A method and apparatus for measuring chrominance

Abstract

The application provides a colorimetric measurement method and device, and belongs to the technical field of industrial detection. The method comprises the following steps: establishing an evaluation function based on a spectrum estimation model to represent the total difference between the measured response value and the predicted response value of the multi-channel of the three primary color light sources of a display device; wherein the spectrum estimation model is a pre-established mathematical model for spectrum curve estimation with a spectrum parameter vector as a variable; taking minimizing the evaluation function as a target, an optimization algorithm is used to solve the target spectrum parameter vector of the spectrum estimation mathematical model to obtain the spectrum estimation curve of the display device; and the three stimulus values and the colorimetric value of the display device are calculated according to the spectrum estimation curve and a color matching function. The colorimetric measurement method and device provided by the application can meet the colorimetric measurement accuracy requirement of the display screen, and the cost is lower than that of various existing schemes with the same accuracy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of industrial detection, in particular to a chroma measurement method and device. BACKGROUND

[0002] Under the prior art, there are three main methods for chroma measurement: the first method is to obtain the same spectral response curve as the color matching function of the CIE1931 standard chroma system by coating the lens of the photoelectric detector; the second method is to use a general RGB color camera to measure the light to be measured, and then linearly calibrate the measurement results to obtain CIE-XYZ response values; and the third method is to measure the spectrum of the light source, and integrate the measured spectrum waveform with the color matching function of the CIE1931 standard chroma system to obtain the simulation response value.

[0003] The first method has high cost and effective precision. Because the color matching function of the CIE1931 standard chroma system is three curves obtained based on human eye visual psychology, there is no existing material spectrum transmittance that conforms to the curves, so a complex multi-coating process is generally required, which brings high cost. However, there is still some difference between the artificially prepared lens spectrum transmittance and the ideal color matching function of the CIE1931 standard chroma system. This difference leads to a certain error in bright chroma measurement, and the current display screen bright chroma measurement has a high precision requirement for bright chroma, generally with a relative error of less than ±3% for brightness Y, and an error of less than ±0.003 for color coordinates x and y. The second method has lower cost, but its precision depends on whether the measured light source spectrum is a linear combination of the calibration spectrum, otherwise the error is large. Although the method of calibrating a plurality of light sources in advance to obtain different calibration coefficients can improve the robustness of the device to changes in the light source spectrum, the precision is difficult to further improve. Under this scheme, to obtain the highest precision, the calibration coefficient under the calibration light source similar to the measured spectrum is usually selected, but it is difficult to make an accurate judgment based on the RGB three-channel data alone. The third method has high precision, high cost, and large device size.

[0004] In summary, the existing technical solutions cannot effectively balance the precision and cost of chroma measurement, and therefore a new technical solution is needed for chroma measurement. SUMMARY

[0005] The present application provides a chroma measurement method and device to solve the problem of difficulty in effectively balancing the precision and cost of bright chroma measurement in the prior art, and realizes low-cost and high-precision chroma measurement.

[0006] In a first aspect, the present application provides a colorimetric measurement method, comprising: establishing an evaluation function based on a spectral estimation model to represent the total difference between the measured response values and the predicted response values of the multi-channel of the three primary color light sources of a display device; wherein the spectral estimation model is a pre-established mathematical model for spectral curve estimation with a spectral parameter vector as a variable; solving the target spectral parameter vector of the spectral estimation mathematical model by using an optimization algorithm to minimize the evaluation function, so as to obtain the spectral estimation curve of the display device; and obtaining the tristimulus values and the colorimetric values of the display device according to the spectral estimation curve and a color matching function.

[0007] According to the colorimetric measurement method provided by the present application, the evaluation function is established based on the spectral estimation model, comprising: measuring the spectral response curve of the multi-channel photoelectric sensor by using a preset optical device; measuring the three primary color light sources of the display device by using the multi-channel photoelectric sensor to obtain the measured response values of each channel; and establishing the evaluation function according to the spectral response curve of each channel, the measured response values of each channel and the spectral estimation model.

