A neutral and preferred white point trajectory modeling method based on a laser display device

Abstract

The application provides a neutral and favorite white point trajectory modeling method based on a laser display device, constructs an ambient light driven human eye adaptive white point calculation model and a trajectory modeling system based on CCT-Duv and CIE 1976 u'v' space, dynamically solves a mixed adaptive white point and an adaptive factor, and combines nonlinear fitting and uniform space mapping to establish a precise chrominance reference of a wide color gamut laser display device. That is, the application can comprehensively consider the linear superposition effect of ambient light intensity and display device light radiation by constructing an ambient light driven human eye adaptive white point calculation model, dynamically solve a mixed adaptive white point and an adaptive factor, and solve the problem of inaccurate color reference anchoring under complex lighting conditions in the traditional method. On this basis, combined with the establishment of the CCT-Duv nonlinear trajectory and the CIE 1976 u'v' uniform space mapping model, the application can accurately define the target position of the neutral white and favorite white in the wide color gamut range, and ensure that the laser display device can obtain a chrominance reference meeting the human eye perception characteristics under different color temperatures and chrominance deviations.

Description

Technical Field

[0001] This invention belongs to the field of display technology and display device technology, and particularly relates to a method for modeling the trajectory of neutral and preferred white dots based on laser display devices. Background Technology

[0002] With the rapid development of display technology, the color gamut coverage of display devices has expanded from the sRGB standard to wide color gamut standards such as DCI-P3 and BT.2020. In display color management systems, the white point is a key parameter that determines the overall color tone of an image, representing the perception of color without color difference. It is an important reference for display calibration and characterization. Current technologies typically set D65, determined based on the sRGB space, as the standard white point. However, the perception of white is not constant but highly dependent on ambient lighting conditions and the spectral characteristics of the display device's own primary colors. In practical applications, if the white point cannot be adjusted according to changes in ambient light (such as color temperature and illuminance), the display is prone to perceptual color deviation in non-standard lighting environments. Furthermore, compared to traditional broadband displays, narrowband wide color gamut display devices have significantly different spectral power distributions, which can easily lead to color mismatch and metamerism, making the display white point determined based on D65 unsuitable for actual display.

[0003] To accurately reproduce the colors of display devices, the concept of "neutral and preferred white point trajectories" has been proposed. These trajectories represent the white point trajectory considered the most neutral or most personally pleasing under different color temperatures. However, existing research largely focuses on the "white area" of display devices and is limited to broadband devices and finite color temperature ranges. While the white point trajectory can be indirectly inferred from the geometric characteristics of the white area, the results are device-specific and lack universality for narrowband wide color gamut devices. Furthermore, precise descriptions of neutral and preferred white point trajectories under different correlated color temperatures are still scarce in the display field, contrasting sharply with the in-depth research in the lighting field. Therefore, constructing neutral and preferred white point trajectory models adapted to the spectral characteristics of laser narrowband wide color gamut display devices is crucial for optimizing their color performance.

[0004] Firstly, existing technologies primarily focus on the white perception characteristics of display devices. Their core approach lies in determining the "white range" under specific conditions through psychophysical experiments, rather than constructing a continuous neutral trajectory. The specific implementation process is as follows: First, a set of white point chromaticity coordinates with different correlated color temperatures (CCT) and different distances from the Planck trajectory (Duv) are selected and reproduced on the display device. Based on the scoring results of different white point chromaticity coordinate positions and the coordinate positions of the white points on the CIE 1976 u'v' chromaticity diagram, the observer's scoring results are statistically fitted using a bivariate Gaussian distribution formula. This allows for the precise definition of the perceived white area on the chromaticity diagram through a mathematical model.

[0005] The white region determined by this technical solution is typically elliptical in shape, with its major axis extending along the direction of CCT variation. In most cases, this major axis is observed to be approximately parallel to the Planck locus. It is worth noting that existing techniques, when calculating chromaticity coordinates and performing subsequent fitting analysis, are usually limited to using a single color matching function, such as the CIE 1931 2° color matching function, without considering or exploring the computational differences caused by using different CIE color matching functions (such as CIE 1964 10° or CIE 2006 2°). The white region determined by this method is mainly concentrated in a narrow color temperature range of CCT 6500K to 10000K and Duv -0.02 to 0.015, and can only approximate the neutral locus using the major axis of an ellipse within this local area, failing to provide information on the white point distribution under other color temperature conditions outside this range.

[0006] Secondly, the limitations and discreteness of existing technologies lie in the constraints of their research results, making it impossible to construct a continuous white point trajectory model that covers a wide color gamut. Because this method can only fit local elliptical regions based on limited experimental samples, the obtained "white range" is confined to a narrow range of CCT (Color Temperature Coordinates). For color temperature regions outside this range (such as common low color warm color temperature regions or regions exceeding a specific threshold), existing technologies cannot reveal the specific distribution patterns of their neutral white points. This results in a lack of referenceable full-color gamut neutral trajectory data for display devices when dealing with diverse ambient light or different user preferences, making it difficult to achieve precise dynamic adjustment of the white point.

