Image stimulation value measuring method, device and system and computer storage medium

Abstract

The embodiment of the invention discloses an image stimulation value measuring method, device and system and a computer storage medium, which are used for improving the measuring accuracy of a measuring instrument. The method provided by the embodiment of the invention comprises the following steps: acquiring a first tristimulus value obtained by measuring an ROI (Region of Interest) on a standard screen by a standard measuring instrument, and acquiring a second tristimulus value obtained by measuring the ROI by a target measuring instrument; determining a mapping transformation equation according to the first tristimulus value and the second tristimulus value; obtaining a first image obtained by shooting the target screen measured by the target measuring instrument, wherein the first image is a grayscale image or an RGB image; converting the first image into a second image, wherein 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; acquiring a first target area of the first image needing to be measured; determining a second target area corresponding to the first target area from the calibration image; and generating a measurement result according to the second target area.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of image processing technology, and in particular to a method, device, system and computer storage medium for measuring image stimulation values. Background Art

[0002] In modern display technology and color management, measuring and calibrating the color performance of display devices is a crucial link. With the diversification of display device types and the continuous growth of application needs, how to ensure the consistency and accuracy of color performance between different display devices has become an important challenge in the field of color science and display technology. To solve this problem, many industries rely on color measurement instruments to evaluate the color output of display devices and calibrate them. However, existing measuring instruments often face the problem of accuracy deviation, which is mainly due to the measurement differences between different measuring instruments, or due to the calibration errors of the measuring instruments themselves, resulting in the color data of the display device cannot be accurately aligned with the standard color data.

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

[0004] The embodiments of the present application provide an image stimulation value measurement method, device, system and computer storage medium, which can improve the measurement accuracy of a measuring instrument.

[0005] A first aspect of an embodiment of the present application provides a method for measuring an image stimulus value, comprising: Acquire the first three stimulus values ​​obtained by measuring the ROI on the standard screen with the standard measuring instrument, and acquire the second three stimulus values ​​obtained by measuring the ROI with the target measuring instrument; determining a mapping transformation equation according to the first three stimulus values ​​and the second three stimulus values; Acquire a first image obtained by measuring a target screen with the target measuring instrument, wherein the first image is a grayscale image or an RGB image; converting the first image into a second image, wherein the second image is a stimulus value image; Calibrate all pixel points of the second image according to the mapping transformation equation to obtain a calibrated image; Acquire a first target area of ​​the first image to be measured; determining, from the calibration image, a second target area corresponding to the first target area; A measurement result is generated based on the second target area.

[0006] Optionally, before obtaining the first tristimulus values ​​obtained by measuring the ROI on the standard screen with a standard measuring instrument and obtaining the second tristimulus values ​​obtained by measuring the ROI with a target measuring instrument, the method further includes: Get the resolution of the standard screen; Determine the number of ROIs according to the target resolution; The ROI distribution positions are determined according to the number of ROIs.

[0007] Optionally, determining the ROI distribution position according to the number of ROIs includes: Evenly dividing the standard screen according to the number of ROIs to obtain candidate regions equal to the number of ROIs; ROI distribution positions are randomly generated in the candidate regions, wherein each of the candidate regions includes a ROI.

[0008] Optionally, after randomly generating ROI distribution positions in the candidate region, the method further includes: Calculate the pixel distance between two adjacent ROIs; Determine whether there is an illegal distance in the pixel distance, the illegal distance being a pixel distance less than or equal to a preset threshold; If yes, the ROI distribution position is randomly generated again.

[0009] Optionally, determining a mapping transformation equation according to the first three stimulus values ​​and the second three stimulus values ​​includes: The first three stimulus values ​​and the second three stimulus values ​​are applied to a mapping transformation formula to calculate various coefficients. The mapping transformation formula is:

[0010] in, are the first three stimulus values, is the second and third stimulus value, are the coefficients of each item; The calculated coefficients are applied to the mapping transformation formula, and the mapping transformation formula after applying the coefficients is determined as a mapping transformation equation.

[0011] Optionally, when calculating the coefficients, a least squares method is used to solve them.

[0012] Optionally, generating a measurement result according to the second target area includes: An average of the three stimulus values ​​of the second target area is calculated as a measurement result.

