generating the same pupil diameter by taking the white field gray stimulation as the reference; calculating a cornea flux density value based on a preset chromaticity-equivalent brightness mapping table in combination with the asymmetric space weighting model; establishing a function relationship between the density value and the perceived brightness, and further associating visual comfort rating; in practical application, the brightness is dynamically adjusted to a human eye comfort interval through real-time screen data analysis. According to the invention, the problem that the traditional method neglects the picture content difference is solved, and an adaptive brightness optimization strategy conforming to visual health is provided for the display equipment.

Description

Technical Field

[0001] The present invention relates to display device brightness adjustment technology, specifically a method and system for characterizing perceived brightness based on display brightness and chromaticity distribution. The method is particularly suitable for mobile terminals such as smartphones. By quantifying the impact of the brightness and chromaticity distribution of the display image on the human eye's perceived brightness, adaptive brightness adjustment based on visual comfort is achieved. Background Art

[0002] With the rapid development of information display technology, the rise in consumer spending power, and the shift in lifestyle habits, the penetration of various display devices in daily life continues to climb. From traditional desktops and laptops to today's ubiquitous mobile devices like smartphones and tablets, electronic displays have become a core means for people to access information, engage in social activities, and engage in entertainment. As a key organ for perceiving external information, the human eye places increasingly stringent demands on the optical performance of display devices. Numerous studies have confirmed that long-term and frequent use of electronic displays can lead to a range of health risks, most notably eye problems such as visual fatigue and dry eye syndrome. It can also disrupt circadian rhythms and even indirectly negatively impact the user's physical and mental health.

[0003] At present, the adaptive brightness adjustment mechanism of mobile devices such as smartphones still has obvious limitations: most existing solutions only use ambient light sensors to sense the external light intensity for rough adjustment, but ignore the content differences of the display image itself - the brightness distribution, color composition and other characteristics of different images have significantly different effects on the human eye's perception of brightness. For example, watching high-brightness or high-contrast images in a dark environment at night can not only easily lead to increased visual fatigue, but may also cause chronic damage to the retina. Especially in the context where using mobile phones before going to bed has become a common habit, the lack of refined brightness adjustment based on the content of the picture makes it difficult to meet the core needs of users for visual health. Therefore, in-depth research on the perceptual brightness representation method based on display brightness and chromaticity distribution, and the construction of an adaptive adjustment strategy that takes into account both ambient light and picture content, are of great practical significance for improving the visual comfort of mobile display devices and protecting users' visual health.

[0004] The quantitative evaluation of display brightness perception has always been a difficult problem in the field of display technology. The core challenge is that the brightness perceived by the human eye for the same physical brightness in different scenarios (such as different picture content, different ambient light) is significantly different. This difference makes it impossible for traditional brightness representation based on physical parameters to accurately reflect the true perception of the human eye. Theoretical research on retinal imaging illuminance and its relationship with visual perception characteristics shows that physical brightness and pupil size are key factors affecting perceived brightness. Furthermore, when the brightness and chromaticity distribution of the display screen are uneven (such as containing alternating light and dark areas or complex colors) and the user's viewing distance changes, the visual angle of the human eye will change accordingly, making the evaluation of perceived brightness more complicated.

[0005] This invention innovatively uses the "spatially weighted corneal flux density" proposed in previous studies as the core variable for characterizing perceived brightness. This parameter can effectively integrate the brightness distribution characteristics within the field of view, and indirectly reflect the differences in the human eye's perception of brightness by predicting the dynamic changes in pupil size, which is highly consistent with the physiological mechanism of the visual system. To verify the effectiveness of this method, the research team designed test images covering a variety of brightness and chromaticity distribution characteristics, and conducted perception experiments using smartphones in a darkroom environment. Ultimately, they constructed a perceived brightness characterization method based on display brightness and chromaticity distribution, laying the theoretical foundation for achieving more accurate adaptive brightness adjustment. Summary of the Invention

