An image stimulus value measurement method, device, system and computer storage medium

Abstract

An embodiment of the present application discloses an image stimulus value measurement method, apparatus, system, and computer storage medium, which are used to improve the measurement accuracy of a measuring instrument. The method in the embodiment of the present application includes: obtaining a first trichromatic stimulus value obtained by a standard measuring instrument measuring an ROI on a standard screen, and obtaining a second trichromatic stimulus value obtained by a target measuring instrument measuring the ROI; determining a mapping transformation equation according to the first trichromatic stimulus value and the second trichromatic stimulus value; obtaining a first image obtained by the target measuring instrument photographing a target screen, where the first image is a grayscale image or an RGB image; converting the first image into a second image, where the second image is a stimulus value image; calibrating all pixel points of the second image according to the mapping transformation equation to obtain a calibrated image; obtaining a first target area of the first image to be measured; determining a second target area corresponding to the first target area from the calibrated image; generating a measurement result according to the second target area.

Description

Technical Field

[0001] Embodiments of the present application relate to the field of image processing technology, and in particular, to an image stimulus value measurement method, apparatus, system, and computer storage medium. Background Art

[0002] In modern display technology and color management, measuring and calibrating the color performance of display devices is a crucial step. With the diversification of display device types and the continuous growth of application requirements, how to ensure the consistency and accuracy of color performance between different display devices has become an important challenge in the fields of color science and display technology. To address this issue, many industries rely on color measurement instruments to evaluate the color output of display devices and calibrate them. However, existing measurement instruments often face the problem of accuracy deviation, which mainly stems from the measurement differences between different measurement instruments or the calibration errors of the measurement instruments themselves, resulting in the inability to precisely align the color data of display devices with the standard color data.

[0003] In this context, it is particularly important to effectively calibrate the target measurement instrument. Traditional measurement instrument calibration methods usually rely on manual adjustment or static mapping relationships, and these methods have certain limitations when dealing with multiple display devices, especially in situations where high measurement accuracy is required. Therefore, how to ensure accurate conversion of color data between the target measurement instrument and the standard instrument through more precise algorithms and processes, and further improve the measurement accuracy, has become an urgent problem in the technical field. Summary of the Invention

[0004] Embodiments of the present application provide an image stimulus value measurement method, apparatus, system, and computer storage medium, which can improve the measurement accuracy of measurement instruments.

[0005] In a first aspect of the embodiments of the present application, an image stimulus value measurement method is provided, including:

[0006] Obtaining first tristimulus values obtained by a standard measurement instrument measuring an ROI on a standard screen, and obtaining second tristimulus values obtained by a target measurement instrument measuring the ROI;

[0007] Determining a mapping transformation equation according to the first tristimulus values and the second tristimulus values;

[0008] Obtaining a first image obtained by the target measurement instrument photographing a target screen, where the first image is a grayscale image or an RGB image;

[0009] Converting the first image into a second image, where the second image is a stimulus value image;

[0010] Calibrate all pixel points of the second image according to the mapping transformation equation to obtain a calibrated image;

[0011] Obtain a first target area of the first image to be measured;

[0012] Determine a second target area corresponding to the first target area from the calibrated image;

[0013] Generate a measurement result according to the second target area.

[0014] Optionally, before obtaining the first tristimulus values obtained by a standard measuring instrument measuring the ROI on a standard screen and obtaining the second tristimulus values obtained by a target measuring instrument measuring the ROI, the method further includes:

[0015] Obtain the resolution of the standard screen;

[0016] Determine the number of ROIs according to the standard resolution;

[0017] Determine the ROI distribution positions according to the number of ROIs.

[0018] Optionally, the determining the ROI distribution positions according to the number of ROIs includes:

[0019] Evenly divide the standard screen according to the number of ROIs to obtain candidate areas equal in number to the number of ROIs;

[0020] Randomly generate ROI distribution positions in the candidate areas, where each candidate area includes one ROI.

