Image processing method and device, display equipment and storage medium
By calculating the optimal estimated current temperature of each pixel in the MLED splicing screen and combining it with historical display images and temperature sensor detection values, the problem of uneven display caused by temperature sensor measurement errors was solved, thus improving the display effect.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- BOE TECHNOLOGY GROUP CO LTD
- Filing Date
- 2024-10-31
- Publication Date
- 2026-05-01
AI Technical Summary
During the display process, MLED video walls may exhibit inconsistent display effects at different temperatures due to inaccurate or erroneous temperature sensor measurements, resulting in a bluish or greenish tint. Existing compensation methods are limited.
By acquiring the current pixel impact value, current temperature detection value, previous temperature prediction value, and temperature gain of each pixel on the display screen, the optimal temperature estimate is calculated, and the pixel data of the image to be displayed is compensated. The current temperature is estimated by combining historical pixel data and temperature sensor detection values.
It improves the accuracy of temperature prediction, enhances the display effect, and reduces display unevenness.
Smart Images
Figure CN121963629A_ABST
Abstract
Description
Image processing methods, apparatus, display devices and storage media Technical Field
[0001] This disclosure relates to the field of display technology, and in particular to an image processing method, apparatus, display device, and storage medium. Background Technology
[0002] MLED video walls have the advantages of high brightness, high contrast and adjustable size, and are widely used in many scenarios, such as outdoor environments.
[0003] During the display process, the pixel devices of the MLED video wall are illuminated, causing their temperature to rise. This temperature increase affects the luminous efficiency of the light-emitting materials within the pixel device. Considering that the pixel device includes RGB sub-pixel devices, the impact of temperature rise on the three sub-pixel devices of the same pixel differs, resulting in different display effects when displaying the same image at different temperatures.
[0004] Referring to Figure 1, (a) illustrates that different content is displayed in different areas of the MLED splicing screen. After a period of display, the temperature on both sides of the screen rises. When the screen temperature is high, the red sub-pixel device is more affected while the blue and green sub-pixel devices are less affected. In the subsequent display process, the displayed image may exhibit a bluish or greenish tint as illustrated in area 11 of Figure (b).
[0005] Existing solutions typically incorporate temperature sensors within the pixel devices to detect their current temperature and compensate for the image to be displayed based on this temperature reading. However, temperature sensors may be inaccurate or contain measurement errors, which limits their ability to improve display quality. Summary of the Invention
[0006] This disclosure provides an image processing method, apparatus, display device, and storage medium.
[0007] According to a first aspect of this disclosure, an image processing method is provided, the method comprising:
[0008] Obtain the current pixel impact value of each pixel on the display screen at the current moment, the current temperature detection value of each pixel at the current moment, the previous temperature prediction value of each pixel at the previous moment, and the temperature gain.
[0009] The optimal temperature estimate for the current moment is obtained based on the current pixel impact value, the current temperature detection value, the previous temperature prediction value, and the temperature gain.
[0010] The pixel data of the image to be displayed is compensated based on the optimal estimate of the current temperature to obtain the target display image, which is then input to the display screen for display.
[0011] Optionally, obtaining the optimal temperature estimate for the current moment based on the current pixel influence value, the current temperature detection value, the previous temperature prediction value, and the temperature gain includes:
[0012] The current temperature prediction value is determined based on the current pixel influence value and the previous temperature prediction value.
[0013] The optimal temperature estimate for the current moment is determined based on the current temperature prediction value, the current temperature detection value, and the temperature gain.
[0014] Optionally, determining the current temperature prediction value at the current moment based on the current pixel influence value and the previous temperature prediction value includes:
[0015] The heating acceleration, heating rate, and initial temperature value of the pixel device are obtained;
[0016] The first temperature difference between the current pixel influence value and the previous temperature prediction value is obtained, and the power value of the first temperature difference is obtained; the power value is obtained with the first temperature difference as the base and the heating acceleration.
[0017] Obtain the product of the power value and the heating rate, and obtain the sum of the product and the initial temperature value, and use the sum as the current temperature prediction value.
[0018] Optionally, determining the optimal temperature estimate for the current moment based on the current temperature prediction value, the current temperature detection value, and the current temperature weighting coefficient includes:
[0019] Obtain the second temperature difference between the current detected temperature value and the current predicted temperature value;
[0020] Obtain the product of the second temperature difference and the temperature gain, and obtain the sum of the product and the current temperature prediction value as the optimal temperature estimate for the current moment.
