Self-diagnosis method for image correction chip, image correction chip, and onboard display device

Abstract

Self-diagnosis method for an image correction chip, image correction chip, and onboard display device. The present invention relates to a self-diagnosis method for an image correction chip, wherein the image correction chip comprises an integrated grayscale value anomaly monitoring module including an upper limit anomaly monitoring module and a lower limit anomaly monitoring module, wherein when one of the upper limit anomaly monitoring module and the lower limit anomaly monitoring module is in an operational state, the other of the upper limit anomaly monitoring module and the lower limit anomaly monitoring module is in a standby state, and the method includes the steps of: enabling the other and setting a monitoring threshold for the other when one of the upper limit / lower limit anomaly monitoring modules is in an operational state; acquiring the grayscale value of the image to be corrected in real time and inputting the acquired grayscale value to the other; and reading the monitoring status result of the other and determining whether the grayscale value anomaly monitoring module is functioning normally based on the monitoring status result. The present invention further relates to an image correction chip for a HUD and an onboard display device including an image correction chip.

Description

Technical Field

[0001] The present invention relates to the technical field of image correction. More specifically, the present invention relates to a self-diagnosis method for an image correction chip, an image correction chip for HUD, and an on-board display device including the image correction chip.

Background Art

[0002] HUD (Head-Up Display) is a display product commonly used in vehicles. The principle of HUD is to form a rectangular projection area in front of the driver's field of view (such as on the front windshield) by optical calculation, and display driving information on this projection area, so that the driver can obtain the driving information of the vehicle in real time without taking his line of sight away from the road.

[0003] The image correction chip is an important component of the HUD product. This chip is mainly responsible for correcting and optimizing the projected image in order to eliminate the deformation and distortion of the projected image, thereby ensuring that the image projected on the front windshield of the vehicle is clearly and accurately displayed to the driver. An important module of the image correction chip is the image gradation value abnormal monitoring module, which is used to monitor and process the abnormal situation of the gradation value of the projected image. For example, it avoids the risk that the driver's eyes are momentarily dazzled due to the gradation value of the image being too large.

[0004] However, during the operation process, the gradation value abnormal monitoring module itself may malfunction. Therefore, it is necessary to monitor in real time whether the gradation value abnormal monitoring function of the image correction chip remains normal.

[0005] Summary of the Invention According to a first aspect of the present invention, a self-diagnosis method for an image correction chip is proposed, the image correction chip having an integrated grayscale value anomaly monitoring module used to monitor whether the grayscale value of an image to be corrected is within a predetermined range, the grayscale value anomaly monitoring module includes an upper limit anomaly monitoring module and a lower limit anomaly monitoring module, and when one of the upper limit anomaly monitoring module and the lower limit anomaly monitoring module is in an operational state, the other of the upper limit anomaly monitoring module and the lower limit anomaly monitoring module is in a standby state, and this method is If one of the upper / lower limit anomaly monitoring modules is operational, the steps include enabling the other and setting the monitoring threshold for the other module, The process involves acquiring the tonal values ​​of the image being corrected in real time and inputting the acquired tonal values ​​into the other device. The step includes reading the monitoring status result of the other device and determining whether the grayscale value abnormality monitoring module is functioning correctly based on this monitoring status result.

[0006] Conveniently, the monitoring threshold of the lower limit anomaly monitoring module is set as the first threshold. If the grayscale value of the image being corrected is greater than the first threshold, the monitoring status result of the lower limit anomaly monitoring module is normal. If the grayscale value of the image being corrected is less than the first threshold, the monitoring status result of the lower limit anomaly monitoring module is abnormal.

[0007] Advantageously, the monitoring threshold of the upper limit anomaly monitoring module is set as a second threshold. If the grayscale value of the image being corrected is greater than the second threshold, the monitoring status result of the upper limit anomaly monitoring module is an abnormal state. If the grayscale value of the image being corrected is less than the second threshold, the monitoring status result of the lower limit anomaly monitoring module is a normal state.

[0008] Advantageously, the grayscale values ​​of the image being corrected have minimum and maximum values, and when the upper limit anomaly monitoring module is operational, the monitoring threshold of the lower limit anomaly monitoring module is set to the minimum value within the first cycle, the first monitoring status result of the lower limit anomaly monitoring module is read, and if the first monitoring status result is abnormal, this indicates that the grayscale value anomaly monitoring module is malfunctioning.