[0021] Optionally, after randomly generating the ROI distribution positions in the candidate areas, the method further includes:

[0022] Calculate the pixel distances between adjacent ROIs;

[0023] Determine whether there are illegal distances among the pixel distances, where the illegal distance is a pixel distance less than or equal to a preset threshold;

[0024] If so, randomly generate the ROI distribution positions again.

[0025] Optionally, the determining the mapping transformation equation according to the first tristimulus values and the second tristimulus values includes:

[0026] Apply the first tristimulus values and the second tristimulus values to a mapping transformation formula to calculate various coefficients, and the mapping transformation formula is:

[0027]

[0028] Wherein, is the first tristimulus value, is the second tristimulus value, are the coefficients;

[0029] Apply the calculated coefficients to the mapping transformation formula, and determine the mapping transformation formula after applying the coefficients as the mapping transformation equation.

[0030] Optionally, when calculating the coefficients, solve by the least squares method.

[0031] Optionally, the generating the measurement result according to the second target area includes:

[0032] Calculate the average tristimulus value of the second target area as the measurement result.

[0033] The second aspect of the embodiments of the present application provides an image stimulus value measurement device, including:

[0034] A first acquisition unit, configured to acquire a first tristimulus value obtained by a standard measurement instrument measuring an ROI on a standard screen, and acquire a second tristimulus value obtained by a target measurement instrument measuring the ROI;

[0035] A first determination unit, configured to determine a mapping transformation equation according to the first tristimulus value and the second tristimulus value;

[0036] A second acquisition unit, configured to acquire a first image obtained by the target measurement instrument photographing a target screen, where the first image is a grayscale image or an RGB image;

[0037] A conversion unit, configured to convert the first image into a second image, where the second image is a stimulus value image;

[0038] A calibration unit, configured to calibrate all pixel points of the second image according to the mapping transformation equation to obtain a calibrated image;

[0039] A third acquisition unit, configured to acquire a first target area of the first image to be measured;

[0040] A second determination unit, configured to determine a second target area corresponding to the first target area from the calibrated image;

[0041] A generation unit, configured to generate a measurement result according to the second target area.

[0042] Optionally, the device further includes:

[0043] A fourth acquisition unit, configured to acquire the resolution of the standard screen;

[0044] A third determination unit, configured to determine the number of ROIs according to the resolution;

[0045] A fourth determination unit, configured to determine the distribution positions of the ROIs according to the number of ROIs.

[0046] Optionally, the fourth determination unit is specifically configured to:

[0047] Evenly divide the standard screen according to the number of ROIs to obtain candidate regions equal in number to the number of ROIs;

[0048] Randomly generate ROI distribution positions in the candidate regions, where each candidate region includes one ROI.

[0049] Optionally, the apparatus further includes a judgment unit, and the judgment unit is configured to:

[0050] Calculate the pixel distance between two adjacent ROIs;

[0051] Judge whether there is an illegal distance among the pixel distances, where the illegal distance is a pixel distance less than or equal to a preset threshold;

[0052] If so, randomly generate the ROI distribution positions again.

[0053] Optionally, the first determination unit is specifically configured to:

[0054] Apply the first tristimulus value and the second tristimulus value to a mapping transformation formula to calculate various coefficients, and the mapping transformation formula is:

[0055]

[0056] Wherein, is the first tristimulus value, is the second tristimulus value, are the various coefficients;

[0057] Apply the calculated various coefficients to the mapping transformation formula, and determine the mapping transformation formula after applying the various coefficients as a mapping transformation equation.

[0058] Optionally, the generation unit is specifically configured to:

[0059] Calculate the mean tristimulus value of the second target region as a measurement result.

[0060] A third aspect of the embodiments of the present application provides an image stimulus value measurement system, including:

[0061] A processor, a memory, an input / output unit, and a bus;

[0062] The processor is connected to the memory, the input / output unit, and the bus;

[0063] The memory stores a program, and the processor calls the program to execute the method in the first aspect and any possible implementation manner of the first aspect.

[0064] A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, on which a program is stored. When the program is executed on a computer, the computer is caused to execute the method in the first aspect and any possible implementation manner of the first aspect.