[0006] Purpose of the invention: The purpose of the present invention is to address the deficiencies of the prior art and propose a method for characterizing perceived brightness based on display brightness and chromaticity distribution. In order to characterize the perceived brightness of different display brightness and chromaticity distributions, the present invention conducts brightness and chromaticity measurement experiments on a variety of display screens. Observers rate the perceived brightness levels for different types of screen display brightness. The main influencing factors of perceived brightness are analyzed through subjective and objective experiments. A display brightness perception model is established using spatially weighted corneal flux density as a key variable factor. The relationship between perceived brightness and comfort is further quantified through a visual comfort rating experiment for each display screen, thereby optimizing the display design for human eye perception of brightness. Improving human visual perception under the premise of different display screen contents can provide effective assistance to the development and upgrading of mobile display devices. Experimental verification shows that the spatial ambient light illuminance and brightness distribution, the physical brightness of the display, and the pupil diameter have a direct and significant impact on the subjects' perceived brightness.

[0007] Technical solution:

[0008] The present invention adopts the following technical solutions to solve the above technical problems:

[0009] A method for characterizing perceived brightness based on display brightness and chromaticity distribution includes the following steps:

[0010] Step 1: Set up the experimental environment. The experimental platform includes an observer, a smartphone, an eye tracker, and a dark room with an ambient illuminance of <0.1 lx. Observers rate the screen brightness and visual comfort for different display images, which is recorded as perceived brightness. The corresponding display brightness and chromaticity distribution data, as well as pupil diameter data, are recorded. The eye tracker is used to measure the subject's pupil size. It uses an infrared camera to record video images of the subject's eyes. An internal computer analyzes the image data and calculates data such as pupil diameter, displacement distance, and gaze position in real time. Its basic performance is: accuracy <0.4°, spatial resolution 0.03°, and sampling frequency 250Hz. The physical brightness and chromaticity distribution data of each display stimulus are measured using an imaging photometer such as the Konica Minolta CA-2000.

[0011] Step 2: Select a white field grayscale stimulus as the reference stimulus. The equivalent brightness of the color stimulus is defined as the brightness of the reference grayscale stimulus that produces the same pupil diameter. The screen chromaticity distribution data of the subsequent display stimulus is calculated using Table 1 to calculate the corresponding equivalent brightness.

[0012] Step 3: For each perceived brightness rating, rate the perceived brightness of the display using a 13-point rating scale from very dark to very bright.

[0013] Step 4: Calculate the spatially weighted corneal flux density of the corresponding display screen using Equation 1 based on the brightness and chromaticity distribution data of each display screen; use the smartphone's brightness control system to set five system brightness levels (the center brightness when the phone displays a full-screen white field). The actual physical brightness will be different when displaying different screens.

[0014] Step 5: Fit the functional relationship between the two, Equation 2, based on the perceived brightness and spatially weighted corneal flux density data;

[0015] Step 6: Fit the functional relationship between the two, Equation 3, based on the perceived brightness and visual comfort rating data; use a 13-level rating scale from very uncomfortable to very comfortable to rate the visual comfort of the display.

[0016] Step 7: When actually using the display device, substitute the detected screen brightness and chromaticity distribution data into Function Equation 1 to obtain the spatially weighted corneal flux density, and then substitute the spatially weighted corneal flux density value into Function Equation 2 to obtain the perceived brightness level. Based on the perceived brightness, determine whether the display brightness setting is reasonable. If it is unreasonable, adjust the system brightness setting of the screen.

[0017] Preferably, the equivalent brightness relationship table 1 is:

[0018] Table 1 L of the colors used in the pupil size estimation experimenteq / L value

[0019] Color w R G B R1 G1 B1 RG RB GB u′ 0.198 0.469 0.123 0.190 0.334 0.161 0.194 0.200 0.311 0.141 v′ 0.468 0.526 0.575 0.140 0.497 0.522 0.304 0.564 0.307 0.456 <![CDATA[L eq / L]]> 1.000 0.506 0.463 5.416 0.726 0.701 1.879 0.570 2.370 1.183

[0020] Preferably, the spatially weighted corneal flux density function equation 1 is:

[0021]

[0022] Among them, L eq (x,y) (u',v') is the equivalent brightness of point (x, y), whose value is determined by the brightness and chromaticity coordinates u' and v'. In practical applications, L can be calculated in advance. eq The value of / L is stored in a two-dimensional lookup table. The value of F can be calculated by inputting the luminance and chromaticity distribution matrices L(x,y), u'(x,y), and v'(x,y) through Table 1.