[0007] Furthermore, existing technologies have significant shortcomings in data processing. That is, completely ignoring the differences color Matching function pairs The impact of chromaticity calculation and trajectory construction Because different CIE color matching functions differ in characterizing human color perception, especially on narrow-band wide-gamut display devices, this difference can lead to increased metamerism among observers. Existing technologies are limited to using a single color matching function for analysis, failing to explore the bias caused by this difference in neutral trajectory determination, resulting in models lacking universality and accuracy. This dual deficiency in trajectory continuity and function applicability prevents existing methods from establishing a precise functional relationship between color temperature (CCT) and chromaticity deviation (Duv), thus limiting the performance improvement of color management systems in reproducing a true neutral appearance and meeting user preferences. Summary of the Invention

[0008] To address the aforementioned issues, this invention provides a method for modeling the neutral and preferred white point trajectories based on laser display devices. This method not only constructs an ambient light-driven human eye-adaptive white point calculation system and a CCT-Duv nonlinear trajectory model to achieve accurate mapping between neutral and preferred white points across a wide color gamut, but also extends and performs color adaptation transformations based on dynamic adaptation factors and various CIE color matching functions, thereby achieving accurate reproduction of the white point trajectory under various observation conditions.

[0009] A method for modeling the trajectories of neutral and preferred white dots based on laser display devices includes the following steps:

[0010] S1: Acquire the source-adaptive white point LMS response driven by ambient light for human eye color adaptation. ; S2: Source-adaptive white point LMS response Construct CCT separately - Duv Spatial Models and CIE1976 u'v' Uniform spatial mapping model; S3: According to CIE 1976 u'v' The uniform spatial mapping model is used to obtain the LMS frustum response of the target white point. L target , M target , S target ] T Source-adaptive white point LMS response As the source adaptation field, the LMS frustum response of the target white point [ L target , M target , S target ] T As the target adaptation field for transformation, the pixel colors of the image to be transformed are converted from the source adaptation field to the target adaptation field, and then the resulting pixel tristimulus values ​​are... X 2, Y 2, Z 2] T Substitute the inverse GOG characteristic model of the laser display device into the model, calculate the corresponding RGB driving signal to drive the laser display device to emit light, thereby presenting an image that conforms to the target white point trajectory on the screen of the laser display device, and completing the modeling of neutral and preferred white point trajectories.

[0011] Furthermore, the source-adaptive white point LMS response used for human eye color adaptation transformation. The specific method for obtaining it is as follows: S11: Obtain the effective white point tristimulus value of ambient light reflected from the screen of the laser display device into the human eye.X view , Y view , Z view ] T as follows:

[0012] in,[ X disp , Y disp , Z disp ] T The actual tristimulus values ​​output by the laser display device, [ X amb , Y amb , Z amb ] T Let be the tristimulus values ​​of ambient light in the XYZ space. ρ The screen reflectivity of the laser display device; S12: [Effective white point tristimulation values] X view , Y view , Z view ] T Transformed to the human visual cone response space, the original hybrid adaptive white point response coordinates are obtained. L ori , M ori , S ori ] T as follows:

[0013] in, The transformation matrix is ​​CIECAT02; S13: Adapt the original blend to the white point response [ L ori , M ori , S ori ] T The source-adaptive white point LMS response is obtained by weighting the white point response of the standard reference light source D65 and applying it to the human eye's color adaptation transformation. as follows:

[0014] in, Based on ambient light intensity LuxCalculated fitness factor This is the white point response of the standard reference light source D65.

[0015] Furthermore, the actual tristimulus values ​​output by the laser display device [ X disp , Y disp , Z disp ] T The color matching function is calculated based on the target CIE color matching function, which is either CIE 1931 2°, CIE 1964 10°, CIE 2006 2°, CIE 2006 4°, or CIE 2006 10° color matching function.

[0016] Furthermore, based on source-adaptive white point LMS response Building CCT - Duv The spatial model method is as follows: S21: Using the CIECAT02 transformation matrix M cat02 inverse matrix M CAT02 -1 Will Switch back to the adaptive white point tristimulus values ​​[ X adp , Y adp , Z adp ] T as follows:

[0017] S22: According to [ X adp , Y adp , Z adp ] T Calculate the coordinates of the adapted white point in the CIE 1960 UCS color space. as follows:

[0018] S23: Set the target color temperature value (CCT) target Substituting into the color temperature-chromaticity mapping relationship, we obtain the corresponding target chromaticity value. Duv target At the same time, CCT target and Duv target Convert to coordinates in CIE 1960 UCS color space ( utarget , v target ); S24: Based on coordinate values ​​( u target , v target Obtain the corresponding CIE XYZ space tristimulus values. X target , Y target , Z target ] T as follows:

[0019] S25: Using the CIECAT02 transformation matrix Will[ X target , Y target , Z target ] T Transform to the human visual cone response space to obtain the target color temperature-chromaticity response coordinates. L target , M target , S target ] T This allows for the identification of a unique white point coordinate on the CIE 1960 UCS chromaticity diagram, thus completing the CCT - Duv Modeling of spatial models.

