A method and system for multidimensional quantification of color discrimination of a white light source

Abstract

This invention discloses a multidimensional quantification method and system for the color discrimination power of a white light source. The method presents preset color sample pairs under the white light source to be evaluated and conducts a color discrimination power test. Based on a sliding time window, it obtains parameters for task accuracy, time cost, blinking behavior, visual comfort, and drift-diffusion model. These indicators are then normalized and oriented according to four dimensions: color discrimination accuracy, color discrimination efficiency, blinking physiological comfort, and visual comfort, resulting in a multidimensional color discrimination power score (MCDS). This invention can simultaneously characterize multiple dimensions of color discrimination performance under white light and their changes over time, solving the technical problem that existing color discrimination power evaluation methods have only one evaluation dimension and are difficult to apply to self-emissive display scenarios.

Description

Technical Field

[0001] This invention belongs to the fields of lighting quality evaluation, human-caused lighting and visual perception measurement technology, and in particular relates to a multi-dimensional quantification method and system for color discrimination power for white light sources, especially suitable for scenarios where indoor white light lighting and self-emissive display terminals work together. Background Technology

[0002] Color discrimination refers to the human eye's ability to distinguish different colors or color differences under specific lighting conditions. It is a key visual performance indicator in lighting quality evaluation, display interaction, medical image reading, industrial inspection, cultural relic display, and office and study scenarios. Existing methods for evaluating the color discrimination of light sources mostly rely on single results from color chess sorting experiments, color difference judgment accuracy, or subjective evaluation, which can describe the influence of light sources on color discrimination to a certain extent.

[0003] However, color discrimination is not a single, static result, but a continuous process involving color information acquisition, evidence accumulation, decision-making, ocular physiological responses, and visual load. When evaluating solely based on accuracy, it's difficult to distinguish whether high accuracy stems from better color information input or from the observer investing more time and cognitive resources in compensation. Furthermore, relying solely on subjective evaluation is easily affected by individual differences and evaluation biases, making it difficult to provide stable quantitative results.

[0004] Furthermore, self-emissive display terminals are widely used in modern indoor environments. Although the displayed colors are generated by the light emitted by the display screen, ambient white light still affects the perception of displayed colors through mechanisms such as color adaptation changes, superposition of screen reflected light, reduction of effective contrast, and accumulation of visual fatigue. Existing light source color discrimination indicators are mostly established for the surface colors of objects, making it difficult to directly reflect the impact of white light sources on the color discrimination ability of displays.

[0005] Furthermore, many practical tasks involve continuous viewing, such as image recognition, online learning, design and drafting, remote work, and precision inspection. Color perception performance is affected over time by visual fatigue, decreased attention, changes in adaptation, and learning effects; the overall average score cannot reveal the difference between high short-term accuracy and long-term stability.

[0006] Therefore, there is an urgent need for a white light source color discrimination evaluation scheme that can simultaneously quantify accuracy, efficiency, blink physiological comfort, visual comfort, and time dynamic characteristics, so as to be applicable to the color discrimination evaluation of self-emissive display devices under different white light illumination environments. Summary of the Invention

[0007] The purpose of this invention is to provide a multidimensional quantification method and system for the color discrimination power of white light sources. This method establishes a multidimensional evaluation framework for the color discrimination power of white light sources by judging task results based on color differences, recording response time, recognizing non-contact blinking behavior, visual evaluation, and estimating drift-diffusion model parameters. This addresses the technical problem of existing technologies being unable to comprehensively characterize the accuracy, efficiency, physiological comfort of blinking, visual comfort, and temporal dynamic characteristics of color discrimination under white light sources.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a multidimensional quantification method for the color discrimination power of a white light source, comprising: Identify the white light source to be evaluated, obtain the optical parameters of the white light source to be evaluated, and construct a color discrimination test environment that includes a display terminal, observation position, and environmental adaptation conditions; Preset color sample pairs are displayed on the display terminal and a light source color discrimination test is performed. The results of the observer's color discrimination test and the response time are recorded. Simultaneously acquire facial video of the observer, and extract blinking behavior data based on the facial video; Visual evaluation scale results were collected from observers before and after the color discrimination test. A single test process is divided into multiple time periods according to a preset time window; Calculate or obtain the task accuracy index, time cost index, blinking behavior index, scale evaluation index, and cognitive decision model parameters for each time period. Based on the obtained indicators and parameters, the four dimensions of color recognition accuracy, color recognition efficiency, blink physiological comfort, and visual comfort are calculated according to directional consistency. The Multidimensional Color Discrimination Score (MCDS) of the white light source to be evaluated is calculated based on the dimensional scores of the four dimensions. Based on the changing trends of MCDS and the dimensional scores of the four dimensions over different time periods, the color discrimination evaluation results of the white light source to be evaluated are output.