[0065] As can be seen from the above technical solutions, the embodiments of the present application have the following advantages:

[0066] In this embodiment, by accurately calibrating the target measuring instrument, the measurement accuracy of the target measuring instrument can be significantly improved. First, by obtaining and comparing the tristimulus values of the standard measuring instrument and the target measuring instrument, basic data is provided for the subsequent mapping transformation equation. Then, through the derivation and application of the mapping transformation equation, the data of the target measuring instrument is successfully converted into the data of the standard measuring instrument, thereby eliminating the differences between the measuring tools. This conversion ensures that the target measuring instrument can provide measurement results consistent with the standard instrument, thus effectively improving the accuracy of the target measuring instrument. In addition, by converting the image into a stimulus value image and performing calibration, it is ensured that the measurement results of each area on the target screen are correctly standardized, enabling the target measuring instrument to maintain high-precision measurement capabilities under different test conditions. Finally, the color and brightness measurement capabilities of the target measuring instrument are significantly improved, and more accurate and consistent measurement data can be provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a schematic flowchart of an embodiment of the image stimulus value measurement method in the embodiments of the present application;

[0068] Figure 2 It is a schematic flowchart of an embodiment of determining the ROI distribution position in the embodiments of the present application;

[0069] Figure 3 It is a schematic flowchart of an embodiment of determining the mapping transformation equation in the embodiments of the present application;

[0070] Figure 4 It is a schematic structural diagram of an embodiment of the image stimulus value measurement device in the embodiments of the present application;

[0071] Figure 5 It is a schematic structural diagram of an embodiment of the image stimulus value measurement system in the embodiments of the present application. Detailed implementation manners

[0072] An embodiment of the present application provides an image stimulus value measurement method, device, system, and computer storage medium, which are used to improve the measurement accuracy of a measuring instrument.

[0073] The method of the present application can be applied to a server, a terminal, or other devices with logical processing capabilities, and the present application does not limit this. For the convenience of description, the following will take the execution entity as a terminal as an example for description.

[0074] The following will describe the embodiments in the present application with reference to the accompanying drawings.

[0075] Please refer to Figure 1 , an embodiment of the image stimulus value measurement method in the embodiment of the present application includes:

[0076] 101. Obtain a first trichromatic stimulus value obtained by a standard measuring instrument measuring an ROI on a standard screen, and obtain a second trichromatic stimulus value obtained by a target measuring instrument measuring the ROI;

[0077] The terminal uses two different measuring instruments to measure the standard screen respectively. The trichromatic stimulus values obtained by the standard measuring instrument and the target measuring instrument correspond to the first trichromatic stimulus value and the second trichromatic stimulus value respectively. The trichromatic stimulus value is quantitative data used to represent color perception, usually represented by three components of RGB (red, green, and blue), but other forms of stimulus values can be used according to different devices or standards. The measurement result of the standard measuring instrument is used as a benchmark, and the target measuring instrument will be calibrated according to this benchmark later. The region of interest (ROI) refers to the pixels in a specific area on the screen. Usually, a relatively uniform or representative area is selected for measurement to ensure that the data is representative.

[0078] It should be noted that the standard measuring instrument is determined by the user, rather than a measuring instrument calibrated by an authoritative measurement unit. The user can select a measuring instrument with good effects on the usage scenario from various measuring instruments according to the actual situation as the standard measuring instrument, and the standard measuring instrument will be used as the calibration target of the target measuring instrument. The standard screen refers to a display device that has been precisely calibrated and has known color characteristics and display performance, ensuring that all devices can achieve consistent standards in terms of color and brightness performance.

[0079] 102. Determine a mapping transformation equation according to the first trichromatic stimulus value and the second trichromatic stimulus value;

[0080] Based on the correspondence between the first tristimulus values and the second tristimulus values, the terminal derives a mapping transformation equation through a mathematical model (such as linear regression or polynomial fitting, etc.). The function of this equation is to map the second tristimulus values obtained by the target measuring instrument into the color coordinate system of the standard measuring instrument. Through this mapping, the terminal can convert the color of the target screen into a representation consistent with that of the standard screen, thereby eliminating the color deviation caused by the difference in measuring instruments.