[0013] A second aspect of an embodiment of the present application provides an image stimulation value measuring device, comprising: A first acquisition unit is used to acquire first tristimulus values ​​obtained by measuring an ROI on a standard screen with a standard measuring instrument, and to acquire second tristimulus values ​​obtained by measuring the ROI with a target measuring instrument; A first determining unit, configured to determine a mapping transformation equation according to the first three stimulus values ​​and the second three stimulus values; A second acquisition unit is used to acquire a first image obtained by measuring the target screen with the target measuring instrument, wherein the first image is a grayscale image or an RGB image; A conversion unit, used for converting the first image into a second image, where the second image is a stimulus value image; A calibration unit, configured to calibrate all pixels of the second image according to the mapping transformation equation to obtain a calibrated image; A third acquisition unit, used for acquiring a first target area of ​​the first image to be measured; a second determining unit, configured to determine, from the calibration image, a second target area corresponding to the first target area; A generating unit is configured to generate a measurement result according to the second target area.

[0014] Optionally, the device further comprises: A fourth acquisition unit, used to acquire the resolution of the standard screen; A third determining unit, configured to determine the number of ROIs according to the resolution; The fourth determining unit is used to determine the ROI distribution position according to the number of ROIs.

[0015] Optionally, the fourth determining unit is specifically configured to: Evenly dividing the standard screen according to the number of ROIs to obtain candidate regions equal to the number of ROIs; ROI distribution positions are randomly generated in the candidate regions, wherein each of the candidate regions includes a ROI.

[0016] Optionally, the device further includes a judgment unit, and the judgment unit is used to: Calculate the pixel distance between two adjacent ROIs; Determine whether there is an illegal distance in the pixel distance, the illegal distance being a pixel distance less than or equal to a preset threshold; If yes, the ROI distribution position is randomly generated again.

[0017] Optionally, the first determining unit is specifically configured to: The first three stimulus values ​​and the second three stimulus values ​​are applied to a mapping transformation formula to calculate various coefficients. The mapping transformation formula is:

[0018] in, are the first three stimulus values, is the second and third stimulus value, are the coefficients of each item; The calculated coefficients are applied to the mapping transformation formula, and the mapping transformation formula after applying the coefficients is determined as a mapping transformation equation.

[0019] Optionally, the generating unit is specifically used for: An average of the three stimulus values ​​of the second target area is calculated as a measurement result.

[0020] A third aspect of the present application provides an image stimulus value measurement system, including: Processor, memory, input-output unit, and bus; The processor is connected to the memory, the input and output unit, and the bus; A program is stored in the memory, and the processor calls the program to execute the method in the first aspect and any possible implementation manner of the first aspect.

[0021] A fourth aspect of an embodiment 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 executes the method in the first aspect and any possible implementation of the first aspect.

[0022] It can be seen from the above technical solutions that the embodiments of the present application have the following advantages: In this embodiment, the measurement accuracy of the target measuring instrument can be significantly improved by accurately calibrating the target measuring instrument. First, by obtaining and comparing the three stimulus 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 calibrating it, it is ensured that the measurement results of each area on the target screen are correctly standardized, so that the target measuring instrument can maintain high-precision measurement capabilities under different test conditions. Ultimately, 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

[0023] Figure 1 This is a flow chart of an embodiment of the method for measuring image stimulus values ​​in the embodiments of the present application; Figure 2 A schematic diagram of a flow chart of an embodiment of determining ROI distribution positions in an embodiment of the present application; Figure 3 A schematic diagram of a flow chart of an embodiment of determining a mapping transformation equation in an embodiment of the present application; Figure 4 This is a schematic diagram of the structure of an embodiment of an image stimulation value measuring device in an embodiment of the present application; Figure 5 It is a structural schematic diagram of an embodiment of the image stimulation value measurement system in the embodiments of the present application. DETAILED DESCRIPTION

[0024] The embodiments of the present application provide an image stimulation value measurement method, device, system and computer storage medium for improving the measurement accuracy of a measuring instrument.

[0025] The method of the present application can be applied to a server, a terminal or other devices with logic processing capabilities, and the present application does not limit this. For the convenience of description, the following description is made by taking the execution subject as an example of a terminal.