[0021] Optionally, obtaining the temperature gain includes:
[0022] Obtain the temperature estimation variance and first weight of the previous moment, and obtain the current error variance and second weight corresponding to the current temperature detection value; the first weight represents the influence of the temperature estimation variance of the previous moment on the optimal temperature estimate, and the second weight represents the influence of the current error variance on the optimal temperature estimate;
[0023] A mapping relationship equivalent to the first weight is constructed based on the temperature estimation variance, the current error variance, and the second weight, which serves as the temperature gain; the temperature gain represents the change of the first weight when the current error variance is fixed and the temperature estimation variance changes.
[0024] According to a second aspect of this disclosure, an image processing apparatus is provided, the apparatus comprising:
[0025] The data acquisition module is used to acquire the current pixel influence value of each pixel on the display screen at the current moment, the current temperature detection value of each pixel at the current moment, the previous temperature prediction value of each pixel at the previous moment, and the temperature gain.
[0026] The optimal temperature estimation module is used to obtain the optimal temperature estimate at the current moment based on the current pixel influence value, the current temperature detection value, the previous temperature prediction value, and the temperature gain.
[0027] The pixel data compensation module is used to compensate the pixel data of the image to be displayed based on the optimal estimate of the current temperature to obtain the target display image, which is used to input to the display screen for display.
[0028] Optionally, the optimal temperature estimation module includes:
[0029] The current temperature prediction submodule is used to determine the current temperature prediction value at the current moment based on the current pixel influence value and the previous temperature prediction value.
[0030] The optimal temperature estimation submodule is used to determine the optimal temperature estimate for the current moment based on the current temperature prediction value, the current temperature detection value, and the temperature gain.
[0031] Optionally, the current temperature prediction submodule includes:
[0032] The temperature parameter acquisition unit is used to acquire the heating acceleration, heating rate and initial temperature value of the pixel device;
[0033] The temperature power value acquisition unit is used to acquire the first temperature difference between the current pixel influence value and the previous temperature prediction value, and to obtain the power value of the first temperature difference; the power value is obtained with the first temperature difference as the base and the heating acceleration as the power.
[0034] The current temperature prediction unit is used to obtain the product of the power value and the heating rate, and to obtain the sum of the product and the initial temperature value, and to use the sum as the current temperature prediction value.
[0035] Optionally, the optimal temperature estimation submodule includes:
[0036] A temperature difference acquisition unit is used to acquire a second temperature difference between the current temperature detection value and the current temperature prediction value;
[0037] The optimal temperature estimation unit is used to obtain the product of the second temperature difference and the temperature gain, and to obtain the sum of the product and the current temperature prediction value as the optimal temperature estimate value at the current moment.
[0038] Optionally, the temperature gain acquisition unit includes:
[0039] The first weight acquisition subunit is used to acquire the temperature estimation variance and the first weight at the previous time. The first weight represents the influence of the temperature estimation variance at the previous time on the optimal temperature estimate.
[0040] The second weight acquisition subunit is used to acquire the current error variance and the second weight corresponding to the current temperature detection value; the second weight represents the influence of the current error variance on the optimal temperature estimate.
[0041] The temperature gain acquisition subunit is used to construct a mapping relationship equivalent to the first weight based on the temperature estimation variance, the current error variance, and the second weight, as the temperature gain; the temperature gain represents the change of the first weight when the current error variance is fixed and the temperature estimation variance changes.
[0042] According to a third aspect of this disclosure, a display device is provided, comprising:
[0043] Display screen, processor, and memory;
[0044] The memory is used to store computer programs that can be executed by the processor;
[0045] The processor is configured to execute a computer program in the memory to implement the method as described in any of the first aspects, to obtain a target display image;
[0046] The display screen is used to display the target image.
[0047] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided, which, when an executable computer program in the storage medium is executed by a processor, enables the implementation of the method as described in any of the first aspects.