[0023] Preferably, σ1=0.258, σ2=0.127.

[0024] Preferably, functional equation 2 is:

[0025]

[0026] Among them, L P is the rating scale of brightness perception (13 levels in total); F is the spatially weighted corneal flux density, calculated as shown in Equation 1; x and y are the tangent values ​​of the horizontal and vertical viewing angles, respectively.

[0027] Preferably, k1=0.061 and k2=0.389.

[0028] Preferably, functional equation 3 is:

[0029]

[0030] Among them, C represents the level of visual comfort, which includes 13 levels in total. P The calculation is shown in formula 2.

[0031] Preferably, k3=9.433, k4=6.651, and k5=4.609.

[0032] Preferably, the perceived brightness L P The general function relationship 4 is:

[0033]

[0034] Among them, F 环 Represents the spatially weighted corneal flux density of the ambient light to which the backlit display is exposed.

[0035] Eye tracker, used to measure pupil diameter;

[0036] Imaging photometer, used to display the brightness and color distribution of the device screen;

[0037] A perceived brightness calculation module is used to calculate the perceived brightness based on the display brightness and chromaticity distribution and functional equations 1 and 2, and to determine whether the perceived brightness meets a preset perceived brightness range;

[0038] Preferably, a brightness adjustment strategy for comfortable display can be formulated according to Formula 3.

[0039] Beneficial effects: Compared with the existing solutions, the above characterization scheme adopted in the present invention has the following advantages:

[0040] The present invention provides a method for characterizing perceived brightness based on display brightness and chromaticity distribution. Compared to current methods that use a crude, single representation of display brightness perception, fail to fully consider the impact of display content on perceived brightness, and lack comprehensive subjective and objective characterizations, such as those incorporating psychological factors, this method provides a more comprehensive and applicable method for characterizing perceived brightness for flat panel displays. Based on the basic structure of the human visual system and the principles of brightness perception, the present invention constructs an experimental platform and conducts ambient light simulation experiments in various lighting scenarios. The method innovatively rates perceived brightness based on the observer's subjective visual comfort. The method combines experimentally measured brightness and chromaticity distribution data and pupil diameter data from smartphone displays to calculate spatially weighted corneal flux density. A functional relationship between perceived brightness and the spatially weighted corneal flux density of the smartphone display is established. The method also determines comfortable display brightness based on the most reasonable perceived brightness, and combines experimentally measured visual comfort rating data to establish a functional relationship between comfortable display brightness and perceived brightness. This method, when using a mobile display device such as a smartphone, simply detects the display type to implement a smartphone display brightness adjustment strategy.

[0041] Specifically, the innovative features of the present invention include:

[0042] 1. Change the equivalent brightness of the color stimulus L eq Defined as the reference white field grayscale stimulus brightness that produces the same pupil diameter, establish the chromaticity coordinates (u', v') and L eq / L mapping table (Table 1);

[0043] The beneficial effects of this innovation are: quantifying the nonlinear effects of different chromaticities on perceived brightness (e.g. blue stimulus L eq / L=5.416Red is 0.506), breaking through the traditional limitation of relying only on physical brightness. Figure 6 ) Objectively verify the model, avoid the uncertainty of subjective evaluation, and improve the biological rationality of the model.

[0044] 2. A radially asymmetric weighting function (Equation 1) is proposed to integrate the equivalent brightness of each position on the screen;

[0045] The beneficial effects of this innovation are: horizontal / vertical differential weighting (σ1≠σ2), capturing the brightness attenuation effect at the edge of the field of view ( Figure 5 ) to address errors in traditional uniformly weighted models. Automatically adapting to changes in viewing distance through x,y (viewing angle tangent values) improves the applicability of mobile devices.

[0046] 3. Propose the perceived brightness model (Equation 2) and the visual comfort model (Equation 3);

[0047] The beneficial effects of this innovation are: clarifying the optimal perceived brightness L P =6.651( Figure 7 ), providing a target for automatic adjustment and avoiding empirical dimming. P Directly output comfort score C(R 2 >0.8, Figure 8 ), simplify the human factors engineering evaluation process.