[0009] Furthermore, the optical parameters include one or more of the following: spectral power distribution, illuminance, correlated color temperature, color rendering index, and chromaticity deviation; the color discrimination test environment in step 1 includes the settings of display terminal brightness, display white point, observation distance, field of view, and environmental adaptation time.

[0010] Furthermore, the color discrimination test is a color difference evaluation experiment, which collects the observer's judgment results on whether the color difference is perceptible and the reaction time. The preset color sample pair includes at least one central color and multiple peripheral colors generated around the central color. The central color and the peripheral colors have a preset color difference in a uniform color space, and the color sample pair is presented randomly during the test.

[0011] Furthermore, the visual evaluation scales include one or more of the following: cognitive load scale, visual fatigue scale, and visual comfort scale. The process of dividing a color discrimination test into multiple time periods according to a preset time window includes: dividing a single color discrimination test into multiple time periods according to a sliding time window, and merging the multiple time periods into short-term and long-term stages according to the test time sequence; defining a period of less than or equal to K minutes as short-term and a period of more than K minutes as long-term.

[0012] Furthermore, the aforementioned task accuracy metric is accuracy (Acc), which is calculated as follows:

[0013] in, N acc The number of times the observer's judgment matches the preset color difference judgment standard. N To effectively determine the total number of times, the preset color difference judgment standard is to calculate the color difference of color sample pairs in the CIECAM16-UCS uniform color appearance space. D E' ,when No. A difference greater than a preset threshold is considered a perceptible difference, while a difference less than or equal to the preset threshold is considered an imperceptible difference.

[0014] Furthermore, the time cost indicator includes response time RT, which is calculated based on the interval between two adjacent effective response timestamps and is statistically obtained separately in each time period. The acquisition of the blinking behavior indicators includes: extracting eye feature points from the observer's facial video, calculating the eye aspect ratio, and identifying blinking events based on local troughs in the aspect ratio time series; the blinking behavior indicators include blink rate (BR) and blink rate variability (BRV).

[0015] Furthermore, the scale evaluation indicators include one or more of the following: mental demand index (MD), blurred vision index (BV), task hindrance index (FL), and task engagement intensity index (EL), which are obtained by filling out a form. The cognitive decision-making model includes a drift-diffusion model, which models the observer's color discrimination judgment process based on accuracy (Acc), reaction time (RT), and reaction time variance, and obtains the drift rate (v) and boundary distance. The non-decision time Ter; where v is used to characterize the efficiency of color information acquisition. Ter is used to characterize the degree of caution in decision-making, while Ter is used to characterize the physiological processing delay in the non-decision stage.

[0016] Furthermore, the indicators and parameters are first normalized, converting them into dimensionless scores ranging from 0 to 100. Then, the normalized indicators and parameters are aligned in direction, and dimensional scores for the four dimensions are calculated. The higher the dimensional score, the better the color discrimination performance in the corresponding dimension. The calculation formulas for each dimensional score are as follows:

[0017] Where D1, D2, D3, and D4 represent the dimensional scores for color discrimination accuracy, color discrimination efficiency, blink physiological comfort, and visual comfort, respectively; Acc is the normalized accuracy, v is the normalized drift rate, and RT is the normalized reaction time. 1 is the normalized boundary distance, Ter is the normalized non-decision time, BR is the normalized blink rate, BRV is the normalized blink rate variability, MD is the normalized mental effort index, BV is the normalized blurred vision index, FL is the normalized task stalling index, and EL is the normalized task engagement intensity index.