[0048] The technical solutions provided by the embodiments of this disclosure may include the following beneficial effects:
[0049] This embodiment provides an image processing method comprising: acquiring the current pixel influence value of each pixel on the display screen at the current moment, the current temperature detection value of each pixel at the current moment, the previous temperature prediction value of each pixel at the previous moment, and a temperature gain; then, acquiring the optimal temperature estimate at the current moment based on the current pixel influence value, the current temperature detection value, the previous temperature prediction value, and the temperature gain; subsequently, compensating the pixel data of the image to be displayed based on the current optimal temperature estimate to obtain a target display image, which is used to input the display screen for display. Thus, this embodiment uses the previous temperature prediction value and the current temperature detection value related to historical pixel data to predict the temperature and obtain the optimal temperature estimate, which can improve the accuracy of temperature prediction, thereby improving the accuracy of pixel compensation and improving the display effect.
[0050] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description
[0051] Figure 1 is a schematic diagram of a display screen showing a temperature afterimage according to an embodiment of the present disclosure.
[0052] Figure 2 is a block diagram of an image processing method according to an embodiment of the present disclosure.
[0053] Figure 3 is a flowchart of an image processing method according to an embodiment of the present disclosure.
[0054] Figure 4 is a flowchart of an embodiment of the present disclosure for obtaining temperature gain.
[0055] Figure 5 is a block diagram of an image processing apparatus according to an embodiment of the present disclosure. Detailed Implementation
[0056] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses consistent with some aspects of this disclosure as detailed in the appended claims.
[0057] To address the aforementioned technical problems, embodiments of this disclosure provide an image processing method, apparatus, display device, and storage medium. One image processing method provided in this disclosure, as shown in Figure 2, involves a temperature sensor embedded in the display screen. This temperature sensor can be implemented using a temperature sensor matrix to detect the temperature of each pixel. After obtaining the temperature values of each pixel, an optimal temperature estimate for each pixel can be obtained. Then, the pixel data of the image to be displayed is compensated using these optimal temperature estimates to obtain the target display image.
[0058] The inventive concept of this disclosure is that, in the process of obtaining the optimal temperature estimate, the optimal temperature estimate at the current moment is estimated by jointly using the historical display image of the display screen and the current temperature detection value detected by the temperature sensor. This not only takes into account the influence of the historical display image on the temperature of each pixel, but also takes into account the influence of the measurement error detected by the temperature sensor on the temperature of each pixel. The joint estimation of temperature by both can improve the accuracy of temperature estimation.
[0059] Based on the above inventive concept, the present disclosure provides an image processing method, as shown in Figure 3, including steps 31 to 33.
[0060] In step 31, the current pixel influence value of each pixel on the display screen at the current moment, the current temperature detection value of each pixel at the current moment, the previous temperature prediction value of each pixel at the previous moment, and the temperature gain are obtained.
[0061] In this embodiment, the processor can calculate the influence value of the current pixel data of each pixel on the temperature at the current time t, hereinafter referred to as the current pixel influence value S(t). It is understood that displaying the current pixel data will affect the temperature value, and the influence value on the temperature before and after displaying the current pixel data can be obtained. For example, after obtaining the current pixel data, the mapping relationship between preset pixel data and influence values can be detected to obtain the aforementioned current pixel influence value S(t). In this embodiment, by obtaining the influence value of the pixel data at the current time t on the pixel temperature, it can be shown that the influence of historically displayed images on the pixel temperature is considered.
[0062] In this embodiment, the processor can obtain the current temperature detection value M(t) of each pixel at the current time t. The current temperature detection value at the current time t refers to the temperature value of each pixel obtained by the temperature sensor. The current temperature detection value M(t) at the current time t is affected by the measurement error of the temperature sensor. In other words, this embodiment considers the impact of the temperature sensor's measurement error on the pixel temperature.
[0063] In this embodiment, the processor can calculate the predicted temperature value of each pixel at the previous time t-1, as shown in Equation (1).
[0064]
[0065] In equation (1), This represents the predicted temperature at time t. Let S(t) represent the predicted temperature value at the previous time t-1, B represent the initial temperature value, S(t) represent the pixel data (or grayscale value) of the pixel at the current time t, and a and b represent the heating rate and heating acceleration of the pixel, respectively.
[0066] ^-
[0067] It should be noted that in equation (1), “” indicates an estimated value, and “” indicates that it is not the optimal estimated value. This previous temperature prediction value It is the temperature estimate at the previous time t-1, which can be read from the processing result of this image processing method executed at the previous time t-1.
[0068] In this embodiment, the processor can obtain temperature gain, as shown in Figure 4, steps 41 and 42.
