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

Abstract

The present invention relates to a self-test method for an image correction chip, the image correction chip comprising an integrated grayscale value abnormality monitoring module, which comprises an upper limit abnormality monitoring module and a lower limit abnormality monitoring module, wherein when one of the upper limit abnormality monitoring module and the lower limit abnormality monitoring module is in an operating state, the other of the upper limit abnormality monitoring module and the lower limit abnormality monitoring module is in an idle state, wherein the method comprises the following steps: when said one of the upper / lower limit abnormality monitoring modules is in the operating state, enabling said other and setting a monitoring threshold value of said other; acquiring a grayscale value of an image to be corrected in real time and inputting the acquired grayscale value to said other; and reading a monitoring state result of said other, and judging whether the grayscale value abnormality monitoring module is functioning normally on the basis of the monitoring state result. The present invention further relates to an image correction chip for a HUD, and an onboard display device comprising the image correction chip.

Description

Self-test method for image correction chip, image correction chip and onboard display deviceTechnical Field

[0001] The present invention relates to the technical field of image correction; more specifically, the present invention relates to a self-test method for an image correction chip, an image correction chip for a HUD, and an onboard display device comprising the image correction chip.Background Art

[0002] A HUD (head-up display) is a display product that is commonly used in vehicles. The principle of a HUD is, by means of optics calculations, to form a rectangular projection region at the front of a driver’s visual field (such as on a front windshield) and to display driving information on this projection region, so that the driver can acquire driving information of the vehicle in real time, without moving their line of vision away from the road.

[0003] An image correction chip is a key constituent part in a HUD product; the chip is mainly responsible for correcting and optimizing an image to be projected, so as to eliminate deformation and distortion in the projected image, thereby ensuring that an image projected onto a front windshield of a vehicle can be clearly and accurately displayed for a driver. An important module in an image correction chip is an image grayscale value abnormality monitoring module; the module is used for monitoring and processing a grayscale value abnormality situation in a projected image, for example, avoiding the risk of an image grayscale value being too large with the result that the driver is momentarily blinded.

[0004] However, in an operating process, the grayscale value abnormality monitoring module itself may malfunction; therefore, it is necessary to monitor in real time whether the grayscale value abnormality monitoring function in the imagecorrection chip remains normal.Summary of the Invention

[0005] According to a first aspect of the present invention, a self-test method for an image correction chip is proposed, the image correction chip having an integrated grayscale value abnormality monitoring module, which is used for monitoring whether a grayscale value of an image to be corrected is within a specified value range, and the grayscale value abnormality monitoring module comprising an upper limit abnormality monitoring module and a lower limit abnormality monitoring module, wherein when one of the upper limit abnormality monitoring module and the lower limit abnormality monitoring module is in an operating state, the other of the upper limit abnormality monitoring module and the lower limit abnormality monitoring module is in an idle state, wherein the method comprises the following steps: when said one of the upper / lower limit abnormality monitoring modules is in the operating state, enabling said other and setting a monitoring threshold value of said other; acquiring a grayscale value of an image to be corrected in real time and inputting the acquired grayscale value to said other; and reading a monitoring state result of said other, and judging whether the grayscale value abnormality monitoring module is functioning normally on the basis of the monitoring state result.

[0006] Advantageously, a monitoring threshold value of the lower limit abnormality monitoring module is set as a first threshold value, wherein when a grayscale value of the image to be corrected is greater than the first threshold value, a monitoring state result of the lower limit abnormality monitoring module is a normal state; when the grayscale value of the image to be corrected is less than the first threshold value, a monitoring state result of the lower limit abnormality monitoringmodule is an abnormal state.

[0007] Advantageously, a monitoring threshold value of the upper limit abnormality monitoring module is set as a second threshold value, wherein when a grayscale value of the image to be corrected is greater than the second threshold value, a monitoring state result of the upper limit abnormality monitoring module is an abnormal state; when the grayscale value of the image to be corrected is less than the second threshold value, a monitoring state result of the upper limit abnormality monitoring module is a normal state.

[0008] Advantageously, the grayscale value of the image to be corrected has a minimum value and a maximum value; when the upper limit abnormality monitoring module is in an operating state, a monitoring threshold value of the lower limit abnormality monitoring module is set as the minimum value in a first cycle, and a first monitoring state result of the lower limit abnormality monitoring module is read, wherein if the first monitoring state result is an abnormal state, this shows that the grayscale value abnormality monitoring module has malfunctioned.