[0020] Furthermore, in step S23, the method for obtaining the color temperature-chromaticity mapping relationship is as follows: CCT color temperature of display devices disp Within the set range of 3500K to 12000K, sampling levels are selected at 500K intervals, including 3500K, 4000K, 4500K, 5000K, 5500K, 6000K, 6500K, 7000K, 7500K, 8000K, 8500K, 9000K, 9500K, 10000K, 10500K, 11000K, 11500K, and 12000K, for a total of 18 CCT levels; To display device color deviation Duv disp Nine DUV levels were selected: -0.025, -0.020, -0.015, -0.010, -0.005, 0, 0.005, 0.010, and 0.015. The color temperature-chromaticity mapping relationship is constructed as follows:

[0021] in, a , b , c , d These are the fitting parameters determined through an optimization algorithm; By combining 18 CCT levels and 9 Duv levels into different data pairs and substituting them into the aforementioned color temperature-chromaticity mapping relationship, the following results were obtained through fitting and calculation. a , b , c , d The specific values ​​are used to determine the color temperature-chromaticity mapping relationship.

[0022] Furthermore, based on source-adaptive white point LMS response Building CIE 1976 u'v' The method for the uniform spatial mapping model is as follows: The coordinates in the CIE 1960 UCS color space ( u , v Convert ) to the CIE 1976 color space ( u’ , v’ Coordinates, and thus complete CIE 1976 u'v' Construction of a uniform spatial mapping model.

[0023] Furthermore, CIE 1976 u'v' The mathematical expression for the uniform space mapping model is as follows:

[0024] in, e , f , g , h These are the model parameters obtained by optimizing the color difference between the fitted curve and the visual data points.

[0025] Further, in step S3, the LMS frustum response of the target white point [ L target , M target , S target ] T The method for obtaining it is as follows:

[0026] in,[ X target , Y target , Z target ] TTo adopt CCT - Duv Spatial model for target color temperature value CCT target and target chromaticity value Duv target The converted CIE XYZ space tristimulus values, M CAT02 This is the CIECAT02 transformation matrix.

[0027] Further, in step S3, the pixel tristimulus values ​​[ X 2, Y 2, Z 2] T The method for obtaining it is as follows: Read the RGB value of any pixel in the input image to be converted, and use the GOG model to convert the RGB value of that pixel into a linear tristimulus value. X 1, Y 1, Z 1] T ; Using the transformation matrix M CAT02 The linear tristimulus value [ X 1, Y 1, Z 1] T Converted to LMS view cone response space to obtain [ L 1, M 1, S 1] T :

[0028] Based on source adaptation field [ L w , M w , S w ] T and target adaptation field [ L target , M target , S target ] T Constructing the diagonal transformation matrix N cat02 :

[0029] Using diagonal transformation matrix N cat02 Calculate the adapted LMS response[ L 2, M 2, S 2] T :

[0030] The transformed LMS response [ L 2, M 2, S 2] T Through the inverse matrix M CAT02 -1 Convert back to CIE XYZ space to obtain the adapted pixel tristimulus values. X 2, Y 2, Z 2] T : .

[0031] Furthermore, a method for modeling the neutral and preferred white dot trajectories based on laser display devices also includes the following steps: S4: Extend modeling different CIE color matching functions (1) Coordinate transformation reference update: For each selected target, extend the CIE color matching function and update the spectrum to XYZ transformation matrix; where the extended CIE color matching function is CIE 1964 10°, CIE 2006 2°, CIE2006 4° or CIE 2006 10°; (2) Model parameter reconstruction: Keeping the CCT sampling points in the range of 3500K-12000K unchanged, recalculate the corresponding CIE 1960 UCS coordinates for the new color matching function. u , v ), and refit Duv Polynomial parameters and u’ v’ Mapping parameters; (3) Full process adaptation: In both ambient light calculation and CAT02 transformation, the currently selected extended CIE color matching function is used for calculation, and different model parameters are obtained by using different extended CIE color matching functions.

