Stimulus value calibration method and device based on distance and storage medium

Abstract

The invention discloses a distance-based stimulus value calibration method and device and a storage medium, which are used for restoring color characteristics of different areas of a screen and improving the color expression capability of screen pixel points. The method comprises the following steps: selecting a plurality of calibration points on a to-be-tested screen, and calculating uncalibrated stimulus values of the calibration points according to an image gray level; testing and recording a standard stimulation value of the calibration point by using a color analyzer; calculating calibration coefficients of the plurality of calibration points according to the standard stimulation value and the uncalibrated stimulation value; calculating the distance between the pixel point of the to-be-measured screen and each calibration point; respectively calculating a first weight coefficient and a second weight coefficient based on the first minimum distance and the second minimum distance between the pixel point and each calibration point; comparing the first weight coefficient and the second weight coefficient with a preset value, and determining a pixel point calibration coefficient of the pixel point according to a comparison result; and calculating the calibration stimulus value of the pixel point according to the uncalibrated stimulus value of the pixel point and the pixel point calibration coefficient.

Description

Technical Field

[0001] The present application relates to the technical field of color calibration, and in particular to a distance-based stimulus value calibration method, device and storage medium. Background Art

[0002] With the rapid development of display technology, people's requirements for screen color performance are increasing. Color calibration ensures that the colors displayed on the screen are as close as possible to the real colors perceived by the human eye by accurately adjusting the color parameters of the display device, thus bringing users a more realistic and vivid visual enjoyment.

[0003] In existing technologies, color calibration is usually performed through hardware calibration and software calibration. Hardware calibration focuses on directly adjusting the physical components or parameters inside the display device to achieve color optimization. Compared with direct intervention at the hardware level, software calibration adjusts the color performance of the display device through algorithms and software, usually by measuring the stimulus value of the display device at different grayscales, and then adjusting the color output of the display device by calculating the calibration coefficient.

[0004] However, there are still some problems with existing color calibration technologies in practical applications. First, most existing technologies perform global calibration based on the average stimulus value of the entire screen, but the color characteristics of different areas of the screen may be different, so the global average calibration coefficient cannot be applied to all areas. This global calibration method ignores the problem of screen uniformity and is difficult to accurately reflect the color characteristics of each local area of ​​the screen, resulting in inaccurate color restoration in some areas. Secondly, when calculating the calibration coefficient, the existing calibration technology usually does not fully consider the characteristics of the image content itself (such as color distribution, brightness range, etc.), which may lead to deviations between the calibrated color performance and the original color under certain specific image content. These problems will not only affect the user's visual experience, but may also mislead applications in professional fields. Summary of the invention

[0005] In order to solve the above technical problems, the present application provides a distance-based stimulation value calibration method, device and storage medium.

[0006] The technical solution provided in this application is described below: The first aspect of the present application provides a distance-based stimulus value calibration method, comprising: Selecting a plurality of calibration points on the screen to be tested, and calculating the uncalibrated stimulus values ​​of the calibration points according to the image grayscale; Using a color analyzer to test and record the standard stimulus value of the calibration point; Calculating calibration coefficients of a plurality of calibration points according to the standard stimulus value and the uncalibrated stimulus value; Calculating the distance between the pixel point of the screen to be tested and each calibration point; Based on a first minimum distance and a second minimum distance between the pixel point and each calibration point, respectively calculating a first weight coefficient and a second weight coefficient, wherein the first weight coefficient is a weight of the first minimum distance in the sum of the two minimum distances, and the second weight coefficient is a weight of the second minimum distance in the sum of the two minimum distances; Compare the first weight coefficient and the second weight coefficient with a preset value, and determine a pixel calibration coefficient of the pixel according to the comparison result; The calibrated stimulation value of the pixel point is calculated according to the uncalibrated stimulation value of the pixel point and the pixel point calibration coefficient.

[0007] Optionally, calculating the distance between the pixel point of the screen to be tested and each calibration point includes: For any pixel point and each calibration point in the screen to be tested, calculating the distance between the pixel point and the calibration point; The distance between the pixel point and the point to be measured is calculated using the following formula 1: Formula 1 Among them, D Pi Represents the distance from pixel point P(x, y) to calibration point C i (x i ,y i ), x and y are the coordinates of the pixel point, x i and i are the coordinates of the calibration point and i is the index of the calibration point.