[0069] In step 41, the temperature estimation variance p(t-1) and the first weight of the previous time moment are obtained, and the current error variance and the second weight corresponding to the current temperature detection value are obtained; the first weight represents the influence of the temperature estimation variance of the previous time moment on the optimal temperature estimate, and the second weight represents the influence of the current error variance on the optimal temperature estimate.
[0070] In this step, the processor can obtain the temperature estimation variance p(t-1) from the previous time step t-1. It is understood that the temperature estimation variance p(t) is calculated simultaneously each time a temperature estimate is obtained. Thus, the processor can directly read the temperature estimation variance p(t-1) from the calculation result of the previous time step t-1. The method for obtaining the temperature estimation variance p(t-1) is the same as the method for obtaining the temperature estimation variance p(t). The method for obtaining the temperature estimation variance p(t) will be described in subsequent embodiments and will not be elaborated here.
[0071] In this step, the processor can obtain the weight of the temperature estimation variance p(t-1) at the previous time t-1, namely the first weight ω1.
[0072] In this step, the processor can obtain the current error variance r(t) corresponding to the current temperature detection value M(t). Considering that the performance of the temperature sensor inside the display screen remains essentially unchanged, its temperature detection error is also essentially constant. This error can be obtained through actual testing, calibration, or from the temperature sensor manufacturer. In other words, the current error variance r(t) corresponding to the current temperature detection value M(t) is a fixed value. Therefore, the processor can directly read the aforementioned current error variance r(t).
[0073] In this step, the processor can obtain the weight of the current error variance r(t) at the current time t, namely the second weight ω2. The sum of the first weight ω1 and the second weight ω2 is 1.
[0074] In step 42, a mapping relationship equivalent to the first weight is constructed based on the temperature estimation variance, the current error variance, and the second weight, which serves as the temperature gain; the temperature gain represents the change of the first weight when the current error variance is fixed and the temperature estimation variance changes.
[0075] In this step, the processor can construct a mapping relationship equivalent to the first weight ω1 based on the temperature estimation variance p(t-1), the current error variance r(t), and the second weight ω2, which serves as the temperature gain.
[0076] Suppose that the temperature state estimate is a random variable x, and that random variable x follows a variance of σ. 2 If kx follows a normal distribution, then kx follows a variance of k. 2 σ 2 The normal distribution of the temperature estimate; the measurement error is a random variable, which can be represented by the variance r(t). The temperature estimate variance p(t-1), the first weight ω1, the current error variance r(t), and the second weight ω2 satisfy equation (2).
[0077]
[0078] In equation (2), p(t) represents the temperature estimate variance at the current time t, and p(t-1) represents the temperature estimate variance at the previous time t-1.
[0079] In equation (2), the current error variance r(t) is a fixed value, and the other four parameters are variables. To obtain the first weight ω1 when p(t) is at its minimum, we can first take the derivative of p(t) with respect to the first weight ω1 in equation (1) and set the derivative to zero, as shown in equation (3).
[0080]
[0081] Simplifying equation (3) yields equation (4).
[0082]
[0083] In this step, equation (4) is used as the mapping relationship described above, which represents the change of the first weight when the current error variance r(t) is fixed and the temperature estimation variance p(t-1) changes. In other words, the processor obtains the temperature gain K(t).
[0084] In step 32, the optimal temperature estimate for the current moment is obtained based on the current pixel influence value, the current temperature detection value, the previous temperature prediction value, and the temperature gain.
[0085] In this embodiment, the processor can obtain the optimal temperature estimate for the current moment based on the current pixel influence value S(t), the current temperature detection value M(t), the previous temperature prediction value, and the temperature gain K(t). As shown in equation (5).
[0086]
[0087] In equation (5), This represents the optimal temperature estimate at time t. K(t) represents the temperature gain, and M(t) represents the current temperature detection value at time t. This represents the predicted temperature at the current time t.
[0088] In step 33, the pixel data of the image to be displayed is compensated according to the optimal estimate of the current temperature to obtain the target display image, which is used to input the display screen for display.
[0089] In this embodiment, the processor can compensate the pixel data of the image to be displayed based on the current optimal temperature estimate to obtain the target display image. In one example, a preset relationship between temperature and compensation value can be stored in advance, and this preset relationship can exist in the form of a lookup table. Then, the processor obtains the current optimal temperature estimate... Then, you can query the above preset relationship to obtain the pixel compensation value C under the optimal estimate of the current temperature. final The channel compensation coefficients for the RGB sub-pixels of each pixel are C0 and C1 respectively. R C G C B Based on the above pixel compensation value C final and channel compensation coefficient C R C G C B The compensation for the displayed image is shown in Equation (6).