[0048] 4. Propose to introduce ambient light factor F into the basic model 环 (Formula 4);

[0049] This innovation has the following benefits: unifying the perceived impact of ambient light and screen light, resolving issues with overexposure or darkening at night or in bright sunlight, and supporting dynamic adjustment of display strategies for complex lighting environments (e.g., color temperature and spectrum differences). BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 Overall implementation block diagram of the method of the present invention.

[0051] Figure 2 Schematic diagram of the spatial position between the human eye and the display device according to the present invention.

[0052] Figure 3 Schematic diagram of the display image type according to the present invention.

[0053] Figure 4 A density diagram showing the RGB distribution of an image.

[0054] Figure 5 Schematic diagram of the brightness and chromaticity distribution of the display.

[0055] Figure 6 Effect diagram of pupil diameter changing with corneal flux density.

[0056] Figure 7 Graph showing the changing trend of perceived brightness score with corneal flux density.

[0057] Figure 8 Correlation between the predicted values ​​of the perceptual brightness estimation model and the subjective evaluation values ​​of the perceptual experiment. DETAILED DESCRIPTION

[0058] The present invention will be further explained below with reference to the accompanying drawings.

[0059] The present invention provides a method for characterizing perceived brightness based on display brightness and chromaticity distribution, such as Figure 1 As shown in the figure, ① is an observer who rates the brightness of the image displayed on the mobile phone; ② is an eye tracker, which is mainly used to capture the size of the human pupil; ③ is a fixed smartphone used for Figure 3 Various test images are shown. The smartphone used in this invention is equipped with a 6.78-inch OLED display with an aspect ratio of 20:9 and a resolution of 2800×1260. The size of the observer's pupil is measured using an eye tracker, which captures video images of the subject's eyes using an infrared camera.

[0060] like Figure 2 As shown in the figure, the spatial positions of ④ in the observer's eye diagram and ⑤ on the display device screen can be calculated based on the observation distance and screen size, and the visual angle of the human eye when viewing the screen can be calculated. The evaluation experiment of the present invention was carried out in an indoor darkroom environment. The subject sat on a comfortable chair next to a 75 cm high table with his chin resting on a bracket. The height of the chair and the bracket can be adjusted to ensure that each subject's line of sight is perpendicular to the screen. The smartphone screen is perpendicular to the table and has a horizontal distance of 27 cm from the subject's eyes. Therefore, the horizontal viewing angle is 14.65° and the vertical viewing angle is 31.42°; when the observation distance changes, the visual angle of the human eye when viewing the screen will change.

[0061] The test picture designed by the present invention is as follows Figure 3 As shown in the figure, three typical pictures were designed (full screen display, centered display and random distribution of display content), and 5 effects were designed for each picture type. A total of 15 pictures were used as display stimulus sources in the evaluation experiment. Figure 3 When each picture in the video is viewed, 75 display light stimuli are formed.

[0062] In order to analyze the grayscale of all pixels in each picture, the present invention first extracts color information from the image, which is composed of three channels: red (R), green (G) and blue (B). The grayscale value of each channel is between 0 and 255. Secondly, the probability density estimation (Kernel Density Estimation, KDE) function is used to smooth the grayscale data of each pixel RGB to generate a continuous probability density function, which makes the distribution characteristics clearer and helps to understand the distribution characteristics of colors in the image. Another advantage of the KDE function is that it helps to calculate the average image grayscale / brightness level of each picture, so that the display brightness adjustment strategy can be further upgraded in combination with the results of different display stimulus perception experiments. Finally, press Figure 4 The density map of the RGB primary color distribution corresponding to each experimental image is drawn in the manner shown.

[0063] When the human eye is looking at a screen displaying complex content, the brightness of each position on the screen has different effects on the observer. Therefore, when establishing a brightness perception model, the brightness distribution of the entire field of view must be considered. The spatially weighted corneal flux density correction model described in the present invention shows that different display brightness distributions in the field of view will produce different stimulations to the human eye. The study found that under the same brightness stimulus intensity, a brightness distribution that is more concentrated (narrower viewing angle) in the horizontal direction is more likely to induce pupil dilation, which highlights the difference in weights of vertical and horizontal brightness stimuli in the field of view. Specifically, the study found that the radially asymmetric bivariate normal distribution function is more effective in weighting the spatial distribution of brightness than the Gaussian function.