[0009] Advantageously, when the upper limit anomaly monitoring module is operational, the monitoring threshold of the lower limit anomaly monitoring module is set to the maximum value within the second cycle, and the second monitoring status result of the lower limit anomaly monitoring module is read. When the second monitoring status result is normal, this indicates that the grayscale value anomaly monitoring module is malfunctioning.

[0010] To the advantage of this method,

[0011] Within a predetermined period, the first and second cycles alternately repeat. If, within this predetermined period, the first monitoring status result remains normal while the second monitoring status result remains abnormal, this indicates that the grayscale value abnormality monitoring module is functioning correctly.

[0012] Advantageously, the tonal values ​​of the image being corrected are 8-bit unsigned hexadecimal data, and the value range of the tonal values ​​of the image being corrected is 0x00 to 0xFF.

[0013] Advantageously, the upper limit anomaly monitoring module and the lower limit anomaly monitoring module each have a state register, and the state monitoring results are stored in the corresponding state registers.

[0014] According to a second aspect of the present invention, an image correction chip for a HUD is proposed, which image correction chip is A tone value anomaly monitoring module used to monitor whether the tone values ​​of the image to be corrected are within a predetermined range, The system includes a processor configured to detect whether the grayscale value anomaly monitoring module is functioning correctly using the self-diagnostic method described above.

[0015] According to a third aspect of the present invention, an onboard display device is further proposed, the onboard display device is An image generation unit used to generate images displayed by the HUD, The above-mentioned image correction chip is used to correct the image generated by the image generation unit, Includes a HUD module used to project a corrected image onto the windshield of a vehicle.

[0016] The self-diagnostic method for an image correction chip according to the present invention can detect in real time whether the grayscale abnormality upper limit monitoring function of the image correction chip is functioning correctly, in order to prevent the grayscale abnormality monitoring module of the chip from freezing, thereby ensuring the safe and reliable implementation of the image grayscale abnormality monitoring function of the HUD product. This method has few requirements for the image correction chip, does not require the chip to have a self-diagnostic function for the grayscale abnormality monitoring module, does not require any additional new hardware circuitry, and has low circuit cost. Furthermore, this method can be implemented in the HUD processor using software, the implementation process is simple, and it does not require excessive software computation in the HUD product.

[0017] Other features and advantages of the methods of the present invention will be more clearly or specifically described by using the drawings incorporated herein and specific embodiments in conjunction with the drawings to illustrate specific principles of the present invention. [Brief explanation of the drawing]

[0018] [Figure 1] A flowchart of a self-diagnosis method for an image correction chip according to an exemplary embodiment of the present invention is shown. [Modes for carrying out the invention]

[0019] A self-diagnostic method for a grayscale value anomaly monitoring module of an image correction chip according to the present invention is described below with reference to the accompanying drawings. Many details are described below so that those skilled in the art may understand the invention more comprehensively. However, it will be apparent to those skilled in the art that the invention can be realized without some of these details. Rather, to carry out the invention, one may consider using any combination of the following features and elements, regardless of whether they relate to different embodiments. Therefore, the various aspects, features, embodiments, and advantages described below are used solely to illustrate the invention and should not be considered components or definitions of the claims.

[0020] In HUD products, abnormalities in image gradation values ​​can lead to problems such as the projected image on the vehicle's windshield being too bright, which can easily and momentarily dazzle the driver. Therefore, a gradation value abnormality monitoring module is generally provided in the image correction chip. This module analyzes the gradation values ​​of the projected image and, if the gradation values ​​are abnormal, outputs an abnormal condition, thereby activating a safety abnormality in the function and issuing an alarm. This prevents, for example, the driver's vision being obstructed due to excessively high image gradation values.

[0021] Specifically, the grayscale value anomaly monitoring module can recognize the grayscale values ​​of an image and determine whether the grayscale values ​​exceed the normal range according to a set threshold. As soon as an abnormal grayscale value is detected, the module can notify the image correction chip to output an abnormal condition, activate the safety status of the HUD function to issue an alarm, and notify the driver.

[0022] However, during the operation process, the tone value abnormal monitoring module itself may malfunction. Therefore, it is necessary to monitor in real time whether the tone value abnormal monitoring function of the image correction chip remains normal. Currently, mainstream HUD products generally use the state monitoring function of the image correction chip itself to indirectly infer whether the function of the tone value abnormal monitoring module is normal. Since the dedicated self-diagnosis function of the tone value abnormal monitoring module is not provided in the image correction chips widely used in current mainstream HUD products, it is impossible to directly monitor the operating state of the module, and the state monitoring function of the correction chip itself cannot fully cover all malfunction scenarios of the tone value abnormal monitoring module.