[0008] According to the colorimetric measurement method provided by the present application, the total difference is the weighted root mean square error of the deviation between the measured response values and the predicted response values of the multi-channel; and the predicted response value of each channel is determined according to the spectral estimation model and the spectral response curve of each channel.

[0009] According to the colorimetric measurement method provided by the present application, the spectral estimation model is established based on the Gaussian-Lorentz model; and the spectral parameter vector comprises a plurality of spectral parameters for describing the single-peak spectrum of the RGB sub-pixel.

[0010] According to the colorimetric measurement method provided by the present application, the target spectral parameter vector of the spectral estimation mathematical model is solved by using the optimization algorithm to minimize the evaluation function, comprising: setting the upper limit and the lower limit of the target spectral parameter vector; and solving the target spectral parameter vector of the spectral estimation mathematical model by using the gradient descent algorithm to minimize the evaluation function within the upper limit and the lower limit.

[0011] According to the colorimetric measurement method provided by the present application, after the target spectral parameter vector of the spectral estimation mathematical model is solved by using the optimization algorithm to minimize the evaluation function, the method further comprises: comparing the value of the current evaluation function with the preset threshold value, and replacing the initial value of the evaluation function in the case that the value of the current evaluation function is greater than the preset threshold value; obtaining a new target spectral parameter vector by using the optimization algorithm again based on the replaced initial value; and taking the new target spectral parameter vector as the final target spectral parameter vector in the case that the value of the evaluation function corresponding to the new target spectral parameter vector is less than or equal to the preset threshold value.

[0012] According to the colorimetric measurement method provided by the application, after the optimization algorithm is used to solve the target spectrum parameter vector of the spectrum estimation mathematical model with the minimum evaluation function as the target, the method further comprises: comparing the value of the current evaluation function with the preset threshold, and correcting the spectrum estimation model in the case that the value of the current evaluation function is greater than the preset threshold; and re-establishing a new evaluation function based on the corrected spectrum estimation model to solve the new target spectrum parameter vector.

[0013] According to the colorimetric measurement method provided by the application, the specific expression of the evaluation function is as follows:

[0014]

[0015] Wherein, k i is the weight coefficient of each channel, X represents the parameter vector, r i is the measured response value of the channel with the serial number i, f(X, λ) represents the spectrum estimation model, R i (λ) represents the spectrum response curve of the channel with the serial number i, and λ represents the wavelength.

[0016] According to the colorimetric measurement method provided by the application, in the case that the similarity between part of the spectrum response curves of the multiple channels and the color matching function is greater than a preset threshold, the weight coefficient corresponding to the part of the channels is increased.

[0017] In a second aspect, the application further provides a colorimetric measurement device, comprising:

[0018] A first processing module is configured to establish an evaluation function based on a spectrum estimation model, so as to represent the total difference between the measured response value and the predicted response value of the multiple channels of the three-primary-color light source of the display device; wherein the spectrum estimation model is a pre-established mathematical model for spectrum curve estimation with a spectrum parameter vector as a variable;

[0019] A second processing module is configured to solve the target spectrum parameter vector of the spectrum estimation mathematical model by using an optimization algorithm with the minimum evaluation function as the target, so as to obtain the spectrum estimation curve of the display device.

[0020] A third processing module is configured to obtain the three-stimulus value and the colorimetric value of the display device according to the spectrum estimation curve and the color matching function.

[0021] In a third aspect, the application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor implements the steps of the colorimetric measurement method according to any one of the above aspects when executing the program.

[0022] In a fourth aspect, the present application further provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of any of the above colorimetric measurement methods.

[0023] The colorimetric measurement method and device provided by the present application utilize a multi-channel photoelectric sensor to estimate the spectral curve of a display screen and measure the bright colorimetry, fully utilize existing equipment and technology, and have a low cost and the following advantages:

[0024] (1) The present application can estimate the spectrum of a display screen, and the estimation result can accurately extract the main characteristics of the peak wavelength and half-height width of the spectrum of the display screen.

[0025] (2) The present application can measure the bright colorimetry with high precision based on the result of the spectrum estimation, and the measurement error meets the general existing requirements for the measurement precision of the bright colorimetry of a display screen.