[0008] Optionally, after calculating the distance between the pixel point of the screen to be tested and each calibration point, the method further includes: Storing the distances between the pixel point and all calibration points in a specific data structure; The first minimum distance and the second minimum distance are extracted from the specific data structure.

[0009] Optionally, the specific data structure is a distance matrix; Extracting the first minimum distance and the second minimum distance in the specific data structure includes: Based on the distance matrix, traverse the distance list from each pixel to each calibration point; A first minimum distance and a second minimum distance in the distance list are determined and extracted.

[0010] Optionally, the specific data structure is a minimum heap; Extracting the first minimum distance and the second minimum distance in the specific data structure includes: Access the first top element in the minimum heap, and record the first top element as the first minimum distance; Delete the first top element in the minimum heap, obtain the second top element, and record the second top element as the second minimum distance.

[0011] Optionally, comparing the first weight coefficient and the second weight coefficient with a preset value, and determining the pixel calibration coefficient of the pixel according to the comparison result includes: Compare the first weight coefficient and the second weight coefficient with a preset value; If both the first weight coefficient and the second weight coefficient are smaller than a preset value, selecting a calibration point coefficient corresponding to a smaller weight coefficient between the first weight coefficient and the second weight coefficient as a pixel point calibration coefficient for the pixel point; If the first weight coefficient is less than a preset value, determining a calibration coefficient of the calibration point corresponding to the first weight coefficient as a pixel point calibration coefficient of the pixel point; If the second weight coefficient is less than a preset value, determining a calibration coefficient of the calibration point corresponding to the second weight coefficient as a pixel point calibration coefficient of the pixel point; If the first weight coefficient and the second weight coefficient are both greater than the preset value, the pixel calibration coefficient of the pixel point is calculated based on the first weight coefficient, the second weight coefficient, the calibration coefficient of the calibration point corresponding to the first weight coefficient, and the calibration coefficient of the calibration point corresponding to the second weight coefficient.

[0012] Optionally, calculating the pixel calibration coefficient of the pixel according to the first weight coefficient, the second weight coefficient, the calibration coefficient of the calibration point corresponding to the first weight coefficient, and the calibration coefficient of the calibration point corresponding to the second weight coefficient includes: The pixel calibration coefficient is calculated using the following formula 2: Formula 2 Among them, Z represents the pixel calibration coefficient, k1 represents the first weight coefficient, k2 represents the second weight coefficient, K1 represents the calibration point coefficient corresponding to the first minimum distance, and K2 represents the calibration point coefficient corresponding to the second minimum distance.

[0013] A second aspect of the present application provides a distance-based stimulation value calibration device, comprising: A first calculation unit, used for selecting a plurality of calibration points on the screen to be tested, and calculating the uncalibrated stimulus values ​​of the calibration points according to the image grayscale; A testing unit, used for testing and recording the standard stimulus value of the calibration point using a color analyzer; A second calculation unit, configured to calculate calibration coefficients of a plurality of calibration points according to the standard stimulus value and the uncalibrated stimulus value; A third calculation unit, used for calculating the distance between the pixel point of the screen to be tested and each calibration point; a fourth calculation unit, configured to calculate a first weight coefficient and a second weight coefficient respectively based on a first minimum distance and a second minimum distance between the pixel point and each calibration point, wherein the first weight coefficient is a weight of the first minimum distance in the sum of the two minimum distances, and the second weight coefficient is a weight of the second minimum distance in the sum of the two minimum distances; a determination unit, configured to compare the first weight coefficient and the second weight coefficient with a preset value, and determine a pixel calibration coefficient of the pixel point according to the comparison result; The fifth calculation unit is used to calculate the calibrated stimulation value of the pixel point according to the uncalibrated stimulation value of the pixel point and the pixel point calibration coefficient.

[0014] The third aspect of the present application provides a distance-based stimulation value calibration device, comprising: Processor, memory, input-output unit, and bus; The processor is connected to the memory, the input and output unit, and the bus; The memory stores a program, and the processor calls the program to execute the first aspect and the optional stimulation value calibration method of any one of the first aspect.

[0015] A fourth aspect 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 program executes the first aspect and the optional stimulation value calibration method of any one of the first aspect.