[0081] 103. Obtain a first image captured by the target measuring instrument for the target screen. The first image is a grayscale image or an RGB image.

[0082] The terminal captures an image of the target screen through the target measuring instrument. The captured image can be a grayscale image, which only contains luminance information, or an RGB image, which contains the red, green, and blue color values of each pixel. The acquisition of the target image reflects the color information actually displayed on the screen. This step is the preliminary color acquisition of the target screen, providing basic data for subsequent conversion and calibration.

[0083] 104. Convert the first image into a second image, where the second image is a stimulus value image.

[0084] The terminal converts the captured first image from an RGB or grayscale image into a stimulus value image. In an RGB image, the color value of each pixel is converted into its corresponding color stimulus value (such as tristimulus values). This conversion usually depends on the color gamut of the display device and the standard model, such as the CIE color space. In the case of a grayscale image, the conversion may be simpler because it only involves the mapping of luminance information. The converted stimulus value image can more accurately reflect the color performance of the target screen, laying the foundation for subsequent calibration steps.

[0085] 105. Calibrate all pixel points of the second image according to the mapping transformation equation to obtain a calibrated image.

[0086] Using the mapping transformation equation obtained in step 102, the terminal calibrates the stimulus value of each pixel in the second image. By transforming each pixel point, the terminal adjusts the stimulus value measured by the target measuring instrument to the corresponding value under the standard measuring instrument. The result of the calibration is a new image called the calibrated image. The color and luminance of each pixel point in this image have been corrected according to the standard, thereby eliminating the influence caused by the difference in measuring devices and ensuring that the calibrated image has the color performance of the standard screen.

[0087] 106. Obtain a first target area of the first image to be measured.

[0088] The terminal selects a specific area from the first image as the measurement object. This area is usually the part in the target image that needs to be analyzed or optimized, and is called the first target area. This area may contain specific color test patterns, brightness variations, or other important visual features to ensure the accuracy of the measurement. When selecting the target area, the uniformity and representativeness of the area are usually considered to ensure the accuracy of the measurement result.

[0089] 107. Determine a second target area corresponding to the first target area from the calibration image;

[0090] The terminal finds the area in the calibration image that corresponds to the first target area of the first image. Since calibration was performed in step 105, the color and brightness information in the calibration image has been matched to the standard. Therefore, the terminal accurately identifies the area in the calibration image corresponding to the first target area by comparing the spatial positions of the first image and the calibration image. This step ensures that the target area is correctly corresponding in the calibrated image, so that accurate measurement can be further performed.

[0091] 108. Generate a measurement result according to the second target area.

[0092] The terminal performs detailed measurement and analysis on the second target area extracted from the calibration image. These measurement results may include the color accuracy, brightness uniformity, color difference value, etc. of this area. Through these analyses, the terminal can obtain a comprehensive evaluation of the color performance and display quality of the target screen, ensure that it meets the standard display requirements, reduce color deviation, and ensure the stability and consistency of the display effect.

[0093] Furthermore, in some embodiments of the present application, when generating a measurement result according to the second target area, the mean value of the tristimulus values of the second target area can be calculated as the measurement result. Specifically, first obtain the tristimulus values of each pixel in the second target area, then calculate the mean value of the tristimulus values of these pixels, and finally use this mean value as the measurement result.

[0094] In this embodiment, the terminal completes the precise calibration of the target measuring instrument, thereby significantly improving the measurement accuracy of the target measuring instrument. First, by obtaining and comparing the tristimulus values of the standard measuring instrument and the target measuring instrument, basic data is provided for the subsequent mapping transformation equation. Then, through the derivation and application of the mapping transformation equation, the data of the target measuring instrument is successfully converted into the data of the standard measuring instrument, thereby eliminating the differences between the measuring tools. This conversion ensures that the target measuring instrument can provide measurement results consistent with the standard instrument, thereby effectively improving the accuracy of the target measuring instrument. In addition, by converting the image into a stimulus value image and performing calibration, it is ensured that the measurement results of each area on the target screen are correctly standardized, enabling the target measuring instrument to maintain high-precision measurement capabilities under different test conditions. Finally, the color and brightness measurement capabilities of the target measuring instrument are significantly improved, and more accurate and consistent measurement data can be provided.

