Distance-based stimulation value calibration method, device and storage medium

Abstract

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 performance of the screen pixels. The method of the present application includes: selecting multiple calibration points on the screen to be tested, and calculating the uncalibrated stimulus value of the calibration point according to the grayscale of the image; using a color analyzer to test and record the standard stimulus value of the calibration point; calculating the calibration coefficients of multiple 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; calculating the first weight coefficient and the 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 the preset value, and determining the pixel calibration coefficient of the pixel point according to the comparison result; calculating the calibrated stimulus value of the pixel point according to the uncalibrated stimulus value and the pixel calibration coefficient of the pixel point.

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 day by day. Color calibration precisely adjusts the color parameters of the display device to ensure that the colors displayed on the screen are as close as possible to the true colors perceived by the human eye, bringing users a more realistic and vivid visual experience.

[0003] In existing technologies, color calibration is typically performed through hardware calibration and software calibration. Hardware calibration focuses on directly adjusting the physical components or parameters within the display device to achieve color optimization. Compared to direct hardware intervention, software calibration uses algorithms and software to adjust the color performance of the display device. This is typically done by measuring the display device's stimulus values at different grayscale levels and then calculating calibration coefficients to adjust the display device's color output.

[0004] However, existing color calibration technologies still have some problems 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 reproduction in some areas. Secondly, when calculating the calibration coefficient, existing calibration technologies usually do 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 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 stimulus value calibration method, device and storage medium.

[0006] The technical solution provided in this application is described below:

[0007] The first aspect of the present application provides a distance-based stimulus value calibration method, comprising:

[0008] Selecting multiple calibration points on the screen to be tested, and calculating the uncalibrated stimulus values of the calibration points according to the image grayscale;

[0009] Use a color analyzer to test and record the standard stimulus values of the calibration points;

[0010] Calculating calibration coefficients of a plurality of calibration points according to the standard stimulus value and the uncalibrated stimulus value;

[0011] Calculating the distance between the pixel point of the screen to be tested and each calibration point;

[0012] Calculating a first weight coefficient and a second weight coefficient based on a first minimum distance and a second minimum distance between the pixel point and each calibration point, respectively, wherein the first weight coefficient is the weight of the first minimum distance in the sum of the two minimum distances, and the second weight coefficient is the weight of the second minimum distance in the sum of the two minimum distances;

[0013] Comparing the first weight coefficient and the second weight coefficient with a preset value, and determining a pixel calibration coefficient of the pixel point according to the comparison result;

[0014] 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.

[0015] Optionally, calculating the distance between the pixel point of the screen to be tested and each calibration point includes:

[0016] 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;

[0017] The distance between the pixel point and the point to be measured is calculated using the following formula 1:

[0018] Formula 1

[0019] Among them, D Pi Indicates 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 y i are the coordinates of the calibration point and i is the index of the calibration point.

[0020] Optionally, after calculating the distances between the pixels of the screen to be tested and the calibration points, the method further includes:

[0021] Storing the distances between the pixel point and all calibration points in a specific data structure;

[0022] The first minimum distance and the second minimum distance are extracted from the specific data structure.

[0023] Optionally, the specific data structure is a distance matrix;

[0024] Extracting the first minimum distance and the second minimum distance from the specific data structure includes:

[0025] Based on the distance matrix, traverse the distance list from each pixel to each calibration point;

[0026] A first minimum distance and a second minimum distance in the distance list are determined and extracted.

[0027] Optionally, the specific data structure is a minimum heap;

[0028] Extracting the first minimum distance and the second minimum distance from the specific data structure includes:

[0029] Access the first top element in the minimum heap, and record the first top element as the first minimum distance;

[0030] 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.

[0031] 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:

[0032] Comparing the first weight coefficient and the second weight coefficient with a preset value;

[0033] If both the first weight coefficient and the second weight coefficient are smaller than a preset value, selecting the calibration point coefficient corresponding to the smaller weight coefficient between the first weight coefficient and the second weight coefficient as the pixel point calibration coefficient of the pixel point;

[0034] If the first weight coefficient is less than a preset value, determining the calibration coefficient of the calibration point corresponding to the first weight coefficient as the pixel point calibration coefficient of the pixel point;

[0035] If the second weight coefficient is less than a preset value, determining the calibration coefficient of the calibration point corresponding to the second weight coefficient as the pixel point calibration coefficient of the pixel point;

[0036] If both the first weight coefficient and the second weight coefficient are greater than the preset value, the pixel point 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.