[0016] It can be seen from the above technical solutions that this application has the following advantages: First, multiple calibration points are selected on the screen and distributed in different areas of the screen to ensure that the color characteristics of different areas of the screen can be reflected. Then, a high-precision color analyzer is used to test the calibration points and record the standard stimulus values ​​of the calibration points. The standard stimulus values ​​represent the color performance of the screen under ideal conditions.

[0017] The calibration coefficient of each calibration point is calculated using the standard stimulus value of the calibration point and the uncalibrated stimulus value obtained by image grayscale calculation. The calibration coefficient reflects the difference between the current color performance of the screen and the ideal color performance. By calculating the calibration coefficients of multiple calibration points, we can more accurately reflect the color performance of different positions on the screen, providing an accurate data basis for subsequent pixel calibration.

[0018] By calculating the distance between each pixel and each calibration point, and determining the first weight coefficient and the second weight coefficient according to the first minimum distance and the second minimum distance, the non-uniformity of the screen color can be more flexibly adapted. By introducing the weight coefficient, different pixel calibration coefficients can be given according to the position of the pixel relative to the calibration point, so as to more accurately adjust the color performance of the pixel. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0020] Figure 1 A schematic diagram of an embodiment of a distance-based stimulation value calibration method of the present application; Figure 2 A schematic diagram of an embodiment of a method for extracting a first minimum distance and a second minimum distance in the present application; Figure 3 A schematic diagram of an embodiment of a method for extracting a first minimum distance and a second minimum distance in the present application; Figure 4 A schematic diagram of another embodiment of a method for extracting a first minimum distance and a second minimum distance according to the present application; Figure 5 A schematic diagram of another embodiment of a method for determining pixel calibration coefficients of a pixel in the present application; Figure 6 A schematic diagram of the structure of the distance-based stimulation value calibration device of the present application; Figure 7 This is another structural schematic diagram of the distance-based stimulation value calibration device of the present application. DETAILED DESCRIPTION

[0021] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0022] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0023] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0024] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0025] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0026] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0027] There are still some problems with existing color calibration technologies in practical applications. First, most existing technologies perform global calibration based on the average stimulus value of the entire screen, but the color characteristics of different areas of the screen may be different, so the global average calibration coefficient cannot be applied to all areas. This global calibration method ignores the problem of screen uniformity and makes it difficult to accurately reflect the color characteristics of each local area of ​​the screen, resulting in inaccurate color restoration in some areas. Secondly, when calculating the calibration coefficient, the existing calibration technology usually does not fully consider the characteristics of the image content itself (such as color distribution, brightness range, etc.), which may lead to deviations between the calibrated color performance and the original color under certain specific image content. These problems will not only affect the user's visual experience, but may also mislead applications in professional fields.

[0028] Based on this, the present application discloses a distance-based stimulus value calibration method, device and storage medium for restoring the color characteristics of different areas of the screen and improving the color expression capability of screen pixels.

[0029] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0030] The method of the present application can be applied to a server, a device, 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.

[0031] See also Figure 1 The present application provides an embodiment of a distance-based stimulus value calibration method, comprising: S101, selecting a plurality of calibration points on the screen to be tested, and calculating the uncalibrated stimulus values ​​of the calibration points according to the image grayscale; On the screen to be tested, select a series of calibration points that are evenly distributed across the entire screen to ensure that the screen's color performance is fully reflected. The number of calibration points can be determined based on the size and resolution of the screen to be tested. Generally speaking, the larger the screen size and the higher the resolution, the more points are required to more accurately capture the screen's color characteristics.

[0032] To facilitate subsequent calibration operations, you can mark calibration points by attaching small labels to the screen to be tested, using software to display point marks on the screen, etc. After selecting the calibration points, use professional image acquisition equipment (such as cameras, scanners, etc.) to shoot the screen, or use screenshot software to obtain a grayscale image of the screen to be tested. A grayscale image refers to an image that contains only brightness information but not color information. For each selected point, use image processing software to extract the grayscale value corresponding to each pixel from the grayscale image.

[0033] Based on the extracted grayscale values, a specific algorithm or formula is used to calculate the uncalibrated stimulus value for each point. After calculating the uncalibrated stimulus value for each calibration point, these uncalibrated stimulus values ​​are recorded. This can be achieved by using tools such as spreadsheets and databases to facilitate subsequent data analysis and calibration operations.