[0032] Beneficial effects: 1. This invention provides a method for modeling the neutral and preferred white point trajectories of laser display devices. It constructs an ambient light-driven human-eye-adapted white point calculation model and a trajectory modeling system based on CCT-Duv and CIE 1976 u'v' spaces. By dynamically calculating the mixed adaptive white point and adaptation factor, and combining nonlinear fitting and uniform space mapping, a precise colorimetric reference for wide-gamut laser display devices is established. In other words, by constructing an ambient light-driven human-eye-adapted white point calculation model, this invention comprehensively considers the linear superposition effect of ambient illuminance and display device light radiation, dynamically calculating the mixed adaptive white point and adaptation factor, thus solving the problem of inaccurate color reference anchoring under complex lighting conditions using traditional methods. Based on this, and by establishing the CCT-Duv nonlinear trajectory and CIE 1976 u'v' uniform space mapping model, this invention can accurately define the target positions of neutral and preferred white within a wide color gamut, ensuring that laser display devices can obtain a colorimetric reference that conforms to human eye perception characteristics under different color temperatures and colorimetric deviations.

[0033] 2. This invention provides a method for modeling the neutral and preferred white point trajectories of laser display devices. By introducing multiple CIE color matching functions for extended modeling and CIECAT02 color adaptation transformation, it can accurately adapt to different observer viewing angles and age characteristics, effectively covering application needs from standard observers to those with personalized spectral sensitivity. In other words, this invention achieves accurate conversion of theoretical chromaticity parameters to the device's RGB driving signals through adaptation to different viewing angles and age characteristics. This multi-model fusion and end-to-end chromaticity control scheme not only eliminates color reproduction deviations caused by individual differences and changes in ambient light, but also significantly improves the color consistency and visual experience quality of laser display devices under various observation scenarios, thereby meeting the color reproduction requirements under diverse observation conditions.

[0034] 3. This invention provides a method for modeling the neutral and preferred white point trajectories based on laser display devices. It not only provides a comprehensive adaptive state calculation method under the combined effect of ambient light and display light, but also establishes a CCT-Duv nonlinear trajectory model and a CIE 1976 u'v uniform space mapping model that cover the requirements of a wide color gamut. Finally, it also provides a calculation process for reconstructing model parameters based on different CIE color matching functions. Cross-standard compatibility is achieved by replacing the color matching function and repeating the modeling, which increases the feasibility of the entire modeling process. Attached Figure Description

[0035] Figure 1 A method and process for modeling trajectories of neutral and preferred white dots; Figure 2 For device reproduction process based on CIECAT02 color adaptation transformation; Figure 3 Five CIE color matching functions; Figure 4 For different CIE color matching functions, neutral and preferred white point trajectories based on CCT–Duv space are used. Figure 5 Neutral and preferred white point trajectories fitted to different CIE color matching functions based on the CIE 1976 u'v' space. Detailed Implementation

[0036] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0037] This invention provides a method for modeling and reproducing neutral and preferred white point trajectories based on laser display devices. This method aims to construct two mathematical models—neutral and preferred white point trajectories—in CC-Duv and CIE1976 u'v' spaces by anchoring the human eye's comprehensive adaptation to the white point under the combined effects of ambient and display light. Combined with CIECAT02 color adaptation transformation, it achieves accurate color reproduction for devices in suitable scenarios and extends multiple CIE color matching functions to complete model adaptation under all observation conditions, thus covering various standard observer models and application needs under different ambient light conditions.

[0038] like Figure 1 As shown, the method flow of this invention mainly includes the following four stages: S1. Ambient light-driven human eye-adaptive white point calculation; S2. Neutral and preferred white trajectory modeling based on CCT-Duv and CIE 1976 u'v' spaces; S3. Device reproduction based on CIECAT02 color adaptation transformation; S4. Extended modeling of different CIE color matching functions. The specific implementation steps of each stage are as follows: Step S1: Ambient light-driven human eye adaptation white point calculation This step is the full-process colorimetric reference anchoring stage, and its core task is to calculate the comprehensive adaptation state of the human eye under the combined effects of actual ambient light and display light. This step comprehensively covers the parameters from light source parameters (CCT, ... Duv The entire process of calculating the hybrid adaptive white point and dynamic adaptation factor D for color adaptation transformation, from chromaticity coordinates (XYZ) to visual cone response (LMS), provides scene-based reference parameters for subsequent chromaticity modeling and device reproduction.

[0039] 1.1 Definition of core parameters of ambient light and display devices First, the color temperature (CCT) of the ambient light system is collected. amb Color deviation Duv amb With Illuminance Luxamb Secondly, for display devices, considering the complex non-linear shift in the perception of neutral white versus preferred white between the display device and the human eye, which does not strictly follow the Planck trajectory, it is necessary to determine the color temperature (CCT) that the device needs to reproduce. disp Color deviation Duv disp Value, of which Duv This indicates the distance of the sample point from the Planckian Locus on the CIE 1960 UCS plot; positive values ​​indicate a green / yellow bias, and negative values ​​indicate a pink / red bias. The specific sampling range is set as follows: (1) Display device color temperature CCT disp The CCT range is set to cover the needs of wide color gamut display applications. In this embodiment, the preferred range is 3500K to 12000K, and 3500K, 4000K, 4500K, 5000K, 5500K, 6000K, 6500K, 7000K, 7500K, 8000K, 8500K, 9000K, 9500K, 10000K, 10500K, 11000K, 11500K, and 12000K are selected in 500K intervals, for a total of 18 CCT levels. (2) Color deviation of display device Duv disp Based on the human eye's perceptual characteristic that a preference for whiteness leans towards negative values, this embodiment selects a total of nine values: -0.025, -0.020, -0.015, -0.010, -0.005, 0, 0.005, 0.010, and 0.015. Duv grade.