[0009] Advantageously, when the upper limit abnormality monitoring module is in an operating state, a monitoring threshold value of the lower limit abnormality monitoring module is set as the maximum value in a second cycle, and a second monitoring state result of the lower limit abnormality monitoring module is read, wherein if the second monitoring state result is a normal state, this shows that the grayscale value abnormality monitoring module has malfunctioned.

[0010] Advantageously, the method further comprises:

[0011] In a predetermined duration, the first cycle and the second cycle are repeated alternately, wherein if the first monitoring state result remains a normal state and the second monitoring state result remains an abnormal state in thepredetermined duration, this shows that the grayscale value abnormality monitoring module functions normally.

[0012] Advantageously, the grayscale value of the image to be corrected is 8-bit unsigned hexadecimal data, and a value range of the grayscale value of the image to be corrected is 0x00 to OxFF.

[0013] Advantageously, the upper limit abnormality monitoring module and the lower limit abnormality monitoring module respectively comprise a state register, and the monitoring state result is stored in the corresponding state register.

[0014] According to a second aspect of the present invention, an image correction chip for a HUD is proposed, the image correction chip comprising: a grayscale value abnormality monitoring module, used for monitoring whether a grayscale value of an image to be corrected is within a specified value range; and a processor, the processor being configured to use the self-test method described above to detect whether the grayscale value abnormality monitoring module is functioning normally.

[0015] According to a third aspect of the present invention, an onboard display device is further proposed, the onboard display device comprising: an picture generation unit, used for generating an image to be displayed by HUD; the image correction chip described above, used for correcting an image generated by the picture generation unit; and a HUD module, used for projecting the corrected image onto a front windshield of a vehicle.

[0016] The self-test method for an image correction chip according to the presentinvention can detect whether a grayscale abnormality upper limit monitoring function of an image correction chip is normal in real time, to prevent the grayscale value abnormality monitoring module of the chip from freezing, thereby ensuring safe and reliable implementation of an image grayscale value abnormality monitoring function of the HUD product. The method has lower requirements for an image correction chip, not requiring the chip to have a self-test function of a grayscale value abnormality monitoring module, and not requiring an additional new hardware circuit, with lower circuit costs. In addition, the method can be implemented in a HUD processor with software, and implemented processes thereof are simple, and do not add excessive software computation for a HUD product.Brief Description of the Drawings

[0017] By means of the drawings incorporated herein, as well as particular embodiments, which are subsequently used together with the drawings for explaining certain principles of the present invention, other features and advantages of the method of the present invention are explained more clearly or more specifically.

[0018] Fig. 1 shows a flow chart of a self-test method for an image correction chip according to an exemplary embodiment of the present invention.Detailed Description of the Invention

[0019] A self-test method for a grayscale value abnormality monitoring module of an image correction chip according to the present invention is described below by means of embodiments with reference to the accompanying drawings. Many details are given in the following description so that those skilled in the art can understand the invention more comprehensively. However, it will be obvious to those skilled in the art that the invention can be realized without some of these details. On the contrary, one may consider using any combination of the following characteristics and elements to implement the invention, regardless of whether they relate to different embodiments. Therefore, the following various aspects, characteristics, embodiments and advantagesare only used to describe the invention, and should not be regarded as elements or definitions of the claims.

[0020] In a HUD product, a grayscale value abnormality of an image may lead to a problem of an image projected onto a front windshield of a vehicle being too bright, such that a driver's vision is easily momentarily blinded. Therefore, a grayscale value abnormality monitoring module is generally provided in an image correction chip, the grayscale value abnormality monitoring module analyses a grayscale value of a projected image, and, when the grayscale value is abnormal, outputs an abnormal state, thereby triggering a function safety abnormal state and raising an alarm, and thus avoiding, for example, a driver's vision being interfered with due to a grayscale value of an image being too large.

[0021] Specifically, the grayscale value abnormality monitoring module may recognize a grayscale value of an image, and judge whether the grayscale value of the image is beyond a normal range according to a set threshold value. As soon as an abnormal grayscale value is detected, the module may notify the image correction chip to output an abnormal state, to trigger a HUD function safety state and raise an alarm to notify a driver.

[0022] However, in an operating process, the grayscale value abnormality monitoring module itself may malfunction; therefore, it is necessary to monitor in real time whether the grayscale value abnormality monitoring function in the image correction chip remains normal. Current mainstream HUD products generally use a state monitoring function of the image correction chip itself to indirectly infer whether the function of the grayscale value abnormality monitoring module is normal. A dedicated self-test function of a grayscale value abnormality monitoring module is not provided in image correction chips widely used in current mainstream HUD products; therefore, it is not possible to directly monitor an operating state of the module, and a state monitoring function of the correction chip itself cannot fully cover all malfunction scenarios of a grayscale value abnormality monitoring module.