[0064] The present invention uses pupil size to reflect the difference in brightness perception of the human eye, and pupil size is affected not only by brightness but also by chromaticity. Under the same physical brightness, there are significant differences in the perceived brightness of different chromaticity stimuli, among which the perceived brightness of red stimuli is higher than that of neutral white, while that of blue stimuli is lower. In order to simplify the parameters, the present invention introduces equivalent brightness into the original spatial weighted corneal flux density to summarize the effect of chromaticity on pupil size. In order to retain the existing structure of the spatial corneal flux density model, grayscale stimulation is selected as the reference stimulation. The equivalent brightness of a color stimulus is defined as the brightness of a reference grayscale stimulus that produces the same pupil diameter. That is, the equivalent brightness is L eq A stimulus with a brightness of L produces the same pupil diameter as a grayscale stimulus with a brightness of L. The ratio of equivalent brightness to luminance is determined by the chromaticity coordinates. Therefore, the formula for estimating the spatially weighted corneal flux density based on the spatial distribution of brightness and chromaticity can be expressed as Equation (1).

[0065]

[0066] Where, L eq (x,y) (u',v')is the equivalent brightness of point (x, y), and its value is determined by the physical brightness and chromaticity coordinates (u', v'). In practical applications, L can be calculated in advance. eq The value of / L is stored in a two-dimensional lookup table. By inputting the luminance and chromaticity distribution matrices L(x,y), u'(x,y) and v'(x,y), the value of F can be calculated by looking up Table 1.

[0067] Table 4.1 L of the colors used in the experiment eq / L value

[0068] Color W R G B R1 G1 B1 RG RB GB u′ 0.198 0.469 0.123 0.190 0.334 0.161 0.194 0.200 0.311 0.141 v′ 0.468 0.526 0.575 0.140 0.497 0.522 0.304 0.564 0.307 0.456 <![CDATA[L eq / L]]> 1.000 0.506 0.463 5.416 0.726 0.701 1.879 0.570 2.370 1.183

[0069] When the picture used in the present invention is used as a display stimulus, its brightness and chromaticity distribution are irregular or uneven. Therefore, it is necessary to obtain the brightness distribution data of the entire display screen. In order to show the brightness distribution effects of different experimental pictures, one of the stimulus cases is listed here. The chromaticity distribution of the entire display screen is an important part of our calculation of equivalent brightness. The present invention calculates the corresponding chromaticity distribution data of the above case as follows: Figure 5 At the same time, the physical brightness is normalized, so that the chromaticity distribution can be observed and the corresponding relationship with the brightness can be reflected.

[0070] When the brightness distribution of the display screen is uneven or the screen size changes, such as Figure 3 For the three types of images shown, before building a perceptual brightness model, formula (1) must be applied to calculate the spatial distribution of brightness within the human eye's field of view. The brightness of pixels at different positions on the screen is processed separately to analyze their impact on the human eye's perceived brightness.

[0071] Figure 2 The figure shows the visual angle of the human eye when viewing the smartphone in this round of experiments. Based on the visual angle range in this figure, we calculated the spatially weighted corneal flux density value of each display stimulus in this round of experiments. In addition, we also analyzed and demonstrated the correlation trend between pupil diameter and perceived brightness score and corneal flux density. Figure 6 As shown, pupil diameter decreases with increasing corneal flux density; conversely, Figure 7 showed that perceived brightness scores increased with increasing corneal flux density.

[0072] Given that spatially weighted corneal flux density exhibited good predictive performance in the aforementioned analysis, the present invention used it as the primary independent variable, and the perception score as the dependent variable. Formula (2) primarily utilizes the inverse tangent function to control the perceived brightness rating range (i.e., 1 to 13), indicating that the primary factors influencing perceived brightness are display brightness and pupil diameter. Formula (1) can be used to estimate pupil diameter while also taking into account the brightness distribution across the entire field of view. Combining these features, we optimized the display brightness perception model, as shown in Formula (2).