[0023] Based on the above background, the present invention proposes a self-diagnosis method for an image correction chip. This method can detect in real time whether the tone abnormal upper limit monitoring function of the image correction chip of the HUD product is normal in order to prevent the tone abnormal monitoring module of the chip from freezing, thereby ensuring the safe and reliable implementation of the image tone value abnormal monitoring function of the HUD product.

[0024] Generally, the tone value abnormal monitoring function of the image correction chip of the HUD product includes two parts: an upper limit abnormal monitoring module and a lower limit abnormal monitoring module.

[0025] <000​​​​For similar reasons, if the grayscale value provided by the image generation unit is smaller than the set threshold of the lower limit detection function, the image correction chip activates the status register of the lower limit anomaly monitoring module to output an anomaly state (e.g., "1"), otherwise it outputs a normal state (e.g., "0").

[0027] In safety control strategies for preventing the grayscale values ​​of output images from becoming too high in HUD products, only the upper limit anomaly monitoring function is required, while the lower limit anomaly monitoring function remains in standby mode. Considering this background, the present invention proposes using the lower limit anomaly monitoring function of an image correction chip to monitor the status of the grayscale value anomaly monitoring function itself online. By alternately setting the monitoring threshold of the lower limit anomaly monitoring module (for example, for an 8-bit grayscale image, the monitoring threshold can alternate between a maximum value of 0xFF and a minimum value of 0x00), it is possible to monitor whether the lower limit anomaly monitoring module is in a malfunction (frozen) state, and specifically, it is possible to determine whether the grayscale value anomaly monitoring function is functioning correctly based on the monitoring status result ("1" or "0") of the status register.

[0028] Specifically, when the result of the register monitoring state matches the expected setting (i.e., the actual reading is the same as the expected value), this indicates that the image correction chip's anomaly monitoring function is working correctly, and the chip's upper limit anomaly monitoring module can be further used to implement a safety control strategy for monitoring upper limit anomalies in the grayscale value of the HUD product's output image. Conversely, when the result of the register monitoring state does not match the expected setting (for example, the expected value is "1" but the actual reading is "0", or the expected value is "0" but the actual reading is "1"), this indicates that the image correction chip's anomaly monitoring function is malfunctioning (not working correctly), and in this case, it is not appropriate to continue using the chip's upper limit anomaly monitoring module to implement a safety control strategy for monitoring upper limit anomalies in the grayscale value of the HUD product's output image, and a safety malfunction alarm for the function will be activated.

[0029] Figure 1 shows a flowchart of a self-diagnostic method for an image correction chip according to an exemplary embodiment of the present invention. The image correction chip has an integrated grayscale value anomaly monitoring module used to monitor whether the grayscale values ​​of the image being corrected are within a predetermined range. The grayscale value anomaly monitoring module may comprise, in particular, an upper limit anomaly monitoring module and a lower limit anomaly monitoring module, where one of the two monitoring modules is in operation while the other is in standby mode. Under these circumstances, the present invention proposes to monitor the status of the grayscale value anomaly monitoring function itself online using the monitoring module in standby mode (e.g., the lower limit anomaly monitoring module).

[0030] Referring to Figure 1, the image correction chip first needs to acquire the image to be corrected from, for example, an image generation unit (PGU). Within the chip, an upper limit anomaly monitoring module is used to monitor whether the grayscale value of the image to be corrected is within a predetermined range, for example, within an upper limit threshold.

[0031] Therefore, at this point, the lower limit anomaly monitoring module is in a standby state, and the module can be considered for use when the grayscale value anomaly monitoring function performs a self-diagnosis, and the lower limit anomaly monitoring module is activated for this purpose. Subsequently, by alternately setting the monitoring threshold of the lower limit anomaly monitoring module in different cycles, it is possible to monitor whether the lower limit anomaly monitoring module is in a malfunction (freeze) state, and specifically, it is possible to determine whether the grayscale value anomaly monitoring function is normal based on the monitoring status result ("1" or "0") of the lower limit anomaly monitoring module.

[0032] The upper limit anomaly monitoring module and the lower limit anomaly monitoring module each have a status register, and the status monitoring results of the corresponding monitoring module are stored in the corresponding status register.