[0018] Furthermore, the Multidimensional Color Discrimination Score (MCDS) of the white light source to be evaluated is calculated based on the dimensional scores of the four dimensions. The calculation method is as follows:

[0019] Among them, D1, D2, D3 and D4 represent the dimensional scores of color discrimination accuracy, color discrimination efficiency, blink physiological comfort and visual comfort, respectively; the larger the MCDS, the better the multidimensional color discrimination ability of the white light source to be evaluated.

[0020] This invention also provides a multi-dimensional quantification system for the color discrimination power of a white light source, comprising: The light source parameter acquisition module is used to determine the white light source to be evaluated, acquire the optical parameters of the white light source to be evaluated, and construct a color discrimination test environment that includes a display terminal, observation position, and environmental adaptation conditions. The color sample presentation and behavior response acquisition module is used to present preset color sample pairs on the display terminal, perform light source color discrimination test, and collect the observer's color discrimination test results and response time. A facial video capture and blink recognition module is used to capture facial videos of the observer during the test and extract blink behavior data based on the facial videos. The visual evaluation scale results acquisition module is used to collect the visual evaluation scale results of observers before and after the color discrimination test; The time segmentation module is used to divide a test process into multiple time periods according to a preset time window; The indicator and parameter acquisition module is used to calculate or obtain task accuracy indicators, time cost indicators, blinking behavior indicators, scale evaluation indicators, and cognitive decision model parameters for each time period. The dimension scoring calculation module is used to calculate the dimension scores of four dimensions—color discrimination accuracy, color discrimination efficiency, blink physiological comfort, and visual comfort—based on the obtained indicators and parameters and according to directional consistency. The multidimensional color discrimination rating calculation module is used to calculate the multidimensional color discrimination rating (MCDS) of the white light source to be evaluated based on the dimensional ratings of four dimensions. The evaluation output module is used to output the color discrimination evaluation results of the white light source to be evaluated based on the changing trends of MCDS and the dimensional scores of the four dimensions over different time periods.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention unifies task accuracy, response time, blink behavior, scale evaluation, and drift-diffusion model parameters into a single scoring framework, achieving multidimensional quantification of the color discrimination power of white light sources.

[0022] This invention utilizes time windows to evaluate the time-varying color discrimination ability, and can simultaneously output short-term performance and long-term stability, making it suitable for applications such as long-term display reading, visual inspection, medical imaging, and human-caused lighting control.

[0023] The MCDS output by this invention and its four-dimensional scores can be used as control targets for intelligent lighting systems, supporting a trade-off between high accuracy, high efficiency, and high comfort based on task type. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0025] Figure 1 A flowchart illustrating a multidimensional quantification method for the color discrimination power of a white light source, provided in an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. The described embodiments are only for illustrating the present invention and are not intended to limit the scope of protection of the present invention. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention.

[0027] like Figure 1 As shown, this embodiment of the invention provides a multidimensional quantification method for the color discrimination power of a white light source, comprising the following steps: Step 1: Determine the white light source to be evaluated, obtain the optical parameters of the white light source to be evaluated, and construct a color discrimination test environment that includes the display terminal, observation position, and environmental adaptation conditions; Step 2: Present the preset color sample pairs on the display terminal and perform a light source color discrimination test, and record the observer's color discrimination test results and response time; Step 3: Synchronously acquire facial video of the observer, and extract blinking behavior data based on the facial video; Step 4: Collect visual evaluation scale results from observers before and after the color perception test; Step 5: Divide a single test process into multiple time periods according to a preset time window; Step 6: Calculate or obtain the task accuracy index, time cost index, blinking behavior index, scale evaluation index, and cognitive decision model parameters for each time period; Step 7: Calculate the dimensional scores of the four dimensions of color discrimination accuracy, color discrimination efficiency, blink physiological comfort, and visual comfort based on the indicators and parameters obtained in Step 6, according to directional consistency. Step 8: Calculate the Multidimensional Color Discrimination Score (MCDS) of the white light source to be evaluated based on the aforementioned dimensional scores; Step 9: Based on the changing trends of the MCDS and the dimensional scores of the four dimensions over different time periods, output the evaluation results of the color discrimination ability of the white light source to be evaluated in the short-term and long-term color discrimination tasks.