[0090]
[0091] In equation (6), I C_R I C_G and I C_B These represent the pixel data of the target displayed image.
[0092] In this embodiment, the processor can send the target display image to the display screen for display. This embodiment uses the previous temperature prediction value and the current temperature detection value related to historical pixel data to predict the temperature, obtaining the optimal temperature estimate. This improves the accuracy of temperature prediction, thereby improving the accuracy of pixel compensation and ultimately enhancing the display effect.
[0093] The following describes an image compensation method provided in this disclosure, using MLED video wall and Kalman filtering as examples, including:
[0094] (1) State prediction
[0095] After an image is displayed on an MLED video wall, the screen begins to heat up. Images with different grayscale values will have different effects on the screen's temperature. For example, when the grayscale value is 255, the screen reaches its highest brightness, and the screen heats up rapidly. The screen's temperature is also the highest after it stabilizes. On the other hand, when the grayscale value is 128, the screen brightness is medium. The screen heats up more slowly, and the highest temperature after it stabilizes is also lower. Therefore, the screen's heating rate is determined by the grayscale value of the image displayed on the screen and the current real-time temperature of the screen. The temperature prediction is shown in equation (1).
[0096] Kalman filtering treats the state estimate as a random variable. In addition to predicting the state estimate itself, this example also predicts the variance of the state estimate, where the estimation error satisfies equation (2).
[0097] (2) Status update
[0098] The back panel of the MLED splicing screen is equipped with a temperature sensor, which can detect the current temperature value M(t) of each pixel. In this example, the optimal temperature estimate at the current time t can be obtained by combining the non-optimal estimate at the current time t with the observed temperature value at the current time t. That is, the state update formula of the Kalman filter is shown in Equation (5).
[0099] Measurement error is the difference between the measured value and the true value. It is random and can be described by the variance r(t).
[0100] In this example, the Kalman filter can fuse the above state predictions and measurements together, as shown in Equation (7).
[0101]
[0102] And satisfy,
[0103] ω1+ω2=1 (8)
[0104] Similarly, the variances of state estimation and state measurement satisfy equation (2).
[0105] In this example, the optimal estimate of the current temperature is obtained by taking the derivative of p(t) with respect to ω1 and obtaining the minimum value of p(t). Finally, the mapping relationship is shown in Equation (4), and the relationship between the mapping relationship and the temperature gain is shown in Equation (9).
[0106]
[0107] Based on the above equations, the variance of the current optimal temperature estimate can be obtained as shown in equation (10).
[0108] p(t)=(1-K(t))p(t-1) (10)
[0109] Referring to equation (10), the range of K(t) is (0, 1), and the range of 1-K(t) is (0, 1), thus obtaining (1-Kn)≤1, meaning that the uncertainty of the temperature estimate decreases as the number of Kalman filter iterations increases. When the measurement uncertainty is high, the Kalman filter gain is low, so the convergence speed of the temperature estimate uncertainty will be slow; when the measurement uncertainty is low, the Kalman filter gain is high, and the state estimate uncertainty will converge to 0 quickly.
[0110] Understandably, the purpose of using the Kalman filter gain in this example is to balance the weights of temperature estimation and temperature detection, i.e., whether the temperature estimation is more accurate or the temperature detection is more accurate. If the temperature estimation is considered more accurate, the second weight will be larger; if the temperature detection is considered more accurate, the first weight will be larger.
[0111] In this example, during the temperature estimation process of the MLED splicing screen, the first stage tends to favor the temperature detection value observed by the temperature sensor, while the second stage achieves a balance between the weights of temperature estimation and temperature detection as the Kalman filter gain changes, ultimately obtaining a more accurate optimal estimate of the current temperature.
[0112] Based on the image processing method provided in this disclosure, this disclosure also provides an image processing apparatus, as shown in FIG5, the apparatus comprising:
[0113] The data acquisition module 51 is used to acquire the current pixel influence value of each pixel of the display screen at the current moment, the current temperature detection value of each pixel at the current moment, the previous temperature prediction value of each pixel at the previous moment, and the temperature gain.
[0114] The optimal temperature estimation module 52 is used to obtain the optimal temperature estimate at the current moment based on the current pixel influence value, the current temperature detection value, the previous temperature prediction value, and the temperature gain.