[0033] For example, if the tonal values ​​of the image to be corrected are 8-bit unsigned hexadecimal data, the monitoring threshold of the lower limit anomaly monitoring module is set to the minimum value 0x00 in the first cycle, and the first monitoring state result of this lower limit anomaly monitoring module is read. The monitoring threshold is the minimum value, and therefore, if the tonal value anomaly monitoring module in the image correction chip is functioning correctly, it is expected that all tonal values ​​of the image to be corrected obtained from the image generation unit should be greater than this minimum value (i.e., the lower limit requirement for tonal values ​​is met, and the result of the first monitoring state should always be "0"). Conversely, if the result of this first monitoring state is "1", this indicates that the tonal value anomaly monitoring module in the image correction chip is malfunctioning.

[0034] Next, the monitoring threshold of the lower limit anomaly monitoring module is set to the maximum value 0xFF within the second cycle, and the second monitoring state result of the lower limit anomaly monitoring module is read. The monitoring threshold is the maximum value, and therefore, if the gradation value anomaly monitoring module in the image correction chip is functioning correctly, all gradation values ​​of the corrected image acquired from the image generation unit should be smaller than this maximum value (i.e., the gradation lower limit requirement is not met, and the result of the second monitoring state should always be "1"). Conversely, if the result of this second monitoring state is "0", this indicates that the gradation value anomaly monitoring module in the image correction chip is malfunctioning.

[0035] Within a predetermined period, the first and second cycles alternate and repeat, and the corresponding monitoring status results are read. If the first monitoring status result remains "0" and the second monitoring status result remains "1" within the predetermined period, this indicates that the grayscale value anomaly monitoring module in the image correction chip is functioning correctly. Otherwise, it indicates that the monitoring module is malfunctioning (e.g., frozen). In other words, within this predetermined period, the following process is repeated alternately. 1) The monitoring threshold of the lower limit anomaly monitoring module is set to the minimum value 0x00 in the first cycle. 2) The monitoring status result in the lower limit anomaly monitoring module's status register is read, and this value should be "0" (i.e., the requirement that the actual grayscale value of the image is greater than the minimum value is met). 3) The monitoring threshold of the lower limit anomaly monitoring module is set to the maximum value of 0xFF in the second cycle. 4) The monitoring status result in the lower limit anomaly monitoring module's status register is read, and this value should be "1" (i.e., the requirement that the actual grayscale value of the image is greater than the minimum value is not met).

[0036] The above cycle repeats as follows: 1)-2)-3)-4)-1)-2)-3)-4)...

[0037] If the status register value of the lower limit anomaly monitoring module read in processes 2) and 4) above does not match the prediction, an alarm indicating that the grayscale value anomaly monitoring is malfunctioning (frozen) will be immediately triggered, the self-diagnostic program will terminate, and the safety status of the HUD function will be activated.

[0038] An exemplary embodiment of the present invention further proposes an image correction chip for a HUD, the image correction chip comprising: an integrated grayscale anomaly monitoring module used to monitor whether the grayscale values ​​of an image to be corrected are within a predetermined range; and a processor configured to detect whether the grayscale anomaly monitoring module is functioning correctly using a self-test method according to the present invention.

[0039] Another exemplary embodiment of the present invention further proposes an onboard display device, the device comprising: an image generation unit used to generate an image to be displayed by a HUD, wherein the image may be, for example, an image relating to driving information; the above-mentioned image correction chip used to correct the image generated by the image generation unit; and a HUD module used to project the corrected image onto the windshield of a vehicle.

[0040] The self-diagnostic method for an image correction chip according to the present invention can detect in real time whether the grayscale abnormality upper limit monitoring function of the image correction chip is functioning correctly, in order to prevent the grayscale abnormality monitoring module of the chip from freezing, thereby ensuring the safe and reliable implementation of the image grayscale abnormality monitoring function of the HUD product. This method has few requirements for the image correction chip, does not require the chip to have a self-diagnostic function for the grayscale abnormality monitoring module, does not require any additional new hardware circuitry, and has low circuit cost. Furthermore, this method can be implemented in the HUD processor using software, the implementation process is simple, and it does not require excessive software computation in the HUD product.

[0041] Those skilled in the art will understand that the steps of the method according to the present invention are not limited to being carried out in the order cited above. Furthermore, in addition to terms such as “include” and “comprise” that mean steps directly and explicitly expressed in the description and claims, the technical solutions of this application do not exclude situations in which other steps exist that are not directly or explicitly expressed.