[0028] Optionally, in step 1, the optical parameters include one or more of spectral power distribution, illuminance, correlated color temperature, color rendering index, and chromaticity deviation; the color discrimination test environment includes the settings of display terminal brightness, display white point, viewing distance, field of view, and environmental adaptation time.

[0029] Optionally, in step 2, the color discrimination test is a color difference evaluation experiment, which collects the observer's judgment result on whether the color difference is perceptible and the reaction time; in step 2, the preset color sample pair includes at least one central color and multiple peripheral colors generated around the central color, the central color and the peripheral colors have a preset color difference in the uniform color space, and the color sample pair is presented randomly during the test.

[0030] Optionally, in step 3, the observer's facial video is acquired using a facial video acquisition device, which includes a camera, webcam, or image sensor, for acquiring a continuous sequence of images containing the observer's eye area. Facial video acquisition is performed with the observer's consent.

[0031] Optionally, in step 4, the visual evaluation scale includes one or more of the following: cognitive load scale, visual fatigue scale, and visual comfort scale.

[0032] Optionally, in step 5, dividing the test into multiple time periods according to a preset time window includes: dividing a single color discrimination test into multiple time periods according to a sliding time window, and calculating color discrimination evaluation indicators based on each time period; further, merging the multiple time periods into short-term and long-term stages according to the test time sequence. Specifically, a period of 20 minutes or less can be defined as short-term, and a period of more than 20 minutes as long-term.

[0033] Optionally, in step 6, the task accuracy indicator is the accuracy rate (Acc), which is calculated as follows:

[0034] in, N acc The number of times the observer's judgment matches the preset color difference judgment standard. N To effectively determine the total number of times, the preset color difference judgment standard is to calculate the color difference of color sample pairs in the CIECAM16-UCS uniform color appearance space. D E' ,when No. A difference greater than a preset threshold is considered a perceptible difference, while a difference less than or equal to the preset threshold is considered an imperceptible difference.

[0035] In step 6, the time cost index includes the response time RT, which is calculated based on the interval between two adjacent effective response timestamps and is statistically obtained separately in each time period.

[0036] In step 6, the acquisition of the blinking behavior index includes: extracting eye feature points from the observer's facial video, calculating the eye aspect ratio, and identifying blinking events based on local troughs in the aspect ratio time series.

[0037] In step 6, the blinking behavior indicators include blink rate (BR) and blink rate variability (BRV), which are calculated as follows:

[0038]

[0039] in,N blink The number of blinks detected within a time period. T The length of the time period. IBI The time interval between two consecutive blinks is denoted as BRV, which is the standard deviation of the IBI.

[0040] In step 6, the scale evaluation indicators include one or more of the following: mental demand index (MD), blurred vision index (BV), task hindrance index (FL), and task engagement intensity index (EL). These indicators are obtained by filling out a form, and the visual load or visual comfort caused by the white light source to be evaluated is obtained based on the evaluation results before and after the test. Preferably, the scale evaluation indicators adopt a graded rating scale, and the higher the score, the higher the degree of the corresponding scale evaluation indicator.

[0041] In step 6, the cognitive decision-making model includes a drift-diffusion model, which models the observer's color discrimination judgment process based on accuracy (Acc), reaction time (RT), and reaction time variance, and obtains the drift rate (v) and boundary distance. The non-decision time Ter; where v is used to characterize the efficiency of color information acquisition. Ter is used to characterize the degree of caution in decision-making, while Ter is used to characterize the physiological processing delay in the non-decision stage.