[0037] Optionally, 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:

[0038] The pixel calibration coefficient is calculated using the following formula 2:

[0039] Formula 2

[0040] Wherein, 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.

[0041] A second aspect of the present application provides a distance-based stimulus value calibration device, comprising:

[0042] A first calculation unit 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;

[0043] A testing unit, configured to test and record the standard stimulus values of the calibration points using a color analyzer;

[0044] 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;

[0045] A third calculation unit is used to calculate the distance between the pixel point of the screen to be tested and each calibration point;

[0046] a fourth calculation unit, configured to calculate a first weight coefficient and a second weight coefficient based on a first minimum distance and a second minimum distance between the pixel point and each calibration point, respectively, 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;

[0047] a determining unit, configured to compare the first weight coefficient and the second weight coefficient with a preset value, and determine a pixel calibration coefficient for the pixel point according to a comparison result;

[0048] The fifth calculation unit is configured 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.

[0049] A third aspect of the present application provides a distance-based stimulus value calibration device, comprising:

[0050] processor, memory, input and output units, and buses;

[0051] The processor is connected to the memory, the input and output unit, and the bus;

[0052] 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 aspects.

[0053] In a fourth aspect, the present application provides a computer-readable storage medium having a program stored thereon. 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 aspects.

[0054] It can be seen from the above technical solutions that this application has the following advantages:

[0055] First, multiple calibration points are selected on the screen, distributed across different areas to ensure the color characteristics of each area are reflected. Then, a high-precision color analyzer is used to test these points and record their standard stimulus values, which represent the color performance of the screen under ideal conditions.

[0056] Using the standard stimulus values at the calibration points and the uncalibrated stimulus values calculated from the image grayscale, we calculate a calibration coefficient for each calibration point. This coefficient reflects the difference between the screen's current color performance and its ideal color performance. By calculating calibration coefficients for multiple calibration points, we can more accurately reflect the color performance at different locations on the screen, providing an accurate data foundation for subsequent pixel calibration.

[0057] By calculating the distance between each pixel and each calibration point and determining the first and second weighting coefficients based on the first and second minimum distances, the system can more flexibly adapt to screen color non-uniformity. By introducing weighting coefficients, different pixel calibration coefficients can be assigned based on the pixel's position relative to the calibration points, thereby more accurately adjusting the pixel's color performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. 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 any creative work.

[0059] Figure 1 A schematic diagram of an embodiment of the distance-based stimulus value calibration method of the present application;

[0060] Figure 2 A schematic diagram of an embodiment of a method for extracting a first minimum distance and a second minimum distance according to the present application;

[0061] Figure 3A schematic diagram of an embodiment of a method for extracting a first minimum distance and a second minimum distance according to the present application;

[0062] 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;

[0063] Figure 5 A schematic diagram of another embodiment of a method for determining pixel calibration coefficients of a pixel according to the present application;

[0064] Figure 6 A schematic structural diagram of a distance-based stimulus value calibration device of the present application;

[0065] Figure 7 This is another structural schematic diagram of the distance-based stimulus value calibration device of the present application. DETAILED DESCRIPTION

[0066] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may 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 avoid obscuring the description of the present application with unnecessary detail.

[0067] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0068] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0069] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" 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 "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0070] 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.

[0071] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0072] Existing color calibration technologies still have some problems 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 reproduction in some areas. Secondly, when calculating the calibration coefficient, existing calibration technologies usually do 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 not only affect the user's visual experience, but may also mislead applications in professional fields.

[0073] 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 performance of screen pixels.

[0074] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0075] The method of the present application can be applied to a server, device, terminal or other device with logic processing capability, and the present application does not limit this. For the convenience of description, the following description is based on the example of the execution subject being a terminal.