[0034] S102, using a color analyzer to test and record the standard stimulus value of the calibration point; According to step S101, multiple calibration points have been selected on the screen to be tested and marked. Each selected calibration point is measured using a color analyzer. During the measurement process, the color analyzer automatically records the standard stimulus value of each calibration point. If the measurement result of a calibration point is found to be abnormal, the calibration point can be re-measured and the color analyzer can be checked for normal operation.

[0035] S103, calculating calibration coefficients of multiple calibration points according to the standard stimulus value and the uncalibrated stimulus value; You can choose to use the linear regression equation y=Kx+b to calculate the calibration coefficient K. However, in most cases, it is assumed that the calibration process is bias-free (i.e., b=0), so only the slope K needs to be calculated as the calibration coefficient. Substitute the standard stimulus value of the calibration point as x and the uncalibrated stimulus value as y into the linear regression equation and calculate the slope K. The calculated slope K is the calibration coefficient of the current calibration point.

[0036] S104, calculating the distance between the pixel point of the screen to be tested and each calibration point; For any pixel point and each calibration point in the screen to be tested, the distance between the pixel point and the calibration point is calculated; Use the following formula 1 to calculate the distance between the pixel and the point to be measured: Formula 1 Among them, D Pi Represents the distance from pixel point P(x, y) to calibration point C i (x i ,y i ), x and y are the coordinates of the pixel point, x i andi are the coordinates of the calibration point and i is the index of the calibration point.

[0037] S105, based on the first minimum distance and the second minimum distance between the pixel point and each calibration point, respectively calculate a first weight coefficient and a second weight coefficient, the first weight coefficient being the weight of the first minimum distance in the sum of the two minimum distances, and the second weight coefficient being the weight of the second minimum distance in the sum of the two minimum distances; After determining the first minimum distance and the second minimum distance, the weight coefficient needs to be calculated according to the weight calculation formula. The weight calculation formula can be based on the inverse of the distance, the ratio or other suitable mathematical models. In this example, the proportion method is used to calculate the weight coefficient, that is, the first weight coefficient = the first minimum distance / (the first minimum distance + the second minimum distance), and the second weight coefficient is correspondingly equal to the ratio of the second minimum distance to the sum of the two. The advantage of this calculation method is that it is simple and intuitive, and can better reflect the distance relationship between the pixel point and the calibration point.

[0038] The first weight coefficient and the second weight coefficient are calculated using the following formula 3: Formula 3 Among them, k1 represents the first weight coefficient, k2 represents the second weight coefficient, D1 represents the first minimum distance, and D2 represents the second minimum distance.

[0039] In the process of calculating the weight coefficient, some special cases may be encountered, such as multiple calibration points being equal or very close to the pixel point. For these cases, additional processing measures can be taken, such as introducing randomness, selecting a third calibration point that is slightly farther away as a reference, etc., to ensure the rationality of the weight coefficient.

[0040] S106, comparing the first weight coefficient, the second weight coefficient and a preset value, and determining a pixel calibration coefficient of the pixel according to the comparison result; The preset value is a reference standard set during the calibration process, which is determined based on experience, screen characteristics or calibration goals. The setting of the preset value needs to take into account the characteristics of the screen such as resolution, color range, brightness, and the color accuracy and consistency goals that are expected to be achieved after calibration. In actual operation, the preset value can be a fixed value, a range or a function relationship, depending on the calibration method and goal.

[0041] After obtaining the first weight coefficient and the second weight coefficient of each pixel, it is necessary to compare them with the preset value. According to the comparison result, the calibration coefficient of the pixel can be selected or calculated.

[0042] S107 , calculating a calibrated stimulus value of the pixel point according to the uncalibrated stimulus value of the pixel point and the pixel point calibration coefficient.

[0043] For each pixel, the calibration coefficient is used to perform a linear transformation or a more complex nonlinear transformation on the uncalibrated stimulus value. The appropriate mathematical transformation method is selected based on the screen characteristics and calibration goals. Common transformation methods include linear transformation and nonlinear transformation. Linear transformation involves multiplication and addition operations, while nonlinear transformation involves more complex mathematical functions or models.

[0044] If a linear transformation is selected, the uncalibrated stimulus value can be simply multiplied by the calibration coefficient to obtain the calibrated stimulus value. If a nonlinear transformation is selected, the corresponding mathematical function or model needs to be used for calculation, which involves complex processes such as iteration, interpolation or numerical optimization.