[0026] (3) The present application can directly use the equipment to measure the bright colorimetry based on the spectrum estimation without calibration and parameter configuration, and does not need to consider the spectrum offset of the screen body and other problems, and is simple and easy to use.

[0027] (4) The present application can also be compatible with other existing methods for colorimetric measurement. BRIEF DESCRIPTION OF DRAWINGS

[0028] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0029] Figure 1 is a flowchart of the colorimetric measurement method provided by the present application;

[0030] Figure 2 is a schematic diagram of the fitting result of the RGB primary color of the OLED screen using the spectrum estimation model provided by the present application;

[0031] Figure 3 is a structural schematic diagram of the colorimetric measurement device provided by the present application;

[0032] Figure 4 is a structural schematic diagram of the electronic device provided by the present application. DETAILED DESCRIPTION

[0033] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below in conjunction with the drawings in the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0034] It should be noted that, in the description of the embodiments of the present application, the terms "comprise", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitation, the element defined by the sentence "comprises a" does not exclude the presence of another identical element in the process, method, article or device comprising the element.

[0035] The terms "first", "second", and the like in the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of a kind.

[0036] In order to more clearly illustrate the technical solutions of the present application, before introducing the specific technical solutions of the present application, first, the CIE1931 standard colorimetric system and the basic principles of colorimetric measurement followed by the inventor when designing the technical solutions of the present application will be briefly described.

[0037] The demand for colorimetric measurement of objects in the industrial field is very extensive, such as the measurement of intensity and color temperature of light sources, the measurement of display screen brightness and color gamut, and the colorimetric measurement of paint spraying surface.

[0038] The most widely recognized representation of brightness and colorimetric is the 2° field of view CIE-XYZ tristimulus value method. Since this method was formed in 1931, it is simply referred to as CIE1931 XYZ. This method represents any light source in the visible light band with three XYZ values, which are the response values representing the response curves of the three ideal spectral responses (color matching functions of CIE1931 XYZ standard colorimetric system) of the spectrum. Among them, the Y of the tristimulus value represents the brightness, and based on the XYZ tristimulus value, the colorimetric coordinate (x, y) can also be obtained. The color matching function of CIE1931 XYZ standard colorimetric system has accurate values, and the data table with 1nm precision can be obtained in CIE official website and some professional books.

[0039] The light emitting element of the display screen is RGB three primary color lamp beads, and the RGB three primary color sub-pixel spectrum waveforms of different pixel positions are similar for the same screen, but there is a certain deviation, and the general deviation is in the order of 1-10nm. Although the gray scales of the screen body are different in actual application, the final spectrum is obtained by linear weighting of the RGB three primary color spectra in a certain proportion (determined by the RGB gray scale value and the gamma value), so the brightness and colorimetric measurement of the display screen can be simplified to the brightness and colorimetric measurement of the RGB three primary colors.

[0040] The common display screen types at the present stage are LCD, OLED and LED, and the three primary color spectra of different types are slightly different, but the waveforms are similar in general, and the peak wavelength and half-width range are also similar. Based on the general characteristics of the RGB three primary color spectrum of the display screen, the following will be combined with the Figures 1-4 The colorimetric measurement method and device provided by the embodiment of the application are described.

[0041] Figure 1 The flowchart of the colorimetric measurement method provided by the application is shown in FIG. 1, which includes but is not limited to the following steps: Figure 1

[0042] Step 101: An evaluation function is established based on a spectrum estimation model to represent the total difference between the measured response value and the predicted response value of the multi-channel three primary color light source of the display device.

[0043] The spectrum estimation model is a pre-established mathematical model for spectrum curve estimation with a spectrum parameter vector as a variable.

[0044] Step 102: The target spectrum parameter vector of the spectrum estimation mathematical model is solved by using an optimization algorithm to minimize the evaluation function, so as to obtain the spectrum estimation curve of the display device.

[0045] Step 103: The XYZ three-stimulus value and the colorimetric value of the display device are obtained according to the spectrum estimation curve and the color matching function of the CIE1931 standard colorimetric system.