[0026] The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0027] See also Figure 1 In the embodiment of the present application, an embodiment of the method for measuring image stimulus value includes: 101. Acquire first three stimulus values ​​obtained by measuring the ROI on the standard screen with a standard measuring instrument, and acquire second three stimulus values ​​obtained by measuring the ROI with a target measuring instrument; The terminal uses two different measuring instruments to measure the standard screen respectively. The tristimulus values ​​obtained by the standard measuring instrument and the target measuring instrument correspond to the first tristimulus values ​​and the second tristimulus values ​​respectively. Tristimulus values ​​are 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 results of the standard measuring instrument are used as a benchmark, and the target measuring instrument will be calibrated based on 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.

[0028] It should be noted that the standard measuring instrument is determined by the user, and does not refer to the measuring instrument calibrated by the authoritative measurement unit. The user can select the measuring instrument with good effect in the usage scenario from various measuring instruments according to the actual situation, 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 accurately calibrated and has known color characteristics and display performance, ensuring that all devices can achieve consistent standards in color and brightness performance.

[0029] 102. Determine a mapping transformation equation according to the first tristimulus values ​​and the second tristimulus values; Based on the correspondence between the first three stimulus values ​​and the second three stimulus values, the terminal derives the 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 three stimulus values ​​obtained by the target measuring instrument to 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 the standard screen, thereby eliminating the color deviation caused by differences in measuring instruments.

[0030] 103. Acquire a first image obtained by photographing a target screen by measuring with a target measuring instrument, where the first image is a grayscale image or an RGB image; The terminal captures the target screen through the target measuring instrument to obtain an image. The captured image can be a grayscale image, which only contains brightness information, or an RGB image, which contains the red, green, and blue 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.

[0031] 104. Convert the first image into a second image, where the second image is a stimulus value image; 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 a corresponding color stimulus value (such as a tristimulus value). This conversion usually depends on the color gamut and standard model of the display device, 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 brightness information. The converted stimulus value image can more accurately reflect the color performance of the target screen, laying the foundation for subsequent calibration steps.

[0032] 105. Calibrate all pixel points of the second image according to the mapping transformation equation to obtain a calibrated image; 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, 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 a calibration image, in which the color and brightness of each pixel in the image have been corrected according to the standard, thereby eliminating the influence caused by the difference in measuring equipment and ensuring that the calibrated image has the color performance of the standard screen.

[0033] 106. Acquire a first target area of ​​a first image to be measured; The terminal selects a specific area from the first image as the measurement object. This area is usually the part of the target image that needs to be analyzed or optimized, called the first target area. This area may contain specific color test patterns, brightness changes, 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 results.

[0034] 107. Determine a second target area corresponding to the first target area from the calibration image; The terminal finds the area corresponding to the first target area of ​​the first image in the calibration image. Since the calibration is performed in step 105, the color and brightness information in the calibration image has been matched with the standard. Therefore, the terminal accurately identifies the area corresponding to the first target area in the calibration image by comparing the spatial positions of the first image and the calibration image. This step ensures that the target area is correctly matched in the calibrated image, so that accurate measurement can be further performed.

[0035] 108. Generate a measurement result according to the second target area.

[0036] The terminal performs detailed measurements 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 the area. Through these analyses, the terminal can draw a comprehensive evaluation of the color performance and display quality of the target screen to ensure that it meets the standard display requirements, reduce color deviation, and ensure the stability and consistency of the display effect.

[0037] Further, in some embodiments of the present application, when generating a measurement result according to the second target area, the mean of the three stimulus values ​​of the second target area can be calculated as the measurement result. Specifically, the three stimulus values ​​of each pixel in the second target area are first obtained, and then the mean of the three stimulus values ​​of these pixels is calculated, and finally the mean is used as the measurement result.

[0038] 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 three stimulus 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 calibrating it, it is ensured that the measurement results of each area on the target screen are correctly standardized, so that the target measuring instrument can maintain high-precision measurement capabilities under different test conditions. Ultimately, the color and brightness measurement capabilities of the target measuring instrument are significantly improved, and more accurate and consistent measurement data can be provided.

[0039] See also Figure 2 In some embodiments of the present application, before step 101 in the above embodiment obtains the first tristimulus values ​​obtained by measuring the ROI on the standard screen by the standard measuring instrument and obtains the second tristimulus values ​​obtained by measuring the ROI by the target measuring instrument, the image stimulus value measurement method may further include the following steps: 201. Get the resolution of the standard screen; The terminal obtains the resolution data of the standard screen, that is, the number of horizontal and vertical pixels 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 area division in the subsequent steps. After obtaining the resolution, the terminal can reasonably allocate areas according to the size and number of pixels of the screen, thereby ensuring the accuracy and effectiveness of subsequent operations.