[0045] The calculated calibration stimulus value is stored at the corresponding pixel position.

[0046] In this embodiment, multiple calibration points on the screen are carefully selected and distributed in different areas of the screen to ensure that the color characteristics of different areas of the screen can be reflected. Subsequently, the calibration points are tested using a high-precision color analyzer to record the standard stimulus values ​​of the calibration points, which represent the color performance of the screen in an ideal state.

[0047] The calibration coefficient of each calibration point is calculated using the standard stimulus value of the calibration point and the uncalibrated stimulus value obtained by image grayscale calculation. The calibration coefficient reflects the difference between the current color performance of the screen and the ideal color performance. By calculating the calibration coefficients of multiple calibration points, we can more accurately reflect the color performance of different positions on the screen, providing an accurate data basis for subsequent pixel calibration.

[0048] By calculating the distance between each pixel and each calibration point, and determining the first weight coefficient and the second weight coefficient according to the first minimum distance and the second minimum distance, the non-uniformity of the screen color can be more flexibly adapted. By introducing the weight coefficient, different pixel calibration coefficients can be given according to the position of the pixel relative to the calibration point, so as to more accurately adjust the color performance of the pixel.

[0049] See also Figure 2 , the present application provides an embodiment of a method for extracting a first minimum distance and a second minimum distance, the embodiment comprising: S201, storing the distances between the pixel point and all calibration points in a specific data structure; S202: Extract a first minimum distance and a second minimum distance from a specific data structure.

[0050] Choose a data structure suitable for storing distances. Considering the large number of screen pixels and the relatively small number of calibration points, you can choose to use a two-dimensional array (one dimension is the pixel index and the other dimension is the calibration point index) or a priority queue (minimum heap). The two-dimensional array can intuitively show the relationship between distance and pixels and calibration points, and the minimum heap can quickly find the minimum distance or perform sorting operations.

[0051] Depending on the data structure chosen when storing distances, the method for extracting the first minimum distance will be different. If an array is used, you can traverse the rows (or columns) of the array, find the minimum value in each row (or column), and then find the global minimum from it. If a priority queue (min heap) is used, you can directly take the top element from the queue, which is the minimum distance value and its corresponding pixel and calibration point information.

[0052] The process of extracting the second minimum distance is similar to extracting the first minimum distance, but some additional factors need to be considered. If an array is used, after extracting the first minimum distance, the remaining elements need to be traversed to find the second minimum value. If a priority queue (min heap) is used, after extracting the first minimum distance, the top element of the heap can be deleted from the queue, and then the top element of the heap can be taken out of the queue again. This element is the second minimum distance value and its corresponding pixel and calibration point information.

[0053] In this embodiment, by storing the distances between the pixel points and the calibration points in a specific data structure, these distance values ​​can be quickly accessed and compared. This avoids recalculating the distance each time it is needed, thereby greatly improving the efficiency of data extraction. Choosing a suitable data structure (such as an array or a priority queue) can make more efficient use of storage space.

[0054] Extracting the first minimum distance and the second minimum distance can quickly locate the calibration point information most relevant to the pixel point. The process of extracting the minimum distance can be automated without human intervention. This greatly improves the efficiency of the calibration process and reduces the possibility of human error.

[0055] See also Figure 3 , the specific data structure may be a distance matrix, and the present application provides an embodiment of a method for extracting a first minimum distance and a second minimum distance, the embodiment comprising: S301, based on the distance matrix, traverse the distance list from each pixel to each calibration point; S302: Determine and extract the first minimum distance and the second minimum distance in the distance list.

[0056] Use a double loop method to traverse the distance matrix, the outer loop traverses each pixel, and the inner loop traverses each calibration point. In each inner loop, read the distance from the current pixel to the current calibration point and add it to a temporary distance list, which will be used for subsequent minimum distance extraction. The distance list can be cleared or reset after each outer loop to store the distance of the next pixel.

[0057] Traverse the distance list of each pixel point and compare each distance value with the current first minimum distance and second minimum distance. If a smaller distance value is found, update the first minimum distance and the original first minimum distance will become the second minimum distance. Store the extracted first minimum distance and second minimum distance into appropriate data structures for subsequent calculation of weight coefficients.