[0042] Optionally, in step 7, the color discrimination evaluation index is categorized into four functional dimensions, including color discrimination accuracy D1, color discrimination efficiency D2, blink physiological comfort D3, and visual comfort D4; wherein, color discrimination accuracy D1 is calculated from Acc and v, and color discrimination efficiency D2 is calculated from RT, ... According to Ter, the blink physiological comfort D3 is calculated from BR and BRV, and the visual comfort D4 is calculated from MD, BV, FL, and EL. First, each indicator is normalized and converted into a dimensionless score from 0 to 100. The calculation method is as follows:

[0043] Where S is the normalized index score, and x is the index value to be normalized. x min and x max These are the minimum and maximum values ​​of the indicator in the preset evaluation sample set, respectively.

[0044] Then, the normalized indicators are subjected to directional consistency processing, and the dimensional scores of the four functional dimensions are calculated. The directional consistency processing means ensuring that each dimensional score satisfies the following condition: the higher the score, the better the color discrimination performance in the corresponding dimension. In one embodiment, the dimensional scores of the four functional dimensions are calculated as follows:

[0045] Where D1, D2, D3, and D4 represent the dimensional scores for color discrimination accuracy, color discrimination efficiency, blink physiological comfort, and visual comfort, respectively; Acc, v, RT, Ter, BR, BRV, MD, BV, FL, and EL are all normalized index scores.

[0046] Optionally, in step 8, the Multidimensional Color Discrimination Score (MCDS) of the white light source to be evaluated is calculated based on the dimensional score. The calculation method is as follows:

[0047] Among them, D1, D2, D3 and D4 represent the dimensional scores of color discrimination accuracy, color discrimination efficiency, blink physiological comfort and visual comfort, respectively; the larger the MCDS, the better the multidimensional color discrimination ability of the white light source to be evaluated.

[0048] Optionally, in step 9, based on the changing trends of the MCDS and the dimensional scores of the four functional dimensions over different time periods, the color discrimination evaluation result of the white light source to be evaluated is output; the color discrimination evaluation result includes one or more of short-term color discrimination task applicability evaluation and long-term color discrimination task applicability evaluation. In step 9, the color discrimination evaluation result is divided into multiple levels according to a preset scoring threshold.

[0049] Examples 1 to 4 employ different illuminance and correlated color temperature conditions for the white light source under evaluation to verify the applicability of the method of the present invention in different typical indoor white light lighting scenarios. Specifically: Example 1: The white light source to be evaluated is a white light source with an illuminance of 150 lx and a correlated color temperature of 6000 K; Example 2: The white light source to be evaluated is a white light source with an illuminance of 800 lx and a correlated color temperature of 2500 K; Example 3: The white light source to be evaluated is a white light source with an illuminance of 800 lx and a correlated color temperature of 6000 K; Example 4: The white light source to be evaluated is a white light source with an illuminance of 2500 lx and a correlated color temperature of 6000 K.

[0050] The aforementioned white light source can be generated by a multi-channel LED panel light, or by other white light sources that can stably output target illuminance and related color temperature.

[0051] Under each white light source to be evaluated, observers underwent a 3-minute color adaptation period before the test. Subsequently, color sample pairs were randomly presented on the display terminal for a 40-minute color difference evaluation experiment. Observers reported their judgment on whether there was a perceptible difference between the two colors via an external keyboard, and the timestamp of each keystroke was recorded. The display terminal had its automatic brightness and automatic color temperature adjustment disabled, and maintained a fixed display brightness.

[0052] The color samples can include five color centers: gray, red, yellow, green, and blue. Each color center is surrounded by 80 peripheral colors, and the samples also include indistinguishable samples paired with the center color, forming 405 color sample pairs. For each color sample pair, its representation in the CIECAM16-UCS space is calculated. No. ,when No. A value greater than 1.0 is considered a perceptible difference sample. D E' Samples with a difference of less than or equal to 1.0 are considered imperceptible samples.

[0053] During facial video acquisition, observer facial videos are captured at a frame rate of 60 fps. During blink recognition, six feature points of the eyes are extracted from each frame, the time series of the eye aspect ratio is calculated, and blink events are identified based on local troughs. Blink rate (BR) and blink rate variability (BRV) are calculated.