[0095] Please refer to Figure 2 , in some embodiments of the present application, before step 101 in the above embodiment, which is to obtain the first tristimulus value obtained by the standard measuring instrument measuring the ROI on the standard screen and obtain the second tristimulus value obtained by the target measuring instrument measuring the ROI, the image stimulus value measurement method may further include the following steps:

[0096] 201. Obtain the resolution of the standard screen;

[0097] The terminal obtains the resolution data of the standard screen, that is, the horizontal and vertical pixel numbers of the standard screen. Resolution is a key parameter that determines the display details of the screen and determines the number of pixels in each display area, which is of great significance for the subsequent area division. After obtaining the resolution, the terminal can reasonably allocate areas according to the size and pixel number of the screen, thereby ensuring the accuracy and effectiveness of subsequent operations.

[0098] 202. Determine the number of ROIs according to the resolution;

[0099] The terminal determines the number of ROIs (Regions of Interest) to be divided according to the resolution of the standard screen. The number of ROIs is usually related to the pixel quantity of the screen, the fineness of region division, and the requirements of subsequent tests. According to the resolution, the terminal will decide how many ROIs to divide the screen into, so as to perform effective region division and testing in the following steps. The goal of this step is to generate an appropriate number of ROIs according to the resolution to ensure that each ROI can contain enough pixels for accurate measurement. It should be noted that in order to achieve a good calibration effect, the higher the resolution of the standard screen, the more the number of ROIs should be, and the two are in a positive correlation. In practical applications, the resolutions of the screen are generally typical values (such as 1920*1080, 2560*1440, 3840*2160, etc., and the typical values also include other values, which are not enumerated here). The number of different resolutions is very limited. Therefore, the corresponding number of ROIs can be preset for each different resolution and stored in the terminal. When applying, the terminal directly looks up the number of ROIs corresponding to the resolution of the standard screen, reducing the calculation amount and being beneficial to improving efficiency. In a possible embodiment, a calculation formula for the resolution and the number of ROIs can be established. After obtaining the resolution of the standard screen, the terminal substitutes the resolution into the calculation formula to calculate the corresponding number of ROIs. In this calculation formula, the resolution and the number of ROIs are in a positive correlation, that is, the larger the resolution, the more the number of ROIs. Through this calculation formula, even if the resolution of the standard screen is not a typical value, the terminal can determine the corresponding number of ROIs, improving the adaptability.

[0100] 203. Uniformly divide the standard screen according to the number of ROIs to obtain candidate regions equal in number to the number of ROIs;

[0101] The terminal uniformly divides the standard screen into the same number of candidate regions according to the determined number of ROIs. The size of each candidate region will be calculated according to the total resolution of the screen and the number of ROIs to ensure that each region on the screen has a similar area and pixel quantity. The purpose of this is to ensure that the measurement conditions of each ROI region are as consistent as possible and avoid measurement errors caused by uneven region sizes.

[0102] 204. Randomly generate the ROI distribution positions in the candidate regions, where each candidate region includes one ROI.

[0103] The terminal randomly generates the position of an ROI within each candidate region. This random distribution method can avoid the deviation of specific rules, making the positions of the ROIs more scattered, so as to cover a wider area during the testing process. This random distribution ensures the representativeness of the measurement area and helps to improve the accuracy and reliability of subsequent test results. Each candidate region contains an ROI, ensuring that each divided region is fully tested.

[0104] 205. Calculate the pixel distance between two adjacent ROIs;

[0105] The terminal calculates the pixel distances between all adjacent ROIs. This distance can be obtained through simple geometric calculations and is used to measure the spatial distribution between two ROIs. By calculating these distances, the terminal can evaluate whether the distribution of the ROI regions is reasonable, ensuring that there will be no overlap or excessive density between regions during subsequent testing, which may affect the measurement accuracy.

[0106] 206. Determine whether there is an illegal distance among the pixel distances, where the illegal distance is a pixel distance less than or equal to a preset threshold; if so, execute steps 204 - 206 again.