[0058] In this embodiment, by traversing the entire distance matrix, it is possible to ensure that the distance from each pixel to all calibration points is taken into account without omission. The distance from each pixel to each calibration point is integrated into a distance list for subsequent processing and analysis. The use of the distance list provides flexibility, and operations such as sorting, filtering, or calculation can be performed on it as needed.

[0059] The process of extracting the minimum distance is automated, reducing the possibility of human intervention and error, and improving the reliability and consistency of the calibration.

[0060] See also Figure 4 , the specific data structure may be a minimum heap, and the present application provides another embodiment of a method for extracting a first minimum distance and a second minimum distance, the embodiment comprising: S401, access the first top element in the minimum heap, and record the first top element as the first minimum distance; S402, delete the first top element in the minimum heap, obtain the second top element, and record the second top element as the second minimum distance.

[0061] A min heap is a special complete binary tree where the value of each node is less than or equal to the value of its child nodes. In a min heap, the top element is the minimum value of all the elements in the heap.

[0062] Use the peek or top method (depending on the programming language and data structure library used) to return the value of the top element of the heap. Record the value of the returned top element of the heap as a variable named firstMinDistance to indicate that it is the first minimum distance.

[0063] Use the poll or pop method to delete the value of the current top element of the heap and remove it from the heap. At this time, due to the characteristics of the minimum heap, when the top element of the heap is deleted, the heap will automatically adjust its structure to ensure that the new top element of the heap is the minimum value of the remaining elements.

[0064] Call the peek or top method again to access the new top element of the heap and record its value as a variable named secondMinDistance to indicate that it is the second minimum distance.

[0065] In this embodiment, the minimum heap data structure can be used to efficiently access the first minimum distance in the current heap; after deleting the top element of the heap, the minimum heap can automatically adjust its internal structure to ensure that the new top element of the heap is the second minimum distance. This automatic maintenance feature reduces the need for manual sorting or adjustment of the heap structure.

[0066] See also Figure 5 The present application provides another embodiment of a method for determining a pixel calibration coefficient of a pixel, the embodiment comprising: S501, comparing the first weight coefficient and the second weight coefficient with a preset value; S502: If both the first weight coefficient and the second weight coefficient are smaller than a preset value, selecting a calibration point coefficient corresponding to a smaller weight coefficient of the first weight coefficient and the second weight coefficient as a pixel point calibration coefficient of the pixel point; S503: If the first weight coefficient is less than a preset value, determine the calibration coefficient of the calibration point corresponding to the first weight coefficient as the pixel point calibration coefficient of the pixel point; S504: If the second weight coefficient is less than a preset value, determine the calibration coefficient of the calibration point corresponding to the second weight coefficient as the pixel point calibration coefficient of the pixel point; S505. If both the first weight coefficient and the second weight coefficient are greater than the preset value, the pixel calibration coefficient of the pixel point is calculated according to the first weight coefficient, the second weight coefficient, the calibration coefficient of the calibration point corresponding to the first weight coefficient, and the calibration coefficient of the calibration point corresponding to the second weight coefficient.

[0067] Assuming the preset value is 0.25, compare the first weight coefficient and the second weight coefficient to see if they are less than 0.25. If both weight coefficients are less than 0.25, select the calibration coefficient of the calibration point corresponding to the smaller weight coefficient of the two weight coefficients as the calibration coefficient of the current pixel; if one of the weights is less than 0.25, the calibration coefficient of the current pixel is taken as the calibration coefficient of the calibration point corresponding to the weight coefficient. If both are greater than 0.25, use the weighted average method to calculate the pixel calibration coefficient of the pixel based on the first weight coefficient, the second weight coefficient and the calibration coefficients of their respective corresponding calibration points.

[0068] Use the following formula 2 to calculate the pixel calibration coefficient: Formula 2 Among them, Z represents the pixel calibration coefficient, k1 represents the first weight coefficient, k2 represents the second weight coefficient, K1 represents the calibration point coefficient corresponding to the first minimum distance, and K2 represents the calibration point coefficient corresponding to the second minimum distance.