[0076] See also Figure 1 The present application provides an embodiment of a distance-based stimulus value calibration method, comprising:

[0077] S101, selecting multiple calibration points on the screen to be tested, and calculating the uncalibrated stimulus values of the calibration points according to the image grayscale;

[0078] On the screen to be tested, select a series of calibration points evenly distributed across the entire screen to ensure a comprehensive representation of the screen's color performance. The number of calibration points depends on the size and resolution of the screen to be tested. Generally speaking, larger screens and higher resolutions require more points to more accurately capture the screen's color characteristics.

[0079] To facilitate subsequent calibration operations, you can mark calibration points by attaching small labels to the screen to be tested or using software to display point markers on the screen. After selecting calibration points, use a professional image acquisition device (such as a camera or scanner) to capture the screen, or use screenshot software to obtain a grayscale image of the screen to be tested. A grayscale image contains only brightness information and not color information. For each selected point, use image processing software to extract the grayscale value corresponding to each pixel from the grayscale image.

[0080] 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 using tools such as spreadsheets and databases to facilitate subsequent data analysis and calibration operations.

[0081] S102. Use a color analyzer to test and record the standard stimulus values of the calibration points;

[0082] In step S101, multiple calibration points have been selected and marked on the screen to be measured. Each selected calibration point is measured using a color analyzer. During the measurement process, the color analyzer automatically records the standard stimulus value for each calibration point. If the measurement result of a calibration point is abnormal, remeasure that point and check whether the color analyzer is functioning properly.

[0083] S103, calculating calibration coefficients of multiple calibration points according to the standard stimulus value and the uncalibrated stimulus value;

[0084] You can choose to calculate the calibration factor, K, using the linear regression equation y = Kx + b. However, in most cases, the calibration process is assumed to be bias-free (i.e., b = 0), so only the slope, K, is calculated as the calibration factor. Substitute the standard stimulus value at 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 factor for the current calibration point.

[0085] S104, calculating the distance between the pixel point of the screen to be tested and each calibration point;

[0086] For any pixel point on the screen to be tested and each calibration point, calculate the distance between the pixel point and the calibration point;

[0087] Use the following formula 1 to calculate the distance between the pixel and the point to be measured:

[0088] Formula 1

[0089] Among them, D Pi Indicates 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 y i are the coordinates of the calibration point and i is the index of the calibration point.

[0090] S105: Calculate a first weight coefficient and a second weight coefficient based on a first minimum distance and a second minimum distance between the pixel point and each calibration point, respectively, where the first weight coefficient is the weight of the first minimum distance in the sum of the two minimum distances, and the second weight coefficient is the weight of the second minimum distance in the sum of the two minimum distances.

[0091] After determining the first and second minimum distances, the weight coefficients need to be calculated using a weight calculation formula. The weight calculation formula can be based on the inverse of the distance, a ratio, or other suitable mathematical model. In this example, the proportional method is used to calculate the weight coefficients, i.e., the first weight coefficient = first minimum distance / (first minimum distance + 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.

[0092] The first and second weight coefficients are calculated using the following formula 3:

[0093] Formula 3

[0094] Wherein, 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.

[0095] When calculating the weight coefficients, you may encounter some special cases, such as when multiple calibration points are equidistant or very close to the pixel. In these cases, additional measures can be taken, such as introducing randomness or selecting a third calibration point that is slightly farther away as a reference, to ensure the rationality of the weight coefficients.

[0096] S106, comparing the first weight coefficient and the second weight coefficient with a preset value, and determining a pixel calibration coefficient of the pixel point according to the comparison result;

[0097] A preset value is a reference standard set during the calibration process, determined based on experience, screen characteristics, or calibration goals. The preset value must take into account screen characteristics such as resolution, color range, and brightness, as well as the desired color accuracy and consistency after calibration. In practice, the preset value can be a fixed value, a range, or a function, depending on the calibration method and goal.

[0098] After obtaining the first weight coefficient and the second weight coefficient of each pixel, they need to be compared with the preset value. According to the comparison result, the calibration coefficient of the pixel can be selected or calculated.

[0099] 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.

[0100] For each pixel, the calibration coefficients are used to transform the uncalibrated stimulus values linearly or through more complex nonlinear transformations. The appropriate mathematical transformation method is selected based on the screen characteristics and calibration objectives. Common transformation methods include linear and nonlinear transformations. Linear transformations involve multiplication and addition operations, while nonlinear transformations involve more complex mathematical functions or models.