[0073]

[0074] Where, L P is the rating scale of brightness perception (13 levels in total); F is the spatially weighted corneal flux density, calculated as shown in formula (2); x and y are the tangent values ​​of the horizontal and vertical viewing angles, respectively; the two constant coefficients k1 = 0.061 and k2 = 0.389.

[0075] So far, we have established an optimized form of the display brightness perception estimation model through the method of the present invention, and analyzed the matching degree between the subjective evaluation value of the perception experiment and the predicted value of the model, such as Figure 8 As shown in Figure 2, the estimated model has a high goodness of fit (R 2 >0.8). In this section, we preliminarily establish a prediction model for perceived brightness by combining experimental data with theoretical analysis, providing a basis for subsequent brightness adjustment strategies.

[0076] In fact, an important application of studying perceived brightness is to improve the visual comfort of display devices, so the inventors used a 13-level visual comfort rating standard ranging from "very uncomfortable" to "very comfortable" to score the visual comfort of each display stimulus.

[0077] By analyzing the relationship between perceived brightness and visual comfort, this paper finds that they follow the distribution law of the Gaussian function. When the perceived brightness is 6.651, the visual comfort score reaches its peak. This can further determine the optimal physical brightness requirement for each image and provide a reference range for brightness adjustment of smartphones. Therefore, this section also establishes a preliminary visual comfort characterization model, as shown in formula (3):

[0078]

[0079] In the formula, C represents the level of visual comfort, which has 13 levels. P= represents the perceived brightness estimation model, calculated as shown in (4.10); in addition, the three constant coefficients are k3 = 9.433, k4 = 6.651, and k5 = 4.609. Associating visual comfort with perceived brightness can simplify the assessment of visual comfort in visual perception to a certain extent, which is of great significance.

[0080] Under fixed environmental conditions, there is a positive correlation between screen brightness and user ratings. However, display devices are usually used under different ambient light conditions, which include multiple factors such as illumination, spectral composition, correlated color temperature, and main wavelength. When using a display device, the screen brightness must meet the visual perception requirements of the human eye, while taking into account the ambient light conditions in various life or application scenarios. According to this requirement, the ambient light and screen brightness must be adjusted to meet the perception needs of the human eye. Therefore, it is crucial to study the dynamic changes in the brightness perception of the display screen under different ambient light conditions, and the model developed in the present invention has been optimized and enhanced accordingly.

[0081] Therefore, when the ambient light changes, the perceived brightness L P The general functional relationship 4 is:

[0082]

[0083] Among them, F 环 Represents the spatially weighted corneal flux density of the ambient light to which the backlit display is exposed.

[0084] This paper proposes a method for characterizing display brightness perception based on the distribution of luminance and chromaticity in visual space. By analyzing the perceptual characteristics of the human visual system, we studied the uneven distribution of image luminance and chromaticity in the case of complex luminous surfaces, and how the visual angle changes with changes in viewing distance. We cleverly employed the optimized spatially weighted corneal flux density, a foundation of our previous research, as a key factor affecting perceived brightness. Given that pupil size effectively reflects the human eye's perception of light and dark, we monitored pupil size throughout the experiment, thereby strengthening the effectiveness of using corneal flux density as a determinant of perceived brightness.

[0085] This paper uses one of the most commonly used display devices, the smartphone, to establish a rating scale for perceived brightness and design two rounds of visual perception experiments. The experiments employed both objective and subjective measurement methods. The first round of experiments involved displaying 15 images of three different types at five different smartphone system brightness settings. The corneal flux density was calculated based on the spatial relationship between the subject and the smartphone screen, and then quantified and analyzed to construct a universal perceived brightness model with excellent goodness of fit. The present invention further provides brightness comfort requirements for various images displayed on smartphones in a dark environment and establishes a preliminary predictive model formula for visual comfort. Therefore, the display brightness model established by the present invention provides theoretical support for brightness adjustment strategies that conform to the perceptual characteristics of the human eye. It can also provide a data basis for the development of visual comfort standards and the protection of user visual health, as well as provide meaningful guidance for the development of display-related visual comfort standards and the improvement of user visual health.