[0115] The pixel data compensation module 53 is used to compensate the pixel data of the image to be displayed based on the optimal estimate of the current temperature to obtain the target display image, which is used to be input to the display screen for display.
[0116] In one embodiment, the optimal temperature estimation module includes:
[0117] The current temperature prediction submodule is used to determine the current temperature prediction value at the current moment based on the current pixel influence value and the previous temperature prediction value.
[0118] The optimal temperature estimation submodule is used to determine the optimal temperature estimate for the current moment based on the current temperature prediction value, the current temperature detection value, and the temperature gain.
[0119] In one embodiment, the current temperature prediction submodule includes:
[0120] The temperature parameter acquisition unit is used to acquire the heating acceleration, heating rate and initial temperature value of the pixel device;
[0121] The temperature power value acquisition unit is used to acquire the first temperature difference between the current pixel influence value and the previous temperature prediction value, and to obtain the power value of the first temperature difference; the power value is obtained with the first temperature difference as the base and the heating acceleration as the power.
[0122] The current temperature prediction unit is used to obtain the product of the power value and the heating rate, and to obtain the sum of the product and the initial temperature value, and to use the sum as the current temperature prediction value.
[0123] In one embodiment, the optimal temperature estimation submodule includes:
[0124] A temperature difference acquisition unit is used to acquire a second temperature difference between the current temperature detection value and the current temperature prediction value;
[0125] The optimal temperature estimation unit is used to obtain the product of the second temperature difference and the temperature gain, and to obtain the sum of the product and the current temperature prediction value as the optimal temperature estimate value at the current moment.
[0126] In one embodiment, the temperature gain acquisition unit includes:
[0127] The first weight acquisition subunit is used to acquire the temperature estimation variance and the first weight at the previous time. The first weight represents the influence of the temperature estimation variance at the previous time on the optimal temperature estimate.
[0128] The second weight acquisition subunit is used to acquire the current error variance and the second weight corresponding to the current temperature detection value; the second weight represents the influence of the current error variance on the optimal temperature estimate.
[0129] The temperature gain acquisition subunit is used to construct a mapping relationship equivalent to the first weight based on the temperature estimation variance, the current error variance, and the second weight, as the temperature gain; the temperature gain represents the change of the first weight when the current error variance is fixed and the temperature estimation variance changes.
[0130] It should be noted that the apparatus shown in this embodiment matches the content of the method embodiment, and the content of the above method embodiment can be referred to, which will not be repeated here.
[0131] In some possible embodiments, a display device is provided, comprising:
[0132] Display screen, processor, and memory;
[0133] The memory is used to store computer programs that can be executed by the processor;
[0134] The processor is configured to execute a computer program in the memory to implement the method as described in any of the first aspects, to obtain a target display image;
[0135] The display screen is used to display the target image.
[0136] In some possible embodiments, a non-transitory computer-readable storage medium is provided, which, when executed by a processor, enables the image processing method described above.
[0137] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This disclosure is intended to cover any variations, uses, or adaptations that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.
[0138] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.
Claims
1. An image processing method, characterized in that, The method includes: acquiring the current pixel influence value of each pixel on the display screen at the current moment, the current temperature detection value of each pixel at the current moment, the previous temperature prediction value of each pixel at the previous moment, and the temperature gain; acquiring the optimal temperature estimate at the current moment based on the current pixel influence value, the current temperature detection value, the previous temperature prediction value, and the temperature gain; compensating the pixel data of the image to be displayed based on the optimal temperature estimate to obtain the target display image, which is used to input the display screen for display.
2. The method according to claim 1, characterized in that, Obtaining the optimal temperature estimate for the current moment based on the current pixel influence value, the current temperature detection value, the previous temperature prediction value, and the temperature gain includes: determining the current temperature prediction value for the current moment based on the current pixel influence value and the previous temperature prediction value; and determining the optimal temperature estimate for the current moment based on the current temperature prediction value, the current temperature detection value, and the temperature gain.
3. The method according to claim 2, characterized in that, Determining the current temperature prediction value based on the current pixel influence value and the previous temperature prediction value includes: acquiring the heating acceleration, heating rate, and initial temperature value of the pixel device; acquiring a first temperature difference between the current pixel influence value and the previous temperature prediction value, and obtaining a power value of the first temperature difference; the power value is obtained with the first temperature difference as the base and the heating acceleration; acquiring the product of the power value and the heating rate, and acquiring the sum of the product and the initial temperature value, and using the sum as the current temperature prediction value.