[0046] The technical scheme of the application estimates and measures the spectrum curve of the display screen by using a multi-channel photoelectric sensor, fully utilizes the existing equipment and technology, and the measurement accuracy can meet the display screen brightness and colorimetric measurement accuracy requirements, and the cost is lower than that of various existing schemes with the same accuracy.

[0047] ​Based on the content of the above embodiments, as an optional embodiment, the present application provides a colorimetric measurement method, which establishes an evaluation function based on a spectrum estimation model, and includes: measuring a spectrum response curve of a multi-channel photoelectric sensor by using a preset optical device; measuring a three-primary color light source of the display device by using the multi-channel photoelectric sensor to obtain measured response values of each channel; and establishing the evaluation function according to the spectrum response curve of each channel, the measured response values of each channel, and the spectrum estimation model.

[0048] It can be understood that the multi-channel photoelectric sensor refers to an output value that can output a plurality of spectral responses to the same measured light source, and the implementation is various, and a typical implementation is to add different filter films to a plurality of single-point photoelectric detectors. The photoelectric detector can be a CCD or a CMOS, or other mainstream detectors. The filter film can be a film-coated filter, a prism spectrometer filter, a grating spectrometer filter, or other ways. The spectrum response curve of each channel can be directly measured by a monochromator.

[0049] Specifically, the spectrum response curve of the multi-channel sensor is the inherent characteristic of each channel of the multi-channel sensor, which generally needs to be tested only once and remains unchanged thereafter (as a fixed parameter stored in the device ROM), and generally needs to be completed by using a third-party device (i.e., a preset optical device), such as a monochromator. The horizontal axis of the spectrum response curve is the wavelength, generally 380nm-780nm, with an interval of 1nm, and the vertical axis is the response rate (sensitivity) corresponding to the wavelength.

[0050] Specifically, the spectrum response curve of each channel of the multi-channel sensor can be marked as R i (λ), wherein i is the serial number of the channel, and the typical number of channels (N) is not less than 12; for measuring the three-primary color light source of the measured display screen, the multi-channel response value is r i , wherein i is the serial number of the channel, i=1, 2, 3…, N-1, N; further, the evaluation function is established according to the spectrum response curve of each channel, the measured response value of each channel, and the spectrum estimation model.

[0051] Optionally, the present application can also compensate the multi-channel response value r i , and the compensation methods can include multiple value averaging, response non-linear compensation, temperature drift compensation, dark noise compensation, etc.

[0052] Optionally, the total difference degree is a weighted root mean square error (RMSE) of the deviation between the measured response value and the predicted response value of the multi-channel, and the predicted response value of each channel is determined according to the spectrum estimation model and the spectrum response curve of each channel, and the evaluation function can be expressed as:

[0053]

[0054] where L(X) is the evaluation function, k i are the weight coefficients for each channel, X is the parameter vector, r i is the measured response value for channel number i, f(X, λ) is the spectral estimation model, R i (λ) is the spectral response curve for channel number i, λ is the wavelength; the integral can be simplified to a sum, and the typical value of the wavelength step dλ is 1 nm.

[0055] where the weight coefficient k i is used to compensate for the difference in the order of magnitude of the response values of each channel, and the typical value is 1 / (r i *sum(R i )).

[0056] As an optional embodiment, the present application provides a colorimetric measurement method, and the spectral estimation model is established based on a Gaussian-Lorentz model; and the spectral parameter vector includes a plurality of spectral parameters for describing the single-peak spectrum of the RGB sub-pixel.

[0057] Specifically, the present application proposes a bilateral-Gaussian-Lorentz model (BLG, Bilateral Gaussian-Lorentz) for describing the single-peak spectrum of the RGB sub-pixel based on the Gaussian-Lorentz model, that is, the spectral estimation model, and the specific formula is:

[0058]

[0059] In the formula, H is a unit step function, generally H(x) = 0.5*(1+sign(x)), X is a vector model parameter with a length of 6 (X = [x1, x2, x3, x4, x5, x6]), λ is the wavelength, and ln is the logarithm with e as the base.