[0086] In reality, optimal display brightness is not only related to perceived brightness but also to factors such as glare, visual clarity, and observation time. This invention sets these values ​​within specific ranges and does not alter them. This invention focuses on correlating the display brightness of different screen contents with the brightness perceived by the human eye, which can, in a sense, simplify the evaluation of visual comfort in human eyes and has positive implications. This allows for feedback and adjustment of display devices and can guide the upgrade and iteration of related display technologies and equipment.

[0087] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for characterizing perceived brightness based on display brightness and chromaticity distribution, characterized in that: The steps include: Step 1: Set up an experimental platform in a dark room with an ambient light intensity of less than 0.1 lx, including an observer, a smartphone, and an eye tracker. The observer will rate the perceived brightness and visual comfort of the displayed image, and simultaneously record pupil diameter data, screen brightness, and chromaticity distribution data. Step 2: Take the white field grayscale stimulus as the reference stimulus and set the equivalent brightness L of the color stimulus eq Defined as the reference grayscale stimulus brightness that produces the same pupil diameter; Step 3: Based on the chromaticity distribution data collected in step 1, the equivalent brightness of each pixel is calculated using a pre-stored equivalent brightness relationship table; Step 4: Based on the luminance and chromaticity distribution data from step 1, calculate the F value using the spatially weighted corneal flux density formula: Among them, L eq (x,y) is the equivalent brightness of point (x,y), whose value is determined by the brightness and chromaticity coordinates (u',v'); x,y are the tangent values ​​of the horizontal and vertical viewing angles, respectively; σ1 and σ2 are the variances of the spatially weighted corneal flux density function; Step 5: Fit the perceived brightness rating data from step 1 and the F value from step 4 to obtain the functional relationship: Among them, L P is the perceived brightness; k1 and k2 are constant coefficients; Step 6: Fit the perceived brightness L from step 1 P And the visual comfort rating data, we get the functional relationship: Among them, C is visual comfort, k3, k4, k5 are constant coefficients; Step 7: In the actual display device, input the real-time screen brightness and chromaticity data into the formula in step 4 to calculate F, and then use the formula in step 5 to obtain L P , according to L P Adjust the system brightness to a reasonable range.

2. The method according to claim 1, characterized in that In step 1, an imaging color luminance meter is used to measure the brightness value and color coordinates (u', v') of each pixel on the screen, while blocking non-screen light sources.

3. The method according to claim 1, characterized in that In step 2, the pupil diameters of 10 color stimuli and grayscale stimuli are matched to establish an equivalent brightness relationship table, including u', v' and L for each color. eq / L value; The equivalent brightness relationship table is: Table 1 L of the colors used in the pupil size estimation experiment eq / L value 4. The method according to claim 1, wherein In step 3, the chromaticity coordinates (u′, v′) are mapped to L through a two-dimensional lookup table. eq / L value, and then combine it with the physical brightness to calculate the equivalent brightness L eq .

5. The method according to claim 1, wherein The display brightness perception rating scale uses a 13-point rating scale, which categorizes display stimuli in different states into levels 1 to 13, with each level increasing in perceived brightness. Subjective experiments determined the optimal perceived brightness to be 6.

651. Visual comfort ratings also use a 13-point rating scale, which categorizes display stimuli in different states into levels 1 to 13, with each level increasing in perceived brightness. Visual comfort ratings are correlated with perceived brightness ratings using a Gaussian function, with peak visual comfort achieved when perceived brightness reaches its optimal value of 6.

651.

6. The method according to claim 1, characterized in that When the ambient light changes, the formula in step 5 expands to: Among them, F 环 Represents the spatially weighted corneal flux density of the ambient light of the backlight display, where k6, k7, and k8 are constant coefficients.

7. The method according to claim 1, characterized in that σ1=0.258,σ2=0.127。 8. The method according to claim 1, characterized in that k1=0.061, k2=0.

389.

9. The method according to claim 1, characterized in that k3=9.433, k4=6.651, k5=4.

609.

10. A system for characterizing perceived brightness based on the method according to claims 1-9, characterized in that: include: Eye tracker, used to detect pupil diameter in real time; Imaging photometer, used for brightness and color distribution of the screen; The perceived brightness calculation module executes the calculation process of steps 4-7 in claim 1 and outputs the perceived brightness level and adjustment instructions.

Images