[0040] 1.2 Based on CCT / Duv Calculation of XYZ tristimulus values For ambient light and device light, for two sets of given CCT and Duv Calculate the coordinates of each value on the CIE 1960 UCS diagram. u , v ), and then calculate the corresponding ambient light and display device ( x , y Chromaticity coordinates, and then the maximum brightness of the laser display device measured by the spectrometer. Y This allows us to obtain the XYZ values ​​corresponding to the ambient light and the display device. All parameter calculations are based on the target CIE color matching function (the CIE 1931 2° color matching function is used by default, laying the foundation for the multi-color matching function expansion in the subsequent S4 step). Considering that ambient light will be reflected from the screen into the human eye, it is assumed that the screen has a certain reflectivity. ρThe effective white point tristimulus value entering the human eye is defined as the linear superposition of the actual output of the device and the reflected ambient light: (1) in,[ X disp , Y disp , Z disp ] T The actual tristimulus values ​​output by the device, [ X amb , Y amb , Z amb ] T Let [the values ​​be] the tristimulus values ​​of ambient light in the XYZ space. X view , Y view , Z view ] T To perceive the white point (i.e., the effective white point tristimulus value entering the human eye).

[0041] 1.3 Conversion from XYZ to LMS view cone response space Using the CIECAT02 transformation matrix M cat02 The mixed white point XYZ calculated in step 1.2 is transformed into the human eye cone response space to obtain the original mixed adaptive white point response. L ori , M ori , S ori ] T : (2) Simultaneously, in order to construct a reference benchmark for color adaptation transformation, a standard reference light source (such as D65, used as the source benchmark for image acquisition) is converted into an LMS response. L D65 , M D65 , S D65 ] T .

[0042] 1.4 Final Adaptation of White Point LMS with Hybrid Weighted Calculation Firstly, this embodiment is based on ambient light intensity. Lux Calculate fitness factors D factor ( Dfactor (Values ​​range from 0 to 1), by constructing a dynamic lookup table. D factor = f ( Lux This characterizes the degree of incomplete adaptation of the human eye to ambient light. Lux amb The smaller (dark room) D factor = 1, Lux amb The larger, the better D factor It gradually decreases, tending towards 0 or a certain constant value. This is then combined with the adaptation factor. D factor The source-adapted white point LMS response for color adaptation is obtained by weighting the white point response of the standard reference light source (D65) with the actual mixed-adapted white point response. L w , M w , S w ] T : (3) The result obtained in S1 can then be used as the core input for subsequent S2 modeling and S3 transformation.

[0043] Step S2: Based on CCT - Duv With CIE 1976 u'v' Neutral and preferred white trajectory modeling of space This step aims to establish a complete mathematical description system, based on the results output by S1, and sequentially construct CCT- Duv Spatial Models and CIE 1976 u'v' Uniform Space Mapping Model. This mathematical model is used to define the precise positions of neutral white and preferred white in the color space, serving as the target reference for device reproduction. It covers the entire process from color temperature definition to chromaticity coordinate mapping, providing standard target chromaticity values ​​for the CIECAT02 transformation of S3.

[0044] 2.1 Model 1: CCT - Duv Neutral / Preference White Trajectory Modeling Based on the S1 adaptive white point benchmark, a CCT covering the wide color gamut requirements of laser displays is constructed. Duv A nonlinear trajectory model is used to define both neutral white and preferred white trajectories, enabling performance at any color temperature. Duv The value is calculated. The specific implementation steps are as follows: First, such as Figure 2As shown, the final adaptive white point frustum response calculated by S1 is [ L w , M w , S w ] T Using the CIECAT02 transformation matrix M cat02 inverse matrix M CAT02 -1 Switch back to the adaptive white point tristimulus values ​​[ X adp , Y adp , Z adp ] T : (4) Then, based on the CIE 1960 UCS color space, calculate the appropriate white point ( u adp , v adp )value.