[0060] Among the six parameters of X, x1 describes the peak wavelength of the spectrum, x2 and x4 respectively describe the half-widths on the short-wave and long-wave sides (equal to or slightly greater than FWHM / 2, generally a maximum of 1.062 times FWHM / 2), x3 and x5 are respectively the Gaussian-Lorentz mixing ratios on the short-wave and long-wave sides (the value is 0-1, the greater the value, the closer to the Gaussian distribution, that is, the smaller the peak foot width; the greater the value, the closer to the Lorentz distribution, that is, the greater the peak foot width), and x6 represents the peak intensity.

[0061] FWHM stands for Full Width Half-Maximum, that is, half-maximum full width, and is abbreviated as half-height width.

[0062] For example, using the OLED spectral estimation model as an example, the model is fitted using the above formula, and a typical fitting result is as followsFigure 2 Figure 2 is a fitting result diagram of RGB primary colors of an OLED screen provided by the present application using a spectrum estimation model. According to Figure 2 a, b and c in the table 1, it can be seen that the fitting line (black solid line) and the original spectrum curve (black circle point) almost completely coincide, and the corresponding fitting parameters are shown in the table 1.

[0063] Table 1: Typical parameters of spectrum estimation model fitting

[0064] Parameters x1 x2 x3 x4 x5 x6 R 622.93 15.75 0.00 23.14 0.84 2.07 G 535.03 15.56 0.05 12.92 0.94 1.60 B 464.68 8.69 0.61 7.62 0.98 2.47

[0065] Based on the content of the above embodiment, as an optional embodiment, the present application provides a colorimetric measurement method, which takes minimizing the evaluation function as the goal, and uses an optimization algorithm to solve the target spectrum parameter vector of the spectrum estimation mathematical model, including: setting the upper limit and the lower limit of the target spectrum parameter vector; and taking minimizing the evaluation function as the goal, and using a gradient descent algorithm to solve the target spectrum parameter vector of the spectrum estimation mathematical model within the upper limit and the lower limit.

[0066] Specifically, the purpose of the present application is to calculate the X vector (target spectrum parameter vector) to minimize the above evaluation function, and the solving problem belongs to the optimization problem in mathematics, and has many mature solving methods, such as the gradient descent method. In order to avoid the occurrence of abnormal values and accelerate the parameter retrieval speed, the range of the X vector is limited. The lower limit is [420, 4, 0.001, 4, 0.001, 0.01], and the upper limit is [660, 30, 1, 50, 1, 100].

[0067] Further, the present application uses the calculated X vector to calculate the estimated target spectrum waveform by substituting the spectrum estimation model, and integrates the estimated spectrum waveform with the color matching function of the CIE1931 XYZ standard colorimetric system in the range of 380nm to 780nm to obtain the estimated value of XYZ three stimulus values.

[0068] The color coordinates x=X / (X+Y+Z), y=Y / (X+Y+Z) are calculated from the XYZ three stimulus values, that is, the bright color index of the display screen can be obtained.

[0069] ​As an optional embodiment based on the content of the above embodiment, the chroma measurement method provided by the present application further comprises, after solving the target spectrum parameter vector of the spectrum estimation mathematical model by using the optimization algorithm with the objective of minimizing the evaluation function: comparing the value of the current evaluation function with the size of the preset threshold, and replacing the initial value of the evaluation function in the case that the value of the current evaluation function is greater than the preset threshold; and solving a new target spectrum parameter vector by using the optimization algorithm based on the replaced initial value; and taking the new target spectrum parameter vector as the final target spectrum parameter vector in the case that the value of the evaluation function corresponding to the new target spectrum parameter vector is less than or equal to the preset threshold.

[0070] Specifically, the minimum value Lmin of the evaluation function can be evaluated, and typically, the minimum value Lmin is compared with a preset threshold, for example, the preset threshold is 0.01, and if Lmin is greater than the preset threshold, it indicates that the fitting result is not good enough, and the initial value of the evaluation function can be replaced to perform optimization calculation again to calculate a new target spectrum parameter vector, and the process is repeated until the value of the evaluation function is less than or equal to the preset threshold.