[0054] Before and after the test, four items were collected: Mental Demand Index (MD), Blurred Vision Index (BV), Task Disruption Index (FL), and Task Engagement Intensity Index (EL). Each item was scored on a 7-point scale from low to high according to its severity.

[0055] The 40-minute test was divided into seven overlapping time windows: 0-10, 5-15, 10-20, 15-25, 20-30, 25-35, and 30-40 minutes. For each time window, accuracy (Acc), reaction time (RT), response time (BR), response time variance (BRV), mean squared error (MD), response time variance (BV), response time variance (FL), and response time variance (EL) were calculated. Based on the Acc, RT, and response time variance within that time window, the DDM parameter v was estimated. And Ter.

[0056] For each color discrimination evaluation index, the average index results of 28 sets formed by four examples and seven time windows were normalized to a score of 0 to 100. Acc and v were assigned to the color discrimination accuracy dimension D1, and RT, ... Ter was categorized into the color discrimination efficiency dimension D2, BR and BRV into the blink physiological comfort dimension D3, and MD, BV, FL, and EL into the visual comfort dimension D4. Furthermore, the results corresponding to the first three time windows were considered short-term color discrimination performance, and the results corresponding to the last four time windows were considered long-term color discrimination performance. The pre-test scale evaluation results were used to calculate the short-term visual comfort dimension D4, and the post-test scale evaluation results were used to calculate the long-term visual comfort dimension D4. Finally, the MCDS of each white light source in the short-term, long-term, and overall testing process were output, along with the results for the four dimensions D1, D2, D3, and D4, as shown in Tables 1 and 2.

[0057] Table 1. Multidimensional color discrimination scores of white light sources in the short-term phase in the four embodiments.

[0058] Table 2. Multidimensional color discrimination scores of white light sources in the four embodiments over a long period of time.

[0059] In one specific embodiment, the evaluation result of the white light source to be evaluated is output based on the MCDS. When MCDS ≥ 80, the evaluation is excellent; when 70 ≤ MCDS < 80, the evaluation is good; when 60 ≤ MCDS < 70, the evaluation is acceptable; and when MCDS < 60, the evaluation is poor. The above thresholds can be adjusted according to the application scenario, test task type, or sample data distribution.

[0060] As shown in Tables 1 and 2, the four embodiments exhibit significant dimensional differences. Embodiment 1 performs best in the D1 color discrimination accuracy dimension, while Embodiments 3 and 4 perform better in the D2 color discrimination efficiency dimension, the D3 blink physiological comfort dimension, and the D4 visual comfort dimension. Considering the overall MCDS results, Embodiment 4 performs best in short-term tasks, while Embodiment 3 performs best in long-term tasks, indicating that the white light source corresponding to Embodiment 3 has better temporal stability and is more suitable for long-term color discrimination tasks. These results demonstrate that the present invention can not only output a comprehensive color discrimination score but also distinguish the differences in accuracy, efficiency, blink physiological comfort, and visual comfort among different white light sources, providing a basis for lighting selection for short-term and long-term color discrimination tasks.

[0061] Example 5: Integrating the system of the present invention into an intelligent lighting controller. When the evaluation output module detects a long-term decrease in D2 color discrimination efficiency and a decrease in D3 blink physiological comfort, the controller can reduce illuminance or adjust the shading device; when D1 color discrimination accuracy is insufficient while D3 blink physiological comfort and D4 visual comfort are still at a high level, the controller can increase display contrast or adjust ambient white light parameters within a range that does not significantly reduce comfort, thereby achieving task-oriented adaptive lighting control.