[0045] (5) Then, by combining the Planck trajectory benchmark, the core anchor points for modeling are calculated. CCT adp , Duv adp Combining the preferred white point datasets corresponding to the 18 CCT levels and 9 Duv levels preset in step 1.1, a cubic polynomial fitting method is used to jointly determine the CCT and... Duv The general mapping relationship between them is expressed mathematically as follows: (6) Wherein, CCT is the correlated color temperature value (unit: K). Duv This is the colorimetric deviation value corresponding to this color temperature; a , b , c , d These are the fitting parameters determined through an optimization algorithm. Using this model, for any given target color temperature value (CCT)... target The corresponding model can be used to calculate Duv target The value is then combined with the transformation rules of S1 to convert the CCT value. target and Duv target Based on formula (5), convert to ( u target , vtarget The coordinates are then used to obtain the corresponding coordinates according to the following steps. X target , Y target , Z target ] T coordinate: (7) Finally, we obtain [the result] through formula (2) and matrix transformation. L target , M target , S target ] T The coordinates are used to lock a unique white point coordinate on the CIE 1960UCS chromaticity diagram, thus achieving chromaticity reference coverage across the entire color temperature range.

[0046] 2.2 Model 2: CIE 1976 u'v' Uniform spatial mapping model CCT - Duv The chromaticity coordinates of the model, the adaptive white point of S1, and the device's basic output white point coordinates are uniformly mapped to CIE 1976. u ' v’ A piecewise linear trajectory model is constructed using a uniform color space. This embodiment is based on CIE 1976. u'v' A piecewise linear model is constructed in space to describe the trajectory of the white point. First, the adapted white point of S1 and the full trajectory white point of Model 1 in 2.1 are compared using CIE 1960. UV Coordinates converted to CIE 1976 u'v' Coordinates, and then in CIE 1976 u'v' A mapping model is constructed in a uniform space, and its mathematical expression is as follows: (8) in, e , f , g , h The fitting parameters are model parameters obtained by minimizing the color difference between the fitted curve and the visual data points. This mathematical expression establishes the chromaticity coordinates. u’ and v’ The relationship between the two, the CCT obtained by this mathematical model target , Duv target , u'v' target This provides a standard target chromaticity value for subsequent color adaptation transformations.

[0047] Step S3: Device reproduction based on CIECAT02 color adaptation transformation Based on the scenario-based adaptation parameters determined by S1 ([ L w , M w , S w ] T The target white point chromaticity reference (CCT) determined by formulas (6) and (8) in D) and S2 target , Duv target The system performs CIECAT02 color adaptation transformation, combining the color characteristics of the laser display device to convert theoretical chromaticity parameters into RGB drive signals that the device can execute, thereby achieving accurate color reproduction.

[0048] 3.1 Target White Point LMS Response Calculation and Pixel Readout (1) The CCT calculated based on the S2 model target , Duv target Convert it to CIE XYZ tristimulus values. X target , Y target , Z target ] T Then, according to the CIECAT02 transformation matrix M CAT02 Calculate the LMS cone response of the target white point. L target , M target , S target ] T .

[0049] (9) Should[ L target , M target , S target ] T It will serve as the target reference for color adaptation transformation.

[0050] (2) Read the RGB value of any pixel in the input image, and use the GOG (Gamma-Offset-Gain) model to convert the RGB value of the pixel into a linear tristimulus value. X 1, Y 1, Z 1]T .

[0051] 3.2 Color Adaptation Transformation Based on CIECAT02 Mixed-adaptive white point calculated using S1 [ L w , M w , S w ] T As the source adaptation field for the transformation, the target white point is calculated in 3.1. L target , M target , S target ] T As the target adaptation field for the transformation, the pixel color is transformed from the mixed adaptation white point source adaptation field calculated with S1 to the target adaptation field using the CAT02 transformation matrix. The specific steps are as follows: (1) Based on the calculation in S2, use the transformation matrix according to the formula. M CAT02 The linear tristimulus value [ X 1, Y 1, Z 1] T Converted to LMS view cone response space to obtain [ L 1, M 1, S 1] T : (10) (2) Based on source adaptation field [ L w , M w , S w ] T and target adaptation field [ L target , M target , S target ] T Constructing the diagonal transformation matrix N cat02 : (11) (3) Using the above matrix N cat02 Calculate the adapted LMS response [ L 2, M 2, S 2]T : (12) (4) Transform the LMS response [ L 2, M 2, S 2] T Through the inverse matrix M CAT02 -1 Convert back to CIE XYZ space to obtain the adapted pixel tristimulus values. X 2, Y 2, Z 2] T : (13) (5) Transformed [ X 2, Y 2, Z 2] T Substituting the inverse GOG characteristic model of the laser display device, the corresponding RGB driving signal (digital level) is calculated to drive the laser to emit light, thereby presenting an image on the screen that conforms to the trajectory of the target white dot.

[0052] Step S4: Extended Modeling of Different CIE Color Matching Functions The spectral sensitivity of the human eye varies due to individual differences, field of view size, and age. To broaden the applicability of the white dot trajectory proposed in this invention, this step introduces various CIE color matching functions to extend the model.

[0053] 4.1 Calculation of different CIE color matching functions like Figure 3 As shown, this embodiment is not limited to the conventional CIE 1931 2° color matching function, but is further extended to the CIE 1964 10°, CIE 2006 2°, CIE 2006 4°, and CIE 2006 10° color matching functions. The CIE 2006 color matching function can more accurately reflect the spectral response under different ages and viewing fields.