[0071] As an optional embodiment based on the content of the above embodiment, the chroma measurement method provided by the present application further comprises, after solving the target spectrum parameter vector of the spectrum estimation mathematical model by using the optimization algorithm with the objective of minimizing the evaluation function: comparing the value of the current evaluation function with the size of the preset threshold, and replacing the initial value of the evaluation function in the case that the value of the current evaluation function is greater than the preset threshold; and solving a new target spectrum parameter vector by using the optimization algorithm based on the replaced initial value; and taking the new target spectrum parameter vector as the final target spectrum parameter vector in the case that the value of the evaluation function corresponding to the new target spectrum parameter vector is less than or equal to the preset threshold.

[0072] Specifically, two Gaussian single-peak correction terms are added to the spectrum estimation model to correct the spectrum estimation model, and the two Gaussian single-peak correction terms are and The expression of the corrected spectrum estimation model is specifically:

[0073]

[0074] wherein X ext represents a vector with a length of 12, the first 6 terms of which are the same as X, and the other 6 terms are newly added parameters x7, x8, x9, x 10 , x 11 and x 12 .

[0075] The corrected spectrum estimation model is used to replace the original spectrum estimation model to generate a new evaluation function, and the target spectrum parameter vector is solved again, so that the spectrum estimation curve can be generated by using the spectrum estimation mathematical model, and the bright chroma can be calculated by combining the color matching function.

[0076] Generally, the display screen three primary color picture of the corrected model has a certain degree of crosstalk, for example, the G picture has RB spectrum mixed in.

[0077] Based on the content of the above embodiment, as an optional embodiment, the chroma measurement method provided by the application further comprises: in the case that the similarity between part of the channels in the multi-channel spectral response curve and the color matching function of the CIE1931 standard chroma system is greater than a preset threshold, increasing the weight coefficient corresponding to the part of the channels.

[0078] Specifically, part of the channels in the multi-channel response curve has relatively high similarity with the color matching function of the CIE1931 standard chroma system, and such a multi-channel device is beneficial to obtain higher spectral estimation and brightness chroma measurement accuracy. Therefore, the evaluation function (in the weight coefficient corresponding to the above channel with higher matching degree k i ) is increased, which is also beneficial to obtain higher accuracy, and the typical operation is 10 times of the original weight coefficient.

[0079] It should be noted that the application can convert the spectral response curve and the color matching function into vectors, and the similarity between the vectors is used to calculate the similarity between the spectral response curve and the color matching function. The calculation of the similarity belongs to the prior art, and will not be described in detail here.

[0080] Further, the application also provides a method for measuring brightness chroma in a calibrated working mode. The general process of calibration is as follows: using a multi-channel sensor to measure the response matrix r of the target under a plurality of spectral waveforms, and using a standard instrument to measure the true value matrix V of the three stimulus values; according to the response matrix and the true value matrix of the three stimulus values, a calibration matrix is calculated; according to the calibration matrix and the measured response value of each channel, the three stimulus values are estimated to calculate the chroma.

[0081] Wherein, the response matrix r is an M row N column matrix, M represents the number of light sources, and N is the number of channels. The true value matrix V of the three stimulus values is an M row 3 column matrix, and the vectors of each column are V1, V2 and V3 in turn, which correspond to CIE-XYZ three stimulus values in turn.

[0082] The application can use the least square method to calculate the calibration matrix as follows:

[0083]

[0084] Wherein, C is a 3 row N column matrix.

[0085] Further, according to the calibration matrix and the measured response value of each channel, the three stimulus values are estimated; the estimated value calculation formula is as follows:

[0086]

[0087] In the formula, r1~r N The response values of the multiple channels are sequentially obtained.

[0088] In other embodiments, the three-stimulus value true value matrix V in the above method can be obtained by the method and device of the present application in a mode based on spectral estimation without a third-party standard instrument.