[0023] On the basis of the above background, the present invention proposes a self-test method for an image correction chip; this method can detect whether a grayscale abnormality upper limit monitoring function of an image correction chip of a HUD product is normal in real time, to prevent the grayscale abnormality monitoring module of the chip from freezing, thereby ensuring safe and reliable implementation of an image grayscale value abnormality monitoring function of the HUD product.

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

[0025] When an image grayscale value provided by an picture generation unit is greater than a set threshold value of an upper limit detection function, the image correction chip triggers a state register of the upper limit abnormality monitoring module to output an abnormal state value (for example, “1”), and otherwise output a normal state (for example, “0”). That is, a monitoring state result “1” indicates an abnormal grayscale value of an image, and a monitoring state result “0” indicates a normal grayscale value of an image.

[0026] By the same reasoning, when a grayscale value of an image provided by the picture generation unit is less than a set threshold value of a lower limit detection function, the image correction chip triggers the state register of the lower limit abnormality monitoring module to output an abnormal state (for example, “1”), and otherwise output a normal state (for example, “0”).

[0027] In a safety control strategy for preventing an output image grayscale value being too large in a HUD product, it is only necessary to use an upper limit abnormality monitoring function, and the lower limit abnormality monitoring function is in an idle state. Considering this background, the present invention proposes using a lower limit abnormality monitoring function of an image correction chip to monitor online a state of the grayscale value abnormality monitoring function itself. By means of alternately setting monitoring threshold values of a lower limit abnormality monitoring module(for example, for an eight-bit grayscale image, the monitoring threshold value may be alternately set to a maximum value OxFF and a minimum value OxOOJ, whether the lower limit abnormality monitoring module is in a malfunctioning (frozen) state is monitored; specifically, on the basis of the monitoring state result (“1” or “0”) of the state register, whether the grayscale value abnormality monitoring function is normal can be judged.

[0028] Specifically, if the monitoring state result of the register complies with an expected setting (that is, an actual read value and expected value are the same), then this shows that the abnormality monitoring function of the image correction chip works normally, and the upper limit abnormality monitoring module of the chip can still be used for realizing a safety control strategy for grayscale value upper limit abnormality monitoring of an output image in a HUD product. Conversely, if the monitoring state result of the register does not comply with an expected setting (for example, an expected value is “1” but an actual read value is “0”; or the expected value is “0” but the actual read value is “1”), then this shows that the abnormality monitoring function of the image correction chip malfunctions (is not normal); at this time, it is not suitable to still use the upper limit abnormality monitoring module of the chip for realizing a safety control strategy for grayscale value upper limit abnormality monitoring of an output image in a HUD product, and at the same time a function safety malfunction alarm is triggered.

[0029] Fig. 1 shows a flow chart of a self-test method for an image correction chip according to an exemplary embodiment of the present invention. The image correction chip has an integrated grayscale value abnormality monitoring module, which is used for monitoring whether a grayscale value of an image to be corrected is within a specified value range, and the grayscale value abnormality monitoring module specifically may comprise an upper limit abnormality monitoring module and a lower limit abnormality monitoring module, wherein when one of the two monitoring modules is in an operating state, the other is in an idle state. Under these circumstances, the present invention proposes using a monitoring module (for example, a lower limitabnormality monitoring module) in an idle state to monitor online a state of the grayscale value abnormality monitoring function itself.

[0030] With reference to Fig. 1, firstly the image correction chip must obtain an image to be corrected, for example, from an picture generation unit (PGU); inside the chip, an upper limit abnormality monitoring module is used to monitor whether a grayscale value of the image to be corrected is within a specified value range, for example, within an upper limit threshold value.

[0031] Thus, at this time, the lower limit abnormality monitoring module is in an idle state, and the module may be considered for use in performing self-testing of a grayscale value abnormality monitoring function, and the lower limit abnormality monitoring module is enabled for this. Subsequently, by means of alternately setting monitoring threshold values of a lower limit abnormality monitoring module in different cycles, whether the lower limit abnormality monitoring module is in a malfunctioning (frozen) state is monitored; specifically, on the basis of the monitoring state result (“1” or “0”) of the lower limit abnormality monitoring module, whether the grayscale value abnormality monitoring function is normal can be judged.