[0101] If a linear transformation is chosen, the uncalibrated stimulus value can be simply multiplied by the calibration coefficient to obtain the calibrated stimulus value. If a nonlinear transformation is chosen, the corresponding mathematical function or model must be used for calculation, involving complex processes such as iteration, interpolation, or numerical optimization.

[0102] The calculated calibration stimulus value is stored at the corresponding pixel position.

[0103] In this embodiment, multiple calibration points are carefully selected and distributed across different areas of the screen to ensure that the color characteristics of each area are reflected. Subsequently, a high-precision color analyzer is used to test these points and record their standard stimulus values, which represent the color performance of the screen under ideal conditions.

[0104] Using the standard stimulus values at the calibration points and the uncalibrated stimulus values calculated from the image grayscale, we calculate a calibration coefficient for each calibration point. This coefficient reflects the difference between the screen's current color performance and its ideal color performance. By calculating calibration coefficients for multiple calibration points, we can more accurately reflect the color performance at different locations on the screen, providing an accurate data foundation for subsequent pixel calibration.

[0105] By calculating the distance between each pixel and each calibration point and determining the first and second weighting coefficients based on the first and second minimum distances, the system can more flexibly adapt to screen color non-uniformity. By introducing weighting coefficients, different pixel calibration coefficients can be assigned based on the pixel's position relative to the calibration points, thereby more accurately adjusting the pixel's color performance.

[0106] 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:

[0107] S201, storing the distances between the pixel point and all calibration points in a specific data structure;

[0108] S202: Extract the first minimum distance and the second minimum distance from the specific data structure.

[0109] Choose a data structure suitable for storing distances. Given the large number of screen pixels and the relatively small number of calibration points, consider using a two-dimensional array (one dimension is pixel index and the other is calibration point index) or a priority queue (min heap). A two-dimensional array can intuitively represent the relationship between distances and pixels and calibration points, while a min heap can quickly find minimum distances or perform sorting operations.

[0110] The method for extracting the first minimum distance varies depending on the data structure used to store distances. If using an array, you can iterate through the rows (or columns) of the array, find the minimum value in each row (or column), and then find the global minimum from these. If using a priority queue (min heap), you can directly extract the top element from the queue. This element is the minimum distance value and its corresponding pixel and calibration point information.

[0111] The process for extracting the second minimum distance is similar to that for the first minimum distance, but some additional considerations apply. If using an array, after extracting the first minimum distance, you need to iterate through the remaining elements to find the second minimum value. If using a priority queue (min heap), after extracting the first minimum distance, you can delete the top element from the queue and then retrieve it again. This element will be the second minimum distance value and its corresponding pixel and calibration point information.

[0112] In this embodiment, by storing the distances between pixels and calibration points in a specific data structure, these distance values can be quickly accessed and compared. This avoids recalculating the distances each time they are needed, significantly improving data extraction efficiency. Choosing an appropriate data structure (such as an array or priority queue) can make more efficient use of storage space.

[0113] Extracting the first and second minimum distances allows for rapid location of the calibration point most relevant to the pixel. The process of extracting the minimum distances can be automated, eliminating the need for human intervention. This significantly improves the efficiency of the calibration process and reduces the potential for human error.

[0114] See also Figure 3 The specific data structure may be a distance matrix. The present application provides an embodiment of a method for extracting a first minimum distance and a second minimum distance, the embodiment comprising:

[0115] S301, based on the distance matrix, traverse the distance list from each pixel to each calibration point;

[0116] S302: Determine and extract the first minimum distance and the second minimum distance in the distance list.

[0117] A dual loop is used to traverse the distance matrix: an outer loop iterates over each pixel, and an inner loop iterates over each calibration point. In each inner loop, the distance from the current pixel to the current calibration point is read and added to a temporary distance list, which is used for subsequent minimum distance extraction. The distance list can be cleared or reset after each outer loop to store the distance for the next pixel.

[0118] Iterate through the distance list for each pixel and compare each distance value with the current first and second minimum distances. If a smaller distance value is found, the first minimum distance is updated, and the original first minimum distance becomes the second minimum distance. Store the extracted first and second minimum distances in an appropriate data structure for use in subsequent weight calculations.