[0062] This invention also provides a multi-dimensional quantification system for the color discrimination power of a white light source, comprising: The light source parameter acquisition module is used to acquire the spectral power distribution, illuminance, correlated color temperature, and color rendering parameters of the white light source to be evaluated. The color sample presentation module is used to generate and randomly present preset color sample pairs on the display terminal; The behavior response acquisition module is used to collect the color discrimination test results and response time of the observers; The facial video capture module is used to capture facial videos of the observers during the testing process; The blink recognition module is used to calculate the aspect ratio of the eyes based on eye feature points in facial videos, and to identify blinking events based on the time series of eye aspect ratios, and output blinking behavior data. The visual evaluation scale results acquisition module is used to collect the observer's evaluation results on mental demand, blurred vision, frustration level and effort level. The time segmentation module is used to segment test data according to a preset time window; The indicator and parameter acquisition module is used to calculate or obtain task accuracy indicators, time cost indicators, blinking behavior indicators, scale evaluation indicators, and cognitive decision model parameters for each time period. The multidimensional color discrimination rating calculation module is used to calculate the multidimensional color discrimination rating (MCDS) of the white light source to be evaluated based on the dimensional ratings of four dimensions. The evaluation output module is used to output the short-term color discrimination evaluation results, long-term color discrimination evaluation results, and lighting control suggestions of the white light source to be evaluated based on the changing trends of the MCDS and the dimensional scores of the four functional dimensions over different time periods.

[0063] The specific implementation methods of each module are the same as those of each step, and will not be described in this invention.

[0064] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A multidimensional quantification method for the color discrimination power of a white light source, characterized in that, include: Identify the white light source to be evaluated, obtain the optical parameters of the white light source to be evaluated, and construct a color discrimination test environment that includes a display terminal, observation position, and environmental adaptation conditions; Preset color sample pairs are displayed on the display terminal and a light source color discrimination test is performed. The results of the observer's color discrimination test and the response time are recorded. Simultaneously acquire facial video of the observer, and extract blinking behavior data based on the facial video; Visual evaluation scale results were collected from observers before and after the color discrimination test. A single test process is divided into multiple time periods according to a preset time window; Calculate or obtain the task accuracy index, time cost index, blinking behavior index, scale evaluation index, and cognitive decision model parameters for each time period. Based on the obtained indicators and parameters, the four dimensions of color recognition accuracy, color recognition efficiency, blink physiological comfort, and visual comfort are calculated according to directional consistency. The Multidimensional Color Discrimination Score (MCDS) of the white light source to be evaluated is calculated based on the dimensional scores of the four dimensions. Based on the changing trends of MCDS and the four-dimensional scores over different time periods, the color discrimination evaluation results of the white light source to be evaluated are output.

2. The multidimensional quantification method for the color discrimination power of a white light source according to claim 1, characterized in that: The optical parameters include one or more of the following: spectral power distribution, illuminance, correlated color temperature, color rendering index, and chromaticity deviation; the color discrimination test environment in step 1 includes the settings of display terminal brightness, display white point, observation distance, field of view, and environmental adaptation time.

3. The multidimensional quantification method for the color discrimination power of a white light source according to claim 1, characterized in that: The color discrimination test mentioned above is a color difference evaluation experiment, which collects the observer's judgment results on whether the color difference is perceptible and the reaction time; The preset color sample pair includes at least one central color and multiple peripheral colors generated around the central color. The central color and the peripheral colors have a preset color difference in a uniform color space, and the color sample pair is presented randomly during the test.

4. The multidimensional quantification method for the color discrimination power of a white light source according to claim 1, characterized in that: The visual evaluation scales mentioned include one or more of the following: cognitive load scale, visual fatigue scale, and visual comfort scale; The process of dividing a color discrimination test into multiple time periods according to a preset time window includes: dividing a single color discrimination test into multiple time periods according to a sliding time window, and merging the multiple time periods into a short-term stage and a long-term stage according to the test time sequence; We define short-term as less than or equal to K minutes, and long-term as greater than K minutes.

5. The multidimensional quantification method for the color discrimination power of a white light source according to claim 1, characterized in that: The accuracy metric for the task is Accuracy, which is calculated as follows: in, N acc The number of times the observer's judgment matches the preset color difference judgment standard. N To effectively determine the total number of times, the preset color difference judgment standard is to calculate the color difference of color sample pairs in the CIECAM16-UCS uniform color appearance space. ΔE' ,when Δ E' A difference greater than a preset threshold is considered a perceptible difference, while a difference less than or equal to the preset threshold is considered an imperceptible difference.