[0032] The upper limit abnormality monitoring module and the lower limit abnormality monitoring module may respectively comprise a state register, and a monitoring state result of the corresponding monitoring module is stored in the corresponding state register.

[0033] For example, for a grayscale value of an image to be corrected being 8-bit unsigned hexadecimal data, a monitoring threshold value of the lower limit abnormality monitoring module may be set to a minimum value 0x00 in a first cycle, and a first monitoring state result of this lower limit abnormality monitoring module is read. The monitoring threshold value is a minimum value; therefore, when the grayscale value abnormality monitoring module in the image correction chip functions normally, it is predicted that the grayscale values of the image to be corrected obtained from the picture generation unit should all be greater than this minimum value (that is,satisfying a grayscale lower limit requirement, the first monitoring state result should always be “0”). Conversely if this first monitoring state result is “1”, then this shows that the grayscale value abnormality monitoring module in the image correction chip malfunctions.

[0034] Subsequently, a monitoring threshold value of the lower limit abnormality monitoring module may be set to a maximum value OxFF in a second cycle, and a second monitoring state result of this lower limit abnormality monitoring module is read. The monitoring threshold is a maximum value; therefore, when the grayscale value abnormality monitoring module in the image correction chip functions normally, it is predicted that the grayscale values of the image to be corrected obtained from the picture generation unit should all be less than this maximum value (that is, not satisfying a grayscale lower limit requirement, the second monitoring state result should always be “1”). Conversely, if this second monitoring state result is “0”, then this shows that the grayscale value abnormality monitoring module in the image correction chip malfunctions.

[0035] In a predetermined duration, the first cycle and the second cycle are alternately cycled through repeatedly and the corresponding monitoring state results are read; if the first monitoring state result always remains “0” and the second monitoring state result always remains “1” in the predetermined duration, then this shows that the grayscale value abnormality monitoring module in the image correction chip functions normally. Otherwise, it shows that the monitoring module malfunctions (for example, freezes). That is, in this predetermined duration, the following processes are repeatedly alternately:1) a monitoring threshold value of the lower limit abnormality monitoring module is set to a minimum value 0x00 in a first cycle;2) a monitoring state result in a state register of the lower limit abnormality monitoring module is read, and this value should be “0” (that is, the actual grayscale value of the image meets the requirement to be greater than a minimum value);3) a monitoring threshold value of the lower limit abnormality monitoring module is set to a maximum value OxFF in a second cycle; and4) a monitoring state result in a state register of the lower limit abnormality monitoring module is read, and this value should be “1” (that is, the actual grayscale value of the image does not satisfy the requirement to be greater than a minimum value).

[0036] The above cycles are cycled through repeatedly: 1) - 2) - 3) - 4) - 1) - 2) - 3) - 4)...

[0037] As soon as a state register value of the lower limit abnormality monitoring module read in processes 2) and 4) above does not comply with predictions, the output of an alarm that grayscale value abnormality monitoring functions abnormally (freezes) is triggered, and the self-test program is exited, to trigger a HUD function safety state.

[0038] An exemplary embodiment of the present invention further proposes an image correction chip for a HUD, the image correction chip having an integrated grayscale value abnormality monitoring module, used for monitoring whether a grayscale value of an image to be corrected is within a specified value range; and a processor, the processor being configured to use the self-test method according to the present invention to detect whether the grayscale value abnormality monitoring module is functioning normally.

[0039] Another exemplary embodiment of the present invention further proposes an onboard display device, the device comprising: an picture generation unit, used for generating an image to be displayed by HUD, wherein the image, for example, may be an image related to driving information; the image correction chip described above, used for correcting an image generated by the picture generation unit; and a HUD module, used for projecting the corrected image onto a front windshield of a vehicle.

[0040] The self-test method for an image correction chip according to the present invention can detect whether a grayscale abnormality upper limit monitoring functionof an image correction chip is normal in real time, to prevent the grayscale value abnormality monitoring module of the chip from freezing, thereby ensuring safe and reliable implementation of an image grayscale value abnormality monitoring function of the HUD product. The method has lower requirements for an image correction chip, not requiring the chip to have a self-test function of a grayscale value abnormality monitoring module, and not requiring an additional new hardware circuit, with lower circuit costs. In addition, the method can be implemented in a HUD processor with software, and implemented processes thereof are simple, and do not add excessive software computation for a HUD product.