[0040] 202. Determine the number of ROIs according to the resolution; 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 number of pixels on the screen, the degree of refinement of the regional division, and the needs of subsequent testing. According to the resolution, the terminal will decide how many ROIs to divide the screen into so as to perform effective regional division and testing in the next step. 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 ROIs should be added, and the two are positively correlated. In practical applications, the resolution of the screen is generally a typical value (such as 1920*1080, 2560*1440, 3840*2160, etc., and the typical value also includes other values, which are not exhaustive here). The number of different resolutions is very limited. Therefore, the corresponding number of ROIs can be pre-set for each different resolution and stored in the terminal. When applied, the terminal directly searches for the number of ROIs corresponding to the resolution of the standard screen, which reduces the amount of calculation and is conducive to improving efficiency. In a possible embodiment, a calculation formula for resolution and the number of ROIs can be established. After obtaining the resolution of the standard screen, the terminal brings the resolution into the calculation formula to calculate the corresponding number of ROIs. In the calculation formula, the resolution is positively correlated with the number of ROIs, that is, the larger the resolution, the greater the number of ROIs. Through the calculation formula, even if the resolution of the standard screen is not a typical value, the terminal can determine the corresponding number of ROIs, thereby improving adaptability.

[0041] 203. Evenly divide the standard screen according to the number of ROIs to obtain candidate regions equal to the number of ROIs; The terminal evenly 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 based on the total resolution of the screen and the number of ROIs, ensuring that each region on the screen has a similar area and number of pixels. The purpose of this is to ensure that the measurement conditions of each ROI region are as consistent as possible to avoid measurement errors caused by uneven region sizes.

[0042] 204. Randomly generate ROI distribution positions in the candidate regions, wherein each candidate region includes an ROI.

[0043] The terminal randomly generates a ROI position in each candidate area. This random distribution method can avoid deviations from specific rules and make the ROI position more dispersed, thereby covering a wider area during the test. This random distribution ensures the representativeness of the measurement area and helps to improve the accuracy and reliability of subsequent test results. Each candidate area contains an ROI to ensure that each divided area is fully tested.

[0044] 205. Calculate the pixel distance between two adjacent ROIs; The terminal calculates the pixel distance 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 area is reasonable and ensure that there will be no overlap or over-crowding between areas in subsequent tests, which will affect the accuracy of the measurement.

[0045] 206. Determine whether there is an illegal distance in the pixel distance, where the illegal distance is a pixel distance that is less than or equal to a preset threshold; if yes, execute steps 204-206 again.

[0046] The terminal determines whether the pixel distance between adjacent ROIs meets the requirements. If the pixel distance between two adjacent ROIs is less than or equal to the preset threshold, the distance is considered to be an illegal distance, which may cause the measurement area to overlap or be too dense, affecting the test effect. If there is an illegal distance, the terminal will randomly regenerate the position of the ROI and repeat steps 204 and 205 until the pixel distances between all adjacent ROIs meet the requirements.

[0047] In this embodiment, the terminal successfully divides the standard screen into multiple uniform ROI areas, and ensures that these areas are reasonably distributed and the distances between them are appropriate. This process can effectively avoid measurement errors caused by overlapping areas or overly concentrated distribution, and ensure the accuracy and representativeness of subsequent measurements. Through reasonable area division and distribution, various parts of the standard screen can be more comprehensively covered, thereby providing a more accurate and reliable data basis for the measurement results. This method greatly improves the scientific nature of area selection during the measurement process, and helps to improve the reliability of the test and the consistency of the data.

[0048] See also Figure 3 In some embodiments of the present application, step 102 in the above embodiment determines the mapping transformation equation according to the first three stimulus values ​​and the second three stimulus values, and may include the following steps: 301. Apply the first tristimulus value and the second tristimulus value to the mapping transformation formula to calculate various coefficients; The terminal uses the first tristimulus value and the second tristimulus value to calculate a series of coefficients through a known mapping transformation formula. The mapping transformation formula is as follows: Formula 1 in, is the first tristimulus value, is the second and third stimulus value, are the coefficients of each item.