[0119] In this embodiment, by traversing the entire distance matrix, we ensure that the distances from each pixel to all calibration points are taken into account without omission. The distances from each pixel to each calibration point are integrated into a distance list to facilitate subsequent processing and analysis. The use of distance lists provides flexibility, allowing for sorting, filtering, and calculations as needed.

[0120] 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.

[0121] 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:

[0122] S401, access the first top element in the minimum heap, and record the first top element as the first minimum distance;

[0123] 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.

[0124] A min heap is a special complete binary tree in which the value of each node is less than or equal to the value of its children. In a min heap, the top element is the minimum value of all the elements in the heap.

[0125] 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 in a variable named firstMinDistance to indicate that it is the first minimum distance.

[0126] Use the poll or pop method to delete the value of the current top element and remove it from the heap. At this time, due to the characteristics of the minimum heap, when the top element is deleted, the heap will automatically adjust its structure to ensure that the new top element is the minimum value among the remaining elements.

[0127] 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.

[0128] In this embodiment, the min-heap data structure allows efficient access to the first minimum distance in the current heap. After deleting the top element, the min-heap automatically adjusts its internal structure to ensure that the new top element is the second minimum distance. This automatic maintenance feature reduces the need for manual sorting or adjusting the heap structure.

[0129] 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:

[0130] S501, comparing the first weight coefficient and the second weight coefficient with a preset value;

[0131] 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 the smaller weight coefficient of the first weight coefficient and the second weight coefficient as a pixel point calibration coefficient of the pixel point;

[0132] 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 calibration coefficient of the pixel point;

[0133] 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 calibration coefficient of the pixel point;

[0134] 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.

[0135] Assuming the preset value is 0.25, compare the first and second weight coefficients to see if they are less than 0.25. If both weight coefficients are less than 0.25, the calibration coefficient of the calibration point corresponding to the smaller of the two weight coefficients is selected as the calibration coefficient for the current pixel. If one of the weight coefficients is less than 0.25, the calibration coefficient for the current pixel is the calibration coefficient of the calibration point corresponding to that weight coefficient. If both weight coefficients are greater than 0.25, a weighted average method is used to calculate the pixel calibration coefficient based on the first and second weight coefficients and the calibration coefficients of their respective corresponding calibration points.

[0136] Use the following formula 2 to calculate the pixel calibration coefficient:

[0137] Formula 2

[0138] Wherein, 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.

[0139] In this embodiment, by comparing the weight coefficients with the preset values, it is possible to accurately determine which calibration points' calibration coefficients contribute more to the pixel calibration coefficients. This precise selection mechanism helps 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 calibration coefficient, simplifying the processing flow. When both weight coefficients are greater than the preset values, the pixel calibration coefficient is calculated using a 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 results. The entire calibration process is automatically performed based on the comparison results of the weight coefficients and the preset values, reducing the need for human intervention and subjective judgment, and improving the objectivity and consistency of the calibration.

[0140] See also Figure 6 , the present application provides a distance-based stimulus value calibration device, comprising:

[0141] The 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;

[0142] A testing unit 602 is used to test and record the standard stimulus values of the calibration points using a color analyzer;

[0143] A second calculation unit 603 is configured to calculate calibration coefficients of a plurality of calibration points based on the standard stimulus value and the uncalibrated stimulus value;

[0144] 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;

[0145] a fourth calculation unit 605 for 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, respectively, where the first weight coefficient is the weight of the first minimum distance in the sum of the two minimum distances, and the second weight coefficient is the weight of the second minimum distance in the sum of the two minimum distances;

[0146] A determination unit 606 is 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 according to the comparison result;

[0147] The fifth calculation unit 607 is configured to calculate a calibrated stimulus value for the pixel point based on the uncalibrated stimulus value for the pixel point and the pixel point calibration coefficient.

[0148] Optionally, the stimulus value calibration device further includes:

[0149] The storage unit 608 stores the distances between the pixel point and all calibration points in a specific data structure;

[0150] The extraction unit 609 extracts the first minimum distance and the second minimum distance from the specific data structure.

[0151] See also Figure 7 , the present application also provides a distance-based stimulus value calibration device, comprising:

[0152] Processor 701 , memory 702 , input / output unit 703 , and bus 704 .