6. The multidimensional quantification method for the color discrimination power of a white light source according to claim 1, characterized in that: The time cost metric includes response time RT, which is calculated based on the interval between two adjacent effective response timestamps and is statistically obtained separately in each time period. The acquisition of the blinking behavior index includes: extracting eye feature points from the observer's facial video, calculating the eye aspect ratio, and identifying blinking events based on local troughs in the aspect ratio time series; The blinking behavior indicators include blink rate (BR) and blink rate variability (BRV).

7. The multidimensional quantification method for the color discrimination power of a white light source according to claim 1, characterized in that: The scale evaluation indicators include one or more of the following: mental demand index (MD), blurred vision index (BV), task hindrance index (FL), and task engagement intensity index (EL), which are obtained by filling out a form. The cognitive decision-making model includes a drift-diffusion model, which models the observer's color discrimination judgment process based on accuracy (Acc), reaction time (RT), and reaction time variance, and obtains the drift rate (v) and boundary distance. Non-decision time Ter; Where v is used to characterize the efficiency of color information acquisition. Ter is used to characterize the degree of caution in decision-making, while Ter is used to characterize the physiological processing delay in the non-decision stage.

8. The multidimensional quantification method for the color discrimination power of a white light source according to claim 1, characterized in that: First, the indicators and parameters are normalized, converting them into dimensionless scores ranging from 0 to 100. Then, the normalized indicators and parameters are aligned in direction, and dimensional scores for four dimensions are calculated. The higher the dimensional score, the better the color discrimination performance in the corresponding dimension. The formulas for calculating the dimensional scores are as follows: Where D1, D2, D3, and D4 represent the dimensional scores for color discrimination accuracy, color discrimination efficiency, blink physiological comfort, and visual comfort, respectively; Acc is the normalized accuracy, v is the normalized drift rate, and RT is the normalized reaction time. 1 is the normalized boundary distance, Ter is the normalized non-decision time, BR is the normalized blink rate, BRV is the normalized blink rate variability, MD is the normalized mental effort index, BV is the normalized blurred vision index, FL is the normalized task stalling index, and EL is the normalized task engagement intensity index.

9. A multidimensional quantification method for the color discrimination power of a white light source according to claim 1 or 8, characterized in that: The Multidimensional Color Discrimination Score (MCDS) of the white light source to be evaluated is calculated based on the dimensional scores of the four dimensions. The calculation method is as follows: Among them, D1, D2, D3 and D4 represent the dimensional scores of color discrimination accuracy, color discrimination efficiency, blink physiological comfort and visual comfort, respectively; the larger the MCDS, the better the multidimensional color discrimination ability of the white light source to be evaluated.

10. A multidimensional quantification system for the color discrimination power of a white light source, characterized in that, include: The light source parameter acquisition module is used to determine the white light source to be evaluated, acquire the optical parameters of the white light source to be evaluated, and construct a color discrimination test environment that includes a display terminal, observation position, and environmental adaptation conditions. The color sample presentation and behavior response acquisition module is used to present preset color sample pairs on the display terminal, perform light source color discrimination test, and collect the observer's color discrimination test results and response time. A facial video capture and blink recognition module is used to capture facial videos of the observer during the test and extract blink behavior data based on the facial videos. The visual evaluation scale results acquisition module is used to collect the visual evaluation scale results of observers before and after the color discrimination test; The time segmentation module is used to divide a test process into multiple time periods according to a preset time window; The indicator and parameter acquisition module is used to calculate or obtain task accuracy indicators, time cost indicators, blinking behavior indicators, scale evaluation indicators, and cognitive decision model parameters for each time period. The dimension scoring calculation module is used to calculate the dimension scores of four dimensions—color discrimination accuracy, color discrimination efficiency, blink physiological comfort, and visual comfort—based on the obtained indicators and parameters and according to directional consistency. The multidimensional color discrimination rating calculation module is used to calculate the multidimensional color discrimination rating (MCDS) of the white light source to be evaluated based on the dimensional ratings of four dimensions. The evaluation output module is used to output the color discrimination evaluation results of the white light source to be evaluated based on the changing trends of MCDS and the dimensional scores of the four dimensions over different time periods.

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