[0069] In this embodiment, by comparing the weight coefficient with the preset value, it is possible to accurately determine which calibration points' calibration coefficients contribute more to the pixel point calibration coefficient. This precise selection mechanism helps to improve the accuracy of calibration. When the weight coefficient is small, the calibration coefficient of the calibration point corresponding to the smaller weight coefficient is directly selected as the pixel point calibration coefficient, which simplifies the processing flow. When both weight coefficients are greater than the preset value, the pixel point calibration coefficient is calculated using the weighted average method, which fully considers the contribution of the calibration coefficients of the two calibration points and improves the reliability and stability of the calibration result. The entire calibration process is automatically performed based on the comparison result of the weight coefficient and the preset value, which reduces the need for human intervention and subjective judgment and improves the objectivity and consistency of the calibration.

[0070] See also Figure 6 , the present application provides a distance-based stimulus value calibration device, comprising: A first calculation unit 601 is used to select a plurality of calibration points on the screen to be tested, and calculate the uncalibrated stimulus values ​​of the calibration points according to the image grayscale; A testing unit 602, used to test and record the standard stimulus value of the calibration point using a color analyzer; A second calculation unit 603 is used to calculate calibration coefficients of multiple calibration points according to the standard stimulus value and the uncalibrated stimulus value; The third calculation unit 604 is used to calculate the distance between the pixel point of the screen to be tested and each calibration point; A fourth calculation unit 605 is used to calculate a first weight coefficient and a second weight coefficient respectively based on a first minimum distance and a second minimum distance between the pixel point and each calibration point, wherein the first weight coefficient is a weight of the first minimum distance in the sum of the two minimum distances, and the second weight coefficient is a weight of the second minimum distance in the sum of the two minimum distances; A determination unit 606, configured to compare the first weight coefficient, the second weight coefficient and a preset value, and determine a pixel calibration coefficient of the pixel according to the comparison result; The fifth calculation unit 607 is used to calculate the calibrated stimulation value of the pixel point according to the uncalibrated stimulation value of the pixel point and the pixel point calibration coefficient.

[0071] Optionally, the stimulus value calibration device further includes: A storage unit 608 stores the distances between the pixel point and all calibration points in a specific data structure; The extraction unit 609 extracts the first minimum distance and the second minimum distance from the specific data structure.

[0072] See also Figure 7 The present application also provides a distance-based stimulation value calibration device, comprising: Processor 701 , memory 702 , input / output unit 703 , and bus 704 .

[0073] The processor 701 is connected to the memory 702 , the input and output unit 703 , and the bus 704 .

[0074] The memory 702 stores a program, and the processor 701 calls the program to execute the following steps: Figures 1 to 5 The method in .

[0075] The present application provides a computer-readable storage medium, on which a program is stored, and when the program is executed on a computer, the program performs the following steps: Figures 1 to 5 The method in .

[0076] 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.

[0077] 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 units is only a logical function division. There may be other division methods 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 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.

[0078] 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.

[0079] 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.

[0080] 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, 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 a number of instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the various embodiments 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, etc. Various media that can store program codes.

Claims

1. A distance-based stimulus value calibration method, characterized in that: include: Selecting a plurality of calibration points on the screen to be tested, and calculating the uncalibrated stimulus values ​​of the calibration points according to the image grayscale; Using a color analyzer to test and record the standard stimulus value of the calibration point; Calculating calibration coefficients of a plurality of calibration points according to the standard stimulus value and the uncalibrated stimulus value; Calculating the distance between the pixel point of the screen to be tested and each calibration point; Based on a first minimum distance and a second minimum distance between the pixel point and each calibration point, respectively calculating a first weight coefficient and a second weight coefficient, wherein the first weight coefficient is a weight of the first minimum distance in the sum of the two minimum distances, and the second weight coefficient is a weight of the second minimum distance in the sum of the two minimum distances; Compare the first weight coefficient and the second weight coefficient with a preset value, and determine a pixel calibration coefficient of the pixel according to the comparison result; The calibrated stimulation value of the pixel point is calculated according to the uncalibrated stimulation value of the pixel point and the pixel point calibration coefficient.

2. The method for calibrating stimulus values ​​according to claim 1, characterized in that: The calculating the distance between the pixel point of the screen to be tested and each calibration point includes: For any pixel point and each calibration point in the screen to be tested, calculating the distance between the pixel point and the calibration point; The distance between the pixel point and the point to be measured is calculated using the following formula 1: Formula 1 Among them, D Pi Represents the distance from pixel point P(x, y) to calibration point C i (x i ,y i ), x and y are the coordinates of the pixel point, x i and i are the coordinates of the calibration point and i is the index of the calibration point.