[0107] The terminal determines whether the pixel distances between adjacent ROIs meet the requirements. If it is found that the pixel distance between two adjacent ROIs is less than or equal to the preset threshold, then this distance is considered an illegal distance, which may cause overlap or excessive density of the measurement area and affect the test effect. If there is an illegal distance, the terminal will randomly generate the positions of the ROIs again and repeat steps 204 and 205 until the pixel distances between all adjacent ROIs meet the requirements.

[0108] In this embodiment, the terminal successfully divides the standard screen into multiple uniform ROI regions and ensures that these regions are reasonably distributed and the distances between them are appropriate. This process can effectively avoid measurement errors caused by region overlap or overly concentrated distribution, ensuring the accuracy and representativeness of subsequent measurements. Through reasonable region division and distribution, it can more comprehensively cover all parts of the standard screen, thus providing a more accurate and reliable data basis for measurement results. This method greatly improves the scientific nature of region selection during the measurement process and helps to improve the reliability of the test and the consistency of the data.

[0109] Please refer to Figure 3 , in some embodiments of the present application, step 102 in the above embodiment of determining the mapping transformation equation according to the first tristimulus value and the second tristimulus value may include the following steps:

[0110] 301. Apply the first tristimulus value and the second tristimulus value to the mapping transformation formula to calculate each coefficient;

[0111] The terminal uses the first tristimulus values and the second tristimulus values to calculate a series of coefficients through a known mapping transformation formula. The mapping transformation formula is as follows:

[0112] Formula 1

[0113] Wherein, is the first tristimulus value, is the second tristimulus value, are the coefficients of each item.

[0114] These coefficients are the core parameters of the mapping transformation equation, which determine how the measurement results of the target measuring instrument are converted into the color space of the standard measuring instrument. By applying these stimulus values, the terminal can theoretically obtain a mathematical model to capture the color difference between the target screen and the standard screen and provide the necessary mathematical support for subsequent calibration.

[0115] Furthermore, when calculating the coefficient , the least squares method can be used to solve it.

[0116] 302. Apply the calculated coefficients of each item to the mapping transformation formula, and determine the mapping transformation formula after applying the coefficients of each item as the mapping transformation equation.

[0117] The terminal substitutes the coefficients calculated in step 301 into the mapping transformation formula to generate the final mapping transformation equation. This mapping transformation equation describes how to convert the measurement results of the target measuring instrument into the measurement results of the standard measuring instrument, ensuring the corresponding relationship of color data between different instruments. The finally determined mapping transformation equation is an accurate calibration tool for the color of the target screen and can provide an accurate mathematical basis for data conversion in subsequent steps.

[0118] In this embodiment, the terminal converts the first tristimulus values and the second tristimulus values into specific coefficients in the mapping transformation equation, thereby realizing the accurate conversion of color data between the target measuring instrument and the standard measuring instrument. This process effectively eliminates the measurement deviation between instruments, enables the measurement results of the target measuring instrument to accurately match the data of the standard measuring instrument, and ensures the accuracy of subsequent measurements. In this way, the error in the entire calibration process is minimized, thereby significantly improving the measurement accuracy and reliability of the target measuring instrument and laying a more accurate data foundation for subsequent display effect analysis and debugging.

[0119] Please refer to Figure 4 , an embodiment of the image stimulus value measuring device in the embodiment of the present application includes:

[0120] The first acquisition unit 401 is configured to acquire a first tristimulus value obtained by a standard measuring instrument measuring an ROI on a standard screen, and acquire a second tristimulus value obtained by a target measuring instrument measuring the ROI;

[0121] The first determination unit 402 is configured to determine a mapping transformation equation according to the first tristimulus value and the second tristimulus value;

[0122] The second acquisition unit 403 is configured to acquire a first image obtained by the target measuring instrument photographing a target screen, where the first image is a grayscale image or an RGB image;

[0123] The conversion unit 404 is configured to convert the first image into a second image, where the second image is a tristimulus value image;

[0124] The calibration unit 405 is configured to calibrate all pixel points of the second image according to the mapping transformation equation to obtain a calibrated image;

[0125] The third acquisition unit 406 is configured to acquire a first target area of the first image to be measured;

[0126] The second determination unit 407 is configured to determine a second target area corresponding to the first target area from the calibrated image;

[0127] The generation unit 408 is configured to generate a measurement result according to the second target area.