[0054] This step uses the exact same algorithm flow as S1, S2, and S3, but the computational baseline has been replaced: (1) Coordinate transformation benchmark update: For each selected target CIE color matching function, update the spectrum to XYZ transformation matrix.

[0055] (2) Model parameter reconstruction: Keeping the CCT sampling points (3500K-12000K) in S2 unchanged, recalculate the corresponding CIE 1960 UCS coordinates for the new color matching function. u , v ), and refit Duv Polynomial parameters and u'v' Mapping parameters, such as Figure 4 As shown.

[0056] (3) Full process adaptation: In the ambient light calculation in step S1 and the CAT02 transformation in step S3, the currently selected color matching function is used for calculation. The model parameters obtained by using different CIE color matching functions are shown in Table 1.

[0057] Table 1. Fitting parameters of the two models calculated using different CIE color matching functions.

[0058] In summary, such as Figure 5 As shown, in practical applications, the device can automatically switch to the corresponding color matching function model based on the user-defined field of view, age information, or image content metadata, thereby ensuring accurate white point reproduction under different observation conditions.

[0059] Of course, the present invention may have other various embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and modifications according to the present invention, but these corresponding changes and modifications should all fall within the protection scope of the appended claims.

Claims

1. A method for modeling the trajectories of neutral and preferred white dots based on laser display devices, characterized in that, Includes the following steps: S1: Acquire the source-adaptive white point LMS response driven by ambient light for human eye color adaptation. ; S2: Source-adaptive white point LMS response Construct CCT separately - Duv Spatial Models and CIE 1976 u'v' Uniform spatial mapping model; S3: According to CIE 1976 u'v' The uniform spatial mapping model is used to obtain the LMS frustum response of the target white point. L target , M target , S target ] T ; Source-adaptive white point LMS response As the source adaptation field, the LMS frustum response of the target white point [ L target , M target , S target ] T As the target adaptation field for transformation, the pixel colors of the image to be transformed are converted from the source adaptation field to the target adaptation field, and then the resulting pixel tristimulus values ​​are... X 2, Y 2, Z 2] T Substitute the inverse GOG characteristic model of the laser display device into the model, calculate the corresponding RGB driving signal to drive the laser display device to emit light, thereby presenting an image that conforms to the target white point trajectory on the screen of the laser display device, and completing the modeling of neutral and preferred white point trajectories.

2. The method for modeling neutral and preferred white dot trajectories based on laser display devices as described in claim 1, characterized in that, Source-adaptive white point LMS response for human eye color adaptation The specific method for obtaining it is as follows: S11: Obtain the effective white point tristimulus value of ambient light reflected from the screen of the laser display device into the human eye. X view , Y view , Z view ] T as follows: in,[ X disp , Y disp , Z disp ] T The actual tristimulus values ​​output by the laser display device, [ X amb , Y amb , Z amb ] T Let be the tristimulus values ​​of ambient light in the XYZ space. ρ The screen reflectivity of the laser display device; S12: [Effective white point tristimulation values] X view , Y view , Z view ] T Transformed to the human visual cone response space, the original hybrid adaptive white point response coordinates are obtained. L ori , M ori , S ori ] T as follows: in, The transformation matrix is ​​CIECAT02; S13: Adapt the original blend to the white point response [ L ori , M ori , S ori ] T The source-adaptive white point LMS response is obtained by weighting the white point response of the standard reference light source D65 and applying it to the human eye's color adaptation transformation. as follows: in, Based on ambient light intensity Lux Calculated fitness factor This is the white point response of the standard reference light source D65.

3. The method for modeling neutral and preferred white dot trajectories based on laser display devices as described in claim 2, characterized in that, The actual tristimulus values ​​output by the laser display device [ X disp , Y disp , Z disp ] T The color matching function is calculated based on the target CIE color matching function, which is either CIE 1931 2°, CIE 1964 10°, CIE 2006 2°, CIE 2006 4°, or CIE 2006 10° color matching function.

4. The method for modeling neutral and preferred white dot trajectories based on laser display devices as described in claim 1, characterized in that, Source-adaptive white point LMS response Building CCT - Duv The spatial model method is as follows: S21: Using the CIECAT02 transformation matrix M cat02 inverse matrix M CAT02 -1 Will Switch back to the adaptive white point tristimulus values ​​[ X adp , Y adp , Z adp ] T as follows: S22: According to [ X adp , Y adp , Z adp ] T Calculate the coordinates of the adapted white point in the CIE 1960 UCS color space. as follows: S23: Set the target color temperature value (CCT) target Substituting into the color temperature-chromaticity mapping relationship, we obtain the corresponding target chromaticity value. Duv target At the same time, CCT target and Duv target Convert to coordinates in CIE 1960 UCS color space ( u target , v target ); S24: Based on coordinate values ​​( u target , v target Obtain the corresponding CIE XYZ space tristimulus values. X target , Y target , Z target ] T as follows: S25: Using the CIECAT02 transformation matrix Will[ X target , Y target , Z target ] T Transform to the human visual cone response space to obtain the target color temperature-chromaticity response coordinates. L target , M target , S target ] T This allows for the identification of a unique white point coordinate on the CIE 1960 UCS chromaticity diagram, thus completing the CCT - Duv Modeling of spatial models.