[0042] The present invention is disclosed above in conjunction with preferred embodiments, but is not limited thereto. Any changes or modifications made by those skilled in the art without departing from the spirit and scope of the invention should be included within the scope of its protection. Accordingly, the scope of protection of the present invention is defined by the claims.

Claims

1. A self-diagnosis method for an image correction chip, wherein the image correction chip has an integrated tone value anomaly monitoring module used to monitor whether the tone values ​​of an image to be corrected are within a predetermined range, the tone value anomaly monitoring module includes an upper limit anomaly monitoring module and a lower limit anomaly monitoring module, and when one of the upper limit anomaly monitoring module and the lower limit anomaly monitoring module is in an operational state, the other of the upper limit anomaly monitoring module and the lower limit anomaly monitoring module is in a standby state, and the method is, If one of the upper / lower limit abnormality monitoring modules is in the operating state, the steps include enabling the other module and setting the monitoring threshold for the other module. The steps include acquiring the grayscale values ​​of the image to be corrected in real time and inputting the acquired grayscale values ​​to the other device, A self-diagnostic method for the image correction chip, comprising the steps of reading the monitoring status result of the other device and determining whether the grayscale value abnormality monitoring module is functioning correctly based on the monitoring status result.

2. The monitoring threshold of the lower limit abnormality monitoring module is set as a first threshold, and if the grayscale value of the image to be corrected is greater than the first threshold, the monitoring status result of the lower limit abnormality monitoring module is a normal state, and if the grayscale value of the image to be corrected is less than the first threshold, the monitoring status result of the lower limit abnormality monitoring module is an abnormal state, as described in claim 1.

3. The monitoring threshold of the upper limit abnormality monitoring module is set as a second threshold, and if the grayscale value of the image to be corrected is greater than the second threshold, the monitoring status result of the upper limit abnormality monitoring module is an abnormal state, and if the grayscale value of the image to be corrected is less than the second threshold, the monitoring status result of the upper limit abnormality monitoring module is a normal state, as described in claim 1.

4. The grayscale values ​​of the corrected image have a minimum and a maximum value, and when the upper limit anomaly monitoring module is in operation, the monitoring threshold of the lower limit anomaly monitoring module is set to the minimum value within the first cycle, and the first monitoring status result of the lower limit anomaly monitoring module is read. A self-diagnosis method for an image correction chip according to any one of claims 1 to 3, wherein when the first monitoring status result is in an abnormal state, this indicates that the grayscale value abnormality monitoring module is malfunctioning.

5. When the upper limit abnormality monitoring module is in operation, the monitoring threshold of the lower limit abnormality monitoring module is set to the maximum value within the second cycle, and the second monitoring status result of the lower limit abnormality monitoring module is read. The self-diagnosis method for an image correction chip according to claim 4, wherein when the second monitoring status result is in a normal state, this indicates that the grayscale value abnormality monitoring module is malfunctioning.

6. The self-diagnosis method for an image correction chip according to claim 5, further comprising the following: within a predetermined period, the first cycle and the second cycle are repeated alternately, and within the predetermined period, if the first monitoring status result remains in a normal state and the second monitoring status result remains in an abnormal state, this indicates that the grayscale value abnormality monitoring module is functioning normally.

7. The self-diagnosis method for an image correction chip according to any one of claims 1 to 3, wherein the grayscale value of the image to be corrected is 8-bit unsigned hexadecimal data, and the value range of the grayscale value of the image to be corrected is 0x00 to 0xFF.

8. The self-diagnosis method for an image correction chip according to any one of claims 1 to 3, wherein the upper limit abnormality monitoring module and the lower limit abnormality monitoring module each include a state register, and the state monitoring result is stored in the corresponding state register.

9. An image correction chip for HUDs, A tone value anomaly monitoring module used to monitor whether the tone values ​​of the image to be corrected are within a predetermined range, An image correction chip for a HUD, comprising: a processor configured to detect whether the grayscale value abnormality monitoring module is functioning correctly using the self-diagnostic method described in any one of claims 1 to 8.

10. It is an onboard display device, An image generation unit used to generate images displayed by the HUD, An image correction chip according to claim 9, used to correct an image generated by the image generation unit, The onboard display device comprises a HUD module used to project the corrected image onto the windshield of a vehicle.

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