3. The method for calibrating stimulus values ​​according to claim 1, characterized in that: After calculating the distance between the pixel points of the screen to be tested and each calibration point, the method further includes: Storing the distances between the pixel point and all calibration points in a specific data structure; The first minimum distance and the second minimum distance are extracted from the specific data structure.

4. The method for calibrating stimulus values ​​according to claim 3, characterized in that: The specific data structure is a distance matrix; Extracting the first minimum distance and the second minimum distance in the specific data structure includes: Based on the distance matrix, traverse the distance list from each pixel to each calibration point; A first minimum distance and a second minimum distance in the distance list are determined and extracted.

5. The method for calibrating stimulus values ​​according to claim 3, characterized in that: The specific data structure is a minimum heap; Extracting the first minimum distance and the second minimum distance in the specific data structure includes: Access the first top element in the minimum heap, and record the first top element as the first minimum distance; Delete the first top element in the minimum heap, obtain the second top element, and record the second top element as the second minimum distance.

6. The stimulation value calibration method according to any one of claims 1 to 5, characterized in that: The comparing the first weight coefficient, the second weight coefficient and a preset value, and determining the pixel calibration coefficient of the pixel according to the comparison result, comprises: Compare the first weight coefficient and the second weight coefficient with a preset value; If both the first weight coefficient and the second weight coefficient are smaller than a preset value, selecting a calibration point coefficient corresponding to a smaller weight coefficient between the first weight coefficient and the second weight coefficient as a pixel point calibration coefficient for the pixel point; If the first weight coefficient is less than a preset value, determining a calibration coefficient of the calibration point corresponding to the first weight coefficient as a pixel point calibration coefficient of the pixel point; If the second weight coefficient is less than a preset value, determining a calibration coefficient of the calibration point corresponding to the second weight coefficient as a pixel point calibration coefficient of the pixel point; If the first weight coefficient and the second weight coefficient are both greater than the preset value, the pixel calibration coefficient of the pixel point is calculated based on the first weight coefficient, the second weight coefficient, the calibration coefficient of the calibration point corresponding to the first weight coefficient, and the calibration coefficient of the calibration point corresponding to the second weight coefficient.

7. The method for calibrating stimulus values ​​according to claim 6, characterized in that: The calculating the pixel calibration coefficient of the pixel point according to the first weight coefficient, the second weight coefficient, the calibration coefficient of the calibration point corresponding to the first weight coefficient, and the calibration coefficient of the calibration point corresponding to the second weight coefficient includes: The pixel calibration coefficient is calculated using the following formula 2: Formula 2 Among them, Z represents the pixel calibration coefficient, k1 represents the first weight coefficient, k2 represents the second weight coefficient, K1 represents the calibration point coefficient corresponding to the first minimum distance, and K2 represents the calibration point coefficient corresponding to the second minimum distance.

8. A distance-based stimulation value calibration device, characterized in that: include: A first calculation unit, used for selecting a plurality of calibration points on the screen to be tested, and calculating the uncalibrated stimulus values ​​of the calibration points according to the image grayscale; A testing unit, used for testing and recording the standard stimulus value of the calibration point using a color analyzer; A second calculation unit, configured to calculate calibration coefficients of a plurality of calibration points according to the standard stimulus value and the uncalibrated stimulus value; A third calculation unit, used for calculating the distance between the pixel point of the screen to be tested and each calibration point; a fourth calculation unit, configured to calculate a first weight coefficient and a second weight coefficient respectively based on a first minimum distance and a second minimum distance between the pixel point and each calibration point, wherein the first weight coefficient is a weight of the first minimum distance in the sum of the two minimum distances, and the second weight coefficient is a weight of the second minimum distance in the sum of the two minimum distances; a determination unit, configured to compare the first weight coefficient and the second weight coefficient with a preset value, and determine a pixel calibration coefficient of the pixel point according to the comparison result; The fifth calculation unit is used to calculate the calibrated stimulation value of the pixel point according to the uncalibrated stimulation value of the pixel point and the pixel point calibration coefficient.

9. A distance-based stimulation value calibration device, 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; The memory stores a program, and the processor calls the program to execute the stimulation value calibration method according to any one of claims 1 to 7. 10 . A computer-readable storage medium having a program stored thereon, wherein when the program is executed on a computer, the stimulation value calibration method according to claim 1 is executed.

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