5. The method for modeling neutral and preferred white dot trajectories based on laser display devices as described in claim 4, characterized in that, In step S23, the method for obtaining the color temperature-chromaticity mapping relationship is as follows: CCT color temperature of display devices disp Within the set range of 3500K to 12000K, sampling levels are selected at 500K intervals, including 3500K, 4000K, 4500K, 5000K, 5500K, 6000K, 6500K, 7000K, 7500K, 8000K, 8500K, 9000K, 9500K, 10000K, 10500K, 11000K, 11500K, and 12000K, for a total of 18 CCT levels; To display device color deviation Duv disp Nine DUV levels were selected: -0.025, -0.020, -0.015, -0.010, -0.005, 0, 0.005, 0.010, and 0.

015. The color temperature-chromaticity mapping relationship is constructed as follows: in, a , b , c , d These are the fitting parameters determined through an optimization algorithm; By combining 18 CCT levels and 9 Duv levels into different data pairs and substituting them into the aforementioned color temperature-chromaticity mapping relationship, the following results were obtained through fitting and calculation. a , b , c , d The specific values ​​are used to determine the color temperature-chromaticity mapping relationship.

6. The method for modeling neutral and preferred white dot trajectories based on laser display devices as described in claim 1, characterized in that, Source-adaptive white point LMS response Building CIE 1976 u'v' The method for the uniform spatial mapping model is as follows: The coordinates in the CIE 1960 UCS color space ( u , v Convert ) to the CIE 1976 color space ( u’ , v’ Coordinates, and thus complete CIE 1976 u'v' Construction of a uniform spatial mapping model.

7. The method for modeling neutral and preferred white dot trajectories based on laser display devices as described in claim 6, characterized in that, CIE 1976 u'v' The mathematical expression for the uniform space mapping model is as follows: in, e , f , g , h These are the model parameters obtained by optimizing the color difference between the fitted curve and the visual data points.

8. The method for modeling neutral and preferred white dot trajectories based on laser display devices as described in claim 1, characterized in that, In step S3, the LMS frustum response of the target white point [ L target , M target , S target ] T The method for obtaining it is as follows: in,[ X target , Y target , Z target ] T To adopt CCT - Duv Spatial model for target color temperature value CCT target and target chromaticity value Duv target The converted CIE XYZ space tristimulus values, M CAT02 This is the CIECAT02 transformation matrix.

9. The method for modeling neutral and preferred white dot trajectories based on laser display devices as described in claim 1, characterized in that, In step S3, the pixel tristimulus values ​​[ X 2, Y 2, Z 2] T The method for obtaining it is as follows: Read the RGB value of any pixel in the input image to be converted, and use the GOG model to convert the RGB value of that pixel into a linear tristimulus value. X 1, Y 1, Z 1] T ; Using the transformation matrix M CAT02 The linear tristimulus value [ X 1, Y 1, Z 1] T Converted to LMS view cone response space to obtain [ L 1, M 1, S 1] T : Based on source adaptation field [ L w , M w , S w ] T and target adaptation field [ L target , M target , S target ] T Constructing the diagonal transformation matrix N cat02 : Using diagonal transformation matrix N cat02 Calculate the adapted LMS response[ L 2, M 2, S 2] T : The transformed LMS response [ L 2, M 2, S 2] T Through the inverse matrix M CAT02 -1 Convert back to CIE XYZ space to obtain the adapted pixel tristimulus values. X 2, Y 2, Z 2] T : 。 10. The method for modeling neutral and preferred white dot trajectories based on laser display devices as described in claim 5, characterized in that... It also includes the following steps: S4: Extend modeling different CIE color matching functions (1) Coordinate transformation reference update: For each selected target, extend the CIE color matching function and update the spectrum to XYZ transformation matrix; where the extended CIE color matching function is CIE 1964 10°, CIE 2006 2°, CIE 2006 4° or CIE 2006 10°; (2) Model parameter reconstruction: Keeping the CCT sampling points in the range of 3500K-12000K unchanged, recalculate the corresponding CIE 1960 UCS coordinates for the new color matching function. u , v ), and refit Duv Polynomial parameters and u'v' Mapping parameters; (3) Full process adaptation: In both ambient light calculation and CAT02 transformation, the currently selected extended CIE color matching function is used for calculation, and different model parameters are obtained by using different extended CIE color matching functions.

Images