[0049] These coefficients are the core parameters of the mapping transformation equation, which determine how the measurement results of the target measuring instrument are converted to 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.

[0050] Furthermore, in calculating the coefficient It can be solved by the least squares method.

[0051] 302. Apply the calculated coefficients to the mapping transformation formula, and determine the mapping transformation formula after applying the coefficients as a mapping transformation equation.

[0052] 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 to ensure the correspondence between the color data of different instruments. The final mapping transformation equation is an accurate calibration tool for the color of the target screen, which can provide an accurate mathematical basis for data conversion in subsequent steps.

[0053] In this embodiment, the terminal converts the first tristimulus value and the second tristimulus value 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, so that the measurement results of the target measuring instrument can accurately match the data of the standard measuring instrument, ensuring 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.

[0054] See also Figure 4 In the embodiment of the present application, an embodiment of the image stimulation value measuring device includes: A first acquisition unit 401 is used to acquire first tristimulus values ​​obtained by measuring the ROI on the standard screen with a standard measuring instrument, and to acquire second tristimulus values ​​obtained by measuring the ROI with a target measuring instrument; A first determining unit 402, configured to determine a mapping transformation equation according to the first tristimulus values ​​and the second tristimulus values; The second acquisition unit 403 is used to acquire a first image obtained by measuring the target screen with the target measuring instrument, where the first image is a grayscale image or an RGB image; A conversion unit 404, configured to convert the first image into a second image, where the second image is a stimulus value image; A calibration unit 405, configured to calibrate all pixels of the second image according to a mapping transformation equation to obtain a calibrated image; A third acquisition unit 406 is used to acquire a first target area of ​​the first image to be measured; A second determining unit 407 is used to determine a second target area corresponding to the first target area from the calibration image; The generating unit 408 is configured to generate a measurement result according to the second target area.

[0055] 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 three stimulus values ​​of the standard measuring instrument and the target measuring instrument are obtained and compared by the first acquisition unit 401, providing basic data for the subsequent mapping transformation equation. Then, the data of the target measuring instrument is successfully converted into the data of the standard measuring instrument through the derivation and application of the mapping transformation equation by the first determination unit 402, 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 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, so that the target measuring instrument can 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.

[0056] Optionally, the device further comprises: A fourth acquisition unit, used to acquire the resolution of the standard screen; A third determining unit, used to determine the number of ROIs according to the resolution; The fourth determining unit is used to determine the ROI distribution position according to the number of ROIs.

[0057] Optionally, the fourth determining unit is specifically configured to: The standard screen is evenly divided according to the number of ROIs to obtain candidate regions equal to the number of ROIs; ROI distribution positions are randomly generated in the candidate regions, wherein each candidate region includes a ROI.

[0058] Optionally, the device further includes a judgment unit, and the judgment unit is used to: Calculate the pixel distance between two adjacent ROIs; Determine whether there is an illegal distance in the pixel distance, where the illegal distance is a pixel distance that is less than or equal to a preset threshold; If yes, the ROI distribution position is randomly generated again.

[0059] Optionally, the first determining unit 402 is specifically configured to: Apply the first three stimulus values ​​and the second three stimulus values ​​to the mapping transformation formula to calculate the coefficients. The mapping transformation formula is:

[0060] in, is the first tristimulus value, is the second and third stimulus value, are the coefficients of each item; The calculated coefficients are applied to the mapping transformation formula, and the mapping transformation formula after applying the coefficients is determined as the mapping transformation equation.

[0061] Optionally, the generating unit 408 is specifically configured to: The average of the three stimulus values ​​of the second target area is calculated as the measurement result.

[0062] In this implementation, the functions of each unit are the same as those mentioned above. Figures 1 to 3 The steps in the illustrated embodiments correspond to each other and will not be described again here.

[0063] See also Figure 5 In the embodiment of the present application, an embodiment of the image stimulus value measurement system includes: Processor 501, memory 502, input and output unit 503 and bus 504; The processor 501 is connected to the memory 502, the input and output unit 503 and the bus 504; The memory 502 stores a program, and the processor 501 calls the program to execute Figures 1 to 3 Steps in the illustrated embodiment.