4. The method according to claim 2, characterized in that, Determining the optimal temperature estimate for the current moment based on the current temperature prediction, the current temperature detection, and the current temperature weighting coefficient includes: obtaining a second temperature difference between the current temperature detection and the current temperature prediction; obtaining the product of the second temperature difference and the temperature gain; and obtaining the sum of the product and the current temperature prediction as the optimal temperature estimate for the current moment.
5. The method according to claim 4, characterized in that, Obtaining the temperature gain includes: obtaining the temperature estimation variance and a first weight at the previous moment, and obtaining the current error variance and a second weight corresponding to the current temperature detection value; the first weight represents the influence of the temperature estimation variance at the previous moment on the optimal temperature estimate, and the second weight represents the influence of the current error variance on the optimal temperature estimate; constructing a mapping relationship equivalent to the first weight based on the temperature estimation variance, the current error variance, and the second weight, as the temperature gain; the temperature gain represents the change of the first weight when the current error variance is fixed and the temperature estimation variance changes.
6. An image processing apparatus, characterized in that, The device includes: a data acquisition module, used to acquire the current pixel influence value of each pixel on the display screen at the current moment, the current temperature detection value of each pixel at the current moment, the previous temperature prediction value of each pixel at the previous moment, and the temperature gain; a temperature optimal estimation module, used to acquire the optimal temperature estimate at the current moment based on the current pixel influence value, the current temperature detection value, the previous temperature prediction value, and the temperature gain; and a pixel data compensation module, used to compensate the pixel data of the image to be displayed based on the current optimal temperature estimate to obtain a target display image, the target display image being input to the display screen for display.
7. The apparatus according to claim 6, characterized in that, The optimal temperature estimation module includes: a current temperature prediction submodule, used to determine the current temperature prediction value at the current moment based on the current pixel influence value and the previous temperature prediction value; and a optimal temperature estimation submodule, used to determine the optimal temperature estimation value at the current moment based on the current temperature prediction value, the current temperature detection value, and the temperature gain.
8. The apparatus according to claim 7, characterized in that, The current temperature prediction submodule includes: a temperature parameter acquisition unit, used to acquire the heating acceleration, heating rate, and initial temperature value of the pixel device; a temperature power value acquisition unit, used to acquire a first temperature difference between the current pixel influence value and the previous temperature prediction value, and obtain a power value of the first temperature difference; the power value is obtained by exponentiation of the first temperature difference; and a current temperature prediction unit, used to acquire the product of the power value and the heating rate, and acquire the sum of the product and the initial temperature value, and use the sum as the current temperature prediction value.
9. The apparatus according to claim 7, characterized in that, The optimal temperature estimation submodule includes: a temperature difference acquisition unit, used to acquire a second temperature difference between the current temperature detection value and the current temperature prediction value; and an optimal temperature estimation unit, used to acquire the product of the second temperature difference and the temperature gain, and acquire the sum of the product and the current temperature prediction value as the optimal temperature estimate value at the current moment.
10. The apparatus according to claim 9, characterized in that, The temperature gain acquisition unit includes: a first weight acquisition subunit, used to acquire the temperature estimation variance and a first weight at the previous moment, wherein the first weight represents the influence of the temperature estimation variance at the previous moment on the optimal temperature estimate; a second weight acquisition subunit, used to acquire the current error variance and a second weight corresponding to the current temperature detection value; wherein the second weight represents the influence of the current error variance on the optimal temperature estimate; and a temperature gain acquisition subunit, used to construct a mapping relationship equivalent to the first weight based on the temperature estimation variance, the current error variance, and the second weight, as the temperature gain; wherein the temperature gain represents the change of the first weight when the current error variance is fixed and the temperature estimation variance changes.
11. A display device, characterized in that, include: Display screen, processor, and memory; The memory is used to store computer programs that can be executed by the processor; The processor is used to execute the computer program in the memory to implement the method as described in any one of claims 1 to 5 to obtain the target display image; the display screen is used to display the target display image.
12. A non-transitory computer-readable storage medium, characterized in that, When the executable computer program in the storage medium is executed by a processor, it can implement the method as described in any one of claims 1 to 5.