[0128] In this embodiment, the server completes the precise calibration of the target measuring instrument, thereby significantly improving the measurement accuracy of the target measuring instrument. First, the first acquisition unit 401 acquires and compares the tristimulus values of the standard measuring instrument and the target measuring instrument, providing basic data for the subsequent mapping transformation equation. Then, through the derivation and application of the mapping transformation equation by the first determination unit 402, the data of the target measuring instrument is successfully converted into the data of the standard measuring instrument, thereby eliminating the differences between the measuring tools. This conversion ensures that the target measuring instrument can provide measurement results consistent with the standard instrument, thus effectively improving the accuracy of the target measuring instrument. In addition, by converting the image into a tristimulus value image through the conversion unit 404 and calibrating it using the calibration unit 405, it is ensured that the measurement results of each area on the target screen are correctly standardized, enabling the target measuring instrument to maintain high-precision measurement capabilities under different test conditions. Finally, the color and brightness measurement capabilities of the target measuring instrument are significantly improved, and more accurate and consistent measurement data can be provided.

[0129] Optionally, the device further includes:

[0130] The fourth acquisition unit is configured to acquire the resolution of the standard screen;

[0131] A third determination unit, configured to determine the number of ROIs according to the resolution;

[0132] A fourth determination unit, configured to determine the ROI distribution positions according to the number of ROIs.

[0133] Optionally, the fourth determination unit is specifically configured to:

[0134] Evenly divide the standard screen according to the number of ROIs to obtain candidate regions equal in number to the number of ROIs;

[0135] Randomly generate ROI distribution positions in the candidate regions, where each candidate region includes one ROI.

[0136] Optionally, the apparatus further includes a judgment unit, and the judgment unit is configured to:

[0137] Calculate the pixel distance between two adjacent ROIs;

[0138] Judge whether there is an illegal distance among the pixel distances, and the illegal distance is a pixel distance less than or equal to a preset threshold;

[0139] If so, randomly generate the ROI distribution positions again.

[0140] Optionally, the first determination unit 402 is specifically configured to:

[0141] Apply the first tristimulus value and the second tristimulus value to the mapping transformation formula to calculate each coefficient, and the mapping transformation formula is:

[0142]

[0143] Wherein, is the first tristimulus value, is the second tristimulus value, is each coefficient;

[0144] Apply the calculated each coefficient to the mapping transformation formula, and determine the mapping transformation formula after applying each coefficient as the mapping transformation equation.

[0145] Optionally, the generation unit 408 is specifically configured to:

[0146] Calculate the mean tristimulus value of the second target region as the measurement result.

[0147] In this embodiment, the functions of each unit correspond to the steps in the foregoing Figures 1 to 3 illustrated embodiment, and will not be elaborated herein.

[0148] Please refer to Figure 5 , an embodiment of the image stimulus value measurement system in the embodiment of the present application includes:

[0149] A processor 501, a memory 502, an input / output unit 503, and a bus 504;

[0150] The processor 501 is connected to the memory 502, the input / output unit 503, and the bus 504;

[0151] A program is stored in the memory 502, and the processor 501 calls the program to execute Figures 1 to 3 the steps in the illustrated embodiment.

[0152] In this embodiment, the functions of the processor 501 correspond to the steps in the foregoing Figures 1 to 3 illustrated embodiment, and will not be elaborated herein.

[0153] This application embodiment also provides a computer-readable storage medium, on which a program is stored. When the program is executed on a computer, the computer is caused to execute the method in any of the foregoing Figures 1 to 3 possible implementation manners.

[0154] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0155] In the several embodiments provided by this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division manners in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0156] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0157] In addition, in each embodiment of this application, the functional units can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0158] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, read-only memory), random access memories (RAM, random access memory), magnetic disks, or optical discs.