[0064] In this embodiment, the function of the processor 501 is the same as that of the aforementioned Figures 1 to 3 The steps in the illustrated embodiments correspond to each other and will not be described again here.

[0065] The embodiment of the present application also provides a computer-readable storage medium on which a program is stored. When the program is executed on a computer, the computer executes the aforementioned Figures 1 to 3 A method in any possible implementation.

[0066] 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 aforementioned method embodiments and will not be repeated here.

[0067] In the several embodiments provided in the present 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0068] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0069] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0070] If 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 the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, read-only memory), random access memory (RAM, random access memory), disk or optical disk and other media that can store program code.

Claims

1. A method for measuring image stimulus value, characterized in that: include: Acquire the first three stimulus values ​​obtained by measuring the ROI on the standard screen with the standard measuring instrument, and acquire the second three stimulus values ​​obtained by measuring the ROI with the target measuring instrument; determining a mapping transformation equation according to the first three stimulus values ​​and the second three stimulus values; Acquire a first image obtained by measuring a target screen with the target measuring instrument, wherein the first image is a grayscale image or an RGB image; converting the first image into a second image, wherein the second image is a stimulus value image; Calibrate all pixel points of the second image according to the mapping transformation equation to obtain a calibrated image; Acquire a first target area of ​​the first image to be measured; determining, from the calibration image, a second target area corresponding to the first target area; A measurement result is generated based on the second target area.

2. The method according to claim 1, characterized in that Before obtaining the first tristimulus values ​​obtained by measuring the ROI on the standard screen with the standard measuring instrument and obtaining the second tristimulus values ​​obtained by measuring the ROI with the target measuring instrument, the method further includes: Get the resolution of the standard screen; Determine the number of ROIs according to the resolution; The ROI distribution positions are determined according to the number of ROIs.

3. The method according to claim 2, characterized in that Determining the ROI distribution position according to the number of ROIs includes: Evenly dividing the standard screen according to the number of ROIs to obtain candidate regions equal to the number of ROIs; ROI distribution positions are randomly generated in the candidate regions, wherein each of the candidate regions includes a ROI.

4. The method according to claim 3, characterized in that After randomly generating ROI distribution positions in the candidate region, the method further includes: Calculate the pixel distance between two adjacent ROIs; Determine whether there is an illegal distance in the pixel distance, the illegal distance being a pixel distance less than or equal to a preset threshold; If yes, the ROI distribution position is randomly generated again.

5. The method according to claim 1, characterized in that Determining a mapping transformation equation according to the first three stimulus values ​​and the second three stimulus values ​​comprises: The first three stimulus values ​​and the second three stimulus values ​​are applied to a mapping transformation formula to calculate various coefficients. The mapping transformation formula is: in, are the first three stimulus values, is the second and third stimulus value, are the coefficients of each item; The calculated coefficients are applied to the mapping transformation formula, and the mapping transformation formula after applying the coefficients is determined as a mapping transformation equation.

6. The method according to claim 5, characterized in that When calculating the various coefficients, the least square method is used to find the solution.

7. The method according to any one of claims 1 to 6, characterized in that Generating a measurement result according to the second target area includes: An average of the three stimulus values ​​of the second target area is calculated as a measurement result.

8. An image stimulation value measuring device, characterized in that: include: A first acquisition unit is used to acquire first tristimulus values ​​obtained by measuring an ROI on a standard screen with a standard measuring instrument, and to acquire second tristimulus values ​​obtained by measuring the ROI with a target measuring instrument; A first determining unit, configured to determine a mapping transformation equation according to the first three stimulus values ​​and the second three stimulus values; A second acquisition unit is used to acquire a first image obtained by measuring the target screen with the target measuring instrument, wherein the first image is a grayscale image or an RGB image; A conversion unit, used for converting the first image into a second image, where the second image is a stimulus value image; A calibration unit, configured to calibrate all pixels of the second image according to the mapping transformation equation to obtain a calibrated image; A third acquisition unit, used for acquiring a first target area of ​​the first image to be measured; a second determining unit, configured to determine, from the calibration image, a second target area corresponding to the first target area; A generating unit is configured to generate a measurement result according to the second target area.

9. An image stimulus value measurement system, characterized in that: include: Processor, memory, input-output unit, and bus; The processor is connected to the memory, the input and output unit, and the bus; A program is stored in the memory, and the processor calls the program to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, 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 7.

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