[0089] In summary, the chroma measurement method provided by the present application uses a multi-channel photoelectric sensor to estimate the spectral curve of the display screen and measure the bright chroma, fully utilizes existing equipment and technology, and has the following advantages:

[0090] (1) The present application can realize spectral estimation of the display screen, and the estimation result can accurately extract the main characteristics such as the peak wavelength and half-height width of the display screen spectrum;

[0091] (2) The present application can measure the bright chroma with high precision based on the result of spectral estimation, and the measurement error meets the general existing requirements for the measurement precision of the bright chroma of the display screen;

[0092] (3) The present application can directly use the equipment to measure the bright chroma based on spectral estimation without calibration and parameter configuration, and does not need to consider the spectral shift of the screen body, which is simple and easy to use;

[0093] (4) The present application can also be compatible with other existing methods for chroma measurement.

[0094] Figure 3 is a structural schematic diagram of the chroma measurement device provided by the present application, as Figure 3 shown, the device comprises a first processing module 301, a second processing module 302 and a third processing module 303.

[0095] The first processing module 301 is used to establish an evaluation function based on a spectral estimation model to represent the total difference between the measured response value and the predicted response value of the multi-channel of the display device three-primary-color light source; wherein the spectral estimation model is a pre-established mathematical model for spectral curve estimation with a spectral parameter vector as a variable;

[0096] The second processing module 302 is used to minimize the evaluation function as the target, and solve the target spectral parameter vector of the spectral estimation mathematical model by using an optimization algorithm to obtain the spectral estimation curve of the display device;

[0097] The third processing module 303 is used to calculate the XYZ three-stimulus value and chroma of the display device according to the spectral estimation curve combined with the color matching function of the CIE1931 standard chroma system.

[0098] It should be noted that the chroma measurement device provided by the embodiment of the present application can execute the chroma measurement method of any of the above-mentioned embodiments in specific operation, and the embodiment will not be described here.

[0099] Figure 4 is a structural schematic diagram of an electronic device provided by the present application, as Figure 4 shown, the electronic device can include a processor 410, a communications interface 420, a memory 430, and a communications bus 440, wherein the processor 410, the communications interface 420, and the memory 430 complete mutual communication through the communications bus 440. The processor 410 can invoke a logical instruction in the memory 430 to execute a chroma measurement method, which includes: establishing an evaluation function based on a spectral estimation model to represent the total difference degree of the measured response value and the predicted response value of the multi-channel of the three primary color light sources of the display device; wherein the spectral estimation model is a pre-established mathematical model for spectral curve estimation with a spectral parameter vector as a variable; solving the target spectral parameter vector of the spectral estimation mathematical model by using an optimization algorithm to minimize the evaluation function, to obtain the spectral estimation curve of the display device; and calculating the XYZ tristimulus value and the chroma of the display device according to the spectral estimation curve combined with the color matching function of the CIE1931 standard chroma system.

[0100] In addition, the logical instruction in the memory 430 described above can be implemented in the form of a software functional unit and sold or used as an independent product, which can be stored in a computer readable storage medium.

[0101] In another aspect, the present application also provides a non-transitory computer readable storage medium having a computer program stored thereon, which is executed by a processor to implement the chroma measurement method provided by the above-mentioned embodiments, which includes: establishing an evaluation function based on a spectral estimation model to represent the total difference degree of the measured response value and the predicted response value of the multi-channel of the three primary color light sources of the display device; wherein the spectral estimation model is a pre-established mathematical model for spectral curve estimation with a spectral parameter vector as a variable; solving the target spectral parameter vector of the spectral estimation mathematical model by using an optimization algorithm to minimize the evaluation function, to obtain the spectral estimation curve of the display device; and calculating the XYZ tristimulus value and the chroma of the display device according to the spectral estimation curve combined with the color matching function of the CIE1931 standard chroma system.