[0153] The processor 701 is connected to the memory 702 , the input / output unit 703 , and the bus 704 .

[0154] The memory 702 stores a program, and the processor 701 calls the program to execute the following Figures 1 to 5 The method in .

[0155] The present application provides a computer-readable storage medium, wherein a program is stored on the computer-readable storage medium, and when the program is executed on a computer, the program performs the following operations: Figures 1 to 5 The method in .

[0156] Those skilled in the art will 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.

[0157] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods, 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 interface, device or unit, which can be electrical, mechanical or other forms.

[0158] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0159] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0160] 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 existing technology, 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 and includes a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute 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, and other media that can store program code.

Claims

1. A distance-based stimulus value calibration method, characterized in that: include: Selecting multiple calibration points on the screen to be tested, and calculating the uncalibrated stimulus values of the calibration points according to the image grayscale; Use a color analyzer to test and record the standard stimulus values of the calibration points; 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; Storing the distances between the pixel point and all calibration points in a specific data structure; Extracting the first minimum distance and the second minimum distance from the specific data structure, specifically, traversing the data in the specific data structure, finding the minimum distance as the first minimum distance, and then traversing the remaining data in the specific data structure, finding the minimum distance as the second minimum distance; Calculating a first weight coefficient and a second weight coefficient based on a first minimum distance and a second minimum distance between the pixel point and each calibration point, respectively, wherein the first weight coefficient is a weight of the first minimum distance to the sum of the first minimum distance and the second minimum distance, and the second weight coefficient is a weight of the second minimum distance to the sum of the first minimum distance and the second minimum distance; Comparing the first weight coefficient and the second weight coefficient with a preset value, and determining a pixel calibration coefficient of the pixel point 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 stimulation value calibration method 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 Indicates 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 y i are the coordinates of the calibration point and i is the index of the calibration point.

3. The stimulation value calibration method according to claim 1, characterized in that: The specific data structure is a distance matrix; Extracting the first minimum distance and the second minimum distance from 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.

4. The stimulation value calibration method according to claim 1, characterized in that: The specific data structure is a minimum heap; Extracting the first minimum distance and the second minimum distance from 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.

5. The stimulation value calibration method according to any one of claims 1 to 4, characterized in that: The 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: Comparing 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 the calibration point coefficient corresponding to the smaller weight coefficient between the first weight coefficient and the second weight coefficient as the pixel point calibration coefficient of the pixel point; If the first weight coefficient is less than a preset value, and the second weight coefficient is not less than a preset value, determining the calibration coefficient of the calibration point corresponding to the first weight coefficient as the pixel point calibration coefficient of the pixel point; If the second weight coefficient is less than a preset value, and the first weight coefficient is not less than a preset value, determining the calibration coefficient of the calibration point corresponding to the second weight coefficient as the pixel point calibration coefficient of the pixel point; If both the first weight coefficient and the second weight coefficient are greater than the preset value, the pixel point 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.

6. The stimulation value calibration method according to claim 5, 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 Wherein, 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.

7. A distance-based stimulus value calibration device, characterized in that: include: A first calculation unit 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, configured to test and record the standard stimulus values of the calibration points 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 is used to calculate the distance between the pixel point of the screen to be tested and each calibration point; A storage unit stores the distances between the pixel point and all calibration points in a specific data structure; an extraction unit, extracting a first minimum distance and a second minimum distance from a specific data structure, specifically traversing data in the specific data structure, finding a minimum distance as the first minimum distance, and then traversing remaining data in the specific data structure, finding a minimum distance as the second minimum distance; 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, the first weight coefficient being a weight of the first minimum distance as a percentage of the sum of the first minimum distance and the second minimum distance, and the second weight coefficient being a weight of the second minimum distance as a percentage of the sum of the first minimum distance and the second minimum distance; a determining unit, configured to compare the first weight coefficient and the second weight coefficient with a preset value, and determine a pixel calibration coefficient for the pixel point according to a comparison result; The fifth calculation unit is configured 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.

8. A distance-based stimulus value calibration device, characterized in that: include: processor, memory, input and output units, and buses; 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 6. 9 . 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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