[0041] A person skilled in the art will understand that the steps in the method according to the present invention are not limited to implementation according to the above cited sequences. In addition, in the present invention, in addition to terms such as “include” and “comprise” meaning the steps that are directly and explicitly expressed in the description and claims, the technical solution of the present application does not exclude the circumstance where there are other steps that are not directly or explicitly expressed.

[0042] Although the present invention has been disclosed above with preferred embodiments, the present invention is not limited to this. Any change or modification made by those skilled in the art without departing from the spirit and scope of the present invention should be included in the scope of protection thereof. Thus, the scope of protection of the present invention shall be the scope defined by the claims.

Claims

Claims1. A self-test method for an image correction chip, the image correction chip having an integrated grayscale value abnormality monitoring module, which is used for monitoring whether a grayscale value of an image to be corrected is within a specified value range, and the grayscale value abnormality monitoring module comprising an upper limit abnormality monitoring module and a lower limit abnormality monitoring module, wherein when one of the upper limit abnormality monitoring module and the lower limit abnormality monitoring module is in an operating state, the other of the upper limit abnormality monitoring module and the lower limit abnormality monitoring module is in an idle state, wherein the method comprises the following steps: when said one of the upper / lower limit abnormality monitoring modules is in the operating state, enabling said other and setting a monitoring threshold value of said other; acquiring a grayscale value of an image to be corrected in real time and inputting the acquired grayscale value to said other; and reading a monitoring state result of said other, and judging whether the grayscale value abnormality monitoring module is functioning normally on the basis of the monitoring state result.

2. The self-test method for an image correction chip as claimed in claim 1, wherein a monitoring threshold value of the lower limit abnormality monitoring module is set as a first threshold value, wherein when a grayscale value of the image to be corrected is greater than the first threshold value, a monitoring state result of the lower limit abnormality monitoring module is a normal state; when the grayscale value of the image to be corrected is less than the first threshold value, a monitoring state result of the lower limit abnormality monitoring module is an abnormal state.

3. The self-test method for an image correction chip as claimed in claim 1, wherein a monitoring threshold value of the upper limit abnormality monitoring module is set as a second threshold value, wherein when a grayscale value of the image to be corrected is greater than the second threshold value, a monitoring state result of the upper limit abnormality monitoring module is an abnormal state; when the grayscale value of the image to be corrected is less than the second threshold value, a monitoring state result of the upper limit abnormality monitoring module is a normal state.

4. The self-test method for an image correction chip as claimed in any one of claims 1 to 3, wherein the grayscale value of the image to be corrected has a minimum value and a maximum value; when the upper limit abnormality monitoring module is in an operating state, a monitoring threshold value of the lower limit abnormality monitoring module is set as the minimum value in a first cycle, and a first monitoring state result of the lower limit abnormality monitoring module is read, wherein if the first monitoring state result is an abnormal state, this shows that the grayscale value abnormality monitoring module has malfunctioned.

5. The self-test method for an image correction chip as claimed in claim 4, wherein when the upper limit abnormality monitoring module is in an operating state, a monitoring threshold value of the lower limit abnormality monitoring module is set as the maximum value in a second cycle, and a second monitoring state result of the lower limit abnormality monitoring module is read, wherein if the second monitoring state result is a normal state, this shows that the grayscale value abnormality monitoring module has malfunctioned.

6. The self-test method for an image correction chip as claimed in claim 5, wherein the method further comprises: in a predetermined duration, the first cycle and the second cycle are repeated alternately, wherein if the first monitoring state result remains a normal state and thesecond monitoring state result remains an abnormal state in the predetermined duration, this shows that the grayscale value abnormality monitoring module functions normally.

7. The self-test method for an image correction chip as claimed in any one of claims 1 to 3, wherein the grayscale value of the image to be corrected is 8-bit unsigned hexadecimal data, and a value range of the grayscale value of the image to be corrected is 0x00 to OxFF.

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

9. An image correction chip for a HUD, wherein the image correction chip comprises: a grayscale value abnormality monitoring module, used for monitoring whether a grayscale value of an image to be corrected is within a specified range; and a processor, the processor being configured to use the self-test method as claimed in any one of claims 1 to 8 to detect whether the grayscale value abnormality monitoring module is functioning normally.

10. An onboard display device, wherein the onboard display device comprises: an picture generation unit, used for generating an image to be displayed by HUD; the image correction chip as claimed in claim 9, used for correcting an image generated by the picture generation unit; and a HUD module, used for projecting the corrected image onto a front windshield of a vehicle.

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