[0102] Those skilled in the art can clearly understand the technical solutions of the various embodiments from the above description of the embodiments, and the various embodiments can be implemented by means of software with the necessary general hardware platforms, and of course, can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that makes a contribution, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0103] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, rather than limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A colorimetric measurement method, characterized in that: include: An evaluation function is established based on a spectral estimation model to characterize the total difference between the measured response values ​​and the predicted response values ​​of the multi-channel three-primary color light source of the display device; wherein the spectral estimation model is a pre-established mathematical model for spectral curve estimation using a spectral parameter vector as a variable; With the goal of minimizing the evaluation function, an optimization algorithm is used to solve the target spectrum parameter vector of the spectrum estimation model to obtain a spectrum estimation curve of the display device; Calculating the tristimulus values ​​and chromaticity of the display device according to the spectrum estimation curve in combination with the color matching function; The total difference is the weighted root mean square error of the deviation between the measured response values ​​and the predicted response values ​​of the multiple channels; the predicted response value of each channel is determined based on the spectral estimation model and the spectral response curve of each channel; The spectrum estimation model is established based on the Gaussian-Lorentz model; the spectrum parameter vector includes a plurality of spectrum parameters for describing the unimodal spectrum of the RGB sub-pixel.

2. The colorimetric measurement method according to claim 1, wherein: The evaluation function is established based on the spectrum estimation model, comprising: Measure the spectral response curve of the multi-channel photoelectric sensor using a preset optical device; Using the multi-channel photoelectric sensor to measure the three primary color light sources of the display device to obtain a measured response value of each channel; The evaluation function is established according to the spectral response curve of each channel, the measured response value of each channel and the spectral estimation model.

3. The colorimetric measurement method according to claim 1, wherein: With the goal of minimizing the evaluation function, an optimization algorithm is used to solve the target spectral parameter vector of the spectral estimation model, including: Setting the upper and lower limits of the target spectrum parameter vector; Within the upper limit and the lower limit, with the goal of minimizing the evaluation function, a gradient descent algorithm is used to solve the target spectral parameter vector of the spectral estimation model.

4. The colorimetric measurement method according to claim 1, wherein: After solving the target spectrum parameter vector of the spectrum estimation model by using an optimization algorithm with the goal of minimizing the evaluation function, the method further includes: Comparing the value of the current evaluation function with a preset threshold, and when the value of the current evaluation function is greater than the preset threshold, replacing the initial value of the evaluation function; Based on the replaced initial value, the optimization algorithm is used again to obtain a new target spectral parameter vector; When the value of the evaluation function corresponding to the new target spectrum parameter vector is less than or equal to a preset threshold, the new target spectrum parameter vector is used as the final target spectrum parameter vector.

5. The colorimetric measurement method according to claim 1, wherein: After solving the target spectrum parameter vector of the spectrum estimation model by using an optimization algorithm with the goal of minimizing the evaluation function, the method further includes: Comparing the value of the current evaluation function with a preset threshold, and correcting the spectrum estimation model when the value of the current evaluation function is greater than the preset threshold; Based on the revised spectral estimation model, a new evaluation function is re-established to obtain a new target spectral parameter vector.

6. The colorimetric measurement method according to claim 2, wherein: The specific expression of the evaluation function is: in, represents the evaluation function, is the weight coefficient of each channel, X represents the spectral parameter vector, The channel number is i The measured response value, represents the spectral estimation model, Indicates the channel number is i The spectral response curve of Indicates wavelength.

7. The colorimetric measurement method according to claim 6, wherein: Also includes: When the similarity between some channels in the spectral response curves of the multiple channels and the color matching function is greater than a preset threshold, the weight coefficients corresponding to the some channels are increased.

8. A colorimetric measuring device, characterized in that: A method for implementing the colorimetric measurement method according to any one of claims 1 to 7, comprising: A first processing module is configured to establish an evaluation function based on a spectral estimation model to characterize the total difference between the measured response values ​​and the predicted response values ​​of the multi-channel three-primary-color light source of the display device; wherein the spectral estimation model is a pre-established mathematical model for spectral curve estimation using a spectral parameter vector as a variable; A second processing module is configured to use an optimization algorithm to solve a target spectrum parameter vector of the spectrum estimation model with the goal of minimizing an evaluation function, so as to obtain a spectrum estimation curve of the display device; The third processing module is configured to obtain tristimulus values ​​and chromaticity of the display device according to the spectrum estimation curve in combination with a color matching function.

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