Claims

1. An image stimulus value measurement method, characterized in that Including: Obtain the resolution of the standard screen; Determine the number of ROIs according to the resolution; Evenly divide the standard screen according to the number of ROIs to obtain candidate regions equal in number to the number of ROIs; Randomly generate ROI distribution positions in the candidate regions, where each candidate region includes one ROI; Calculate the pixel distance between two adjacent ROIs; Determine whether there is an illegal distance among the pixel distances, where the illegal distance is a pixel distance less than or equal to a preset threshold; If so, randomly generate ROI distribution positions again; Obtain the first tristimulus values obtained by a standard measuring instrument measuring the ROIs on the standard screen, and obtain the second tristimulus values obtained by a target measuring instrument measuring the ROIs; Apply the first tristimulus values and the second tristimulus values to a mapping transformation formula to calculate various coefficients, and the mapping transformation formula is: wherein, is the first tristimulus value, is the second tristimulus value, are the coefficients; Apply the calculated various coefficients to the mapping transformation formula, and determine the mapping transformation formula after applying the various coefficients as a mapping transformation equation; Obtain a first image obtained by the target measuring instrument photographing a target screen, where the first image is a grayscale image or an RGB image; Convert the first image into a second image, where the second image is a tristimulus value image; Calibrate all pixel points of the second image according to the mapping transformation equation to obtain a calibrated image; Obtain a first target region of the first image to be measured; Determine a second target region corresponding to the first target region from the calibrated image; Generate a measurement result according to the second target region.

2. The method according to claim 1, wherein When calculating the various coefficients, solve by the least squares method.

3. The method according to any one of claims 1 to 2, characterized in that, The generating the measurement result according to the second target region includes: Calculate the mean tristimulus value of the second target region as the measurement result.

4. An image stimulus value measuring device, characterized in that, Including: A first obtaining unit, configured to obtain the first tristimulus values obtained by a standard measuring instrument measuring the ROIs on the standard screen, and obtain the second tristimulus values obtained by a target measuring instrument measuring the ROIs; A first determining unit, configured to determine a mapping transformation equation according to the first tristimulus values and the second tristimulus values; A second obtaining unit, configured to obtain a first image obtained by the target measuring instrument photographing a target screen, where the first image is a grayscale image or an RGB image; A conversion unit, configured to convert the first image into a second image, where the second image is a tristimulus value image; A calibration unit, configured to calibrate all pixel points of the second image according to the mapping transformation equation to obtain a calibrated image; A third obtaining unit, configured to obtain a first target region of the first image to be measured; A second determining unit, configured to determine a second target region corresponding to the first target region from the calibrated image; A generating unit, configured to generate a measurement result according to the second target region; The device further includes: A fourth obtaining unit, configured to obtain the resolution of the standard screen; A third determining unit, configured to determine the number of ROIs according to the resolution; A fourth determining unit, configured to determine the ROI distribution positions according to the number of ROIs; The specific use of the fourth determination unit is as follows: Evenly divide the standard screen according to the number of ROIs to obtain candidate regions equal in number to the number of ROIs; Randomly generate ROI distribution positions in the candidate regions, where each candidate region includes one ROI; The specific use of the first determination unit is as follows: Apply the first tristimulus value and the second tristimulus value to a mapping transformation formula to calculate various coefficients, and the mapping transformation formula is: wherein, is the first tristimulus value, is the second tristimulus value, are the coefficients; Apply the calculated various coefficients to the mapping transformation formula, and determine the mapping transformation formula after applying the various coefficients as a mapping transformation equation; The device further includes a judgment unit, and the judgment unit is used for: Calculate the pixel distances between two adjacent ROIs; Judge whether there is an illegal distance among the pixel distances, and the illegal distance is a pixel distance less than or equal to a preset threshold; If so, randomly generate ROI distribution positions again.

5. An image stimulus value measurement system, characterized in that, It includes: A processor, a memory, an input / output unit, and a bus; The processor is connected to the memory, the input / output unit, and the bus; The memory stores a program, and the processor calls the program to execute the method according to any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, A program is stored on the computer-readable storage medium, and when the program is executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 3.

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