Methods, terminal devices, and related media for assessing mobile device imaging sharpness

Abstract

Embodiments of the present application provide a method for evaluating imaging definition of a mobile device, a terminal device and a related medium. The method is applied to the terminal device and includes: controlling the mobile device to capture a test card to obtain a plurality of target images, the test card can include a plurality of evaluation areas, different evaluation areas correspond to different evaluation elements, and the evaluation elements can include real scene elements; determining a plurality of image areas corresponding to the plurality of evaluation areas in each of the plurality of target images; determining a comprehensive definition score of each evaluation element based on a gray scale contrast of the plurality of image areas in each target image; and determining an imaging definition score of the mobile device based on the comprehensive definition score of each evaluation element. By implementing the method, the testing scene of the imaging capability of the mobile device is closer to the use scene of the user, the testing is more objective and accurate, and the imaging capability of the mobile device can be more comprehensively and accurately evaluated.

Description

Technical Field

[0001] This application relates to the field of computers, and in particular to methods, terminal devices, and related media for evaluating the imaging sharpness of mobile devices. Background Technology

[0002] With the increasing computing power of mobile devices, photography and / or video recording algorithms on mobile devices have also been optimized. Compared with traditional video recording algorithms on mobile devices, the new generation of algorithms can significantly improve the image quality of mobile devices in challenging scenarios such as low light and super telephoto by pre-learning from a large number of real-world scene images based on artificial intelligence image algorithms. However, complex deep learning algorithms are also prone to problems such as generating incorrect textures and uneven image quality. Moreover, the sharpness imaging problems caused by algorithms are completely different from those of traditional optical imaging, making it impossible for testers to evaluate the image sharpness of current mobile devices based on traditional test charts. Therefore, how to provide a new method to objectively and accurately evaluate the image quality of mobile devices is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0003] In a first aspect, this application provides a method for evaluating the imaging clarity of a mobile device, a terminal device, and a related medium. The method is applied to the terminal device, which is connected to the mobile device. The method may include:

[0004] The mobile device is controlled to capture test charts to obtain multiple target images. The test charts may include multiple evaluation areas, and different evaluation areas correspond to different evaluation elements. Evaluation elements may include real-world elements.

[0005] In each of the multiple target images, identify multiple image regions corresponding to multiple evaluation regions, wherein one image region corresponds to one evaluation region and one image region contains one evaluation element;

[0006] Based on the grayscale contrast of multiple image regions in each target image, a comprehensive sharpness score for each evaluation element is determined;

[0007] The image sharpness score of the mobile device is determined based on the comprehensive sharpness score of each evaluation element.

[0008] Implementing the method provided in the first aspect, the terminal device can obtain multiple target images by controlling the mobile device to capture test image cards, and evaluate the imaging capabilities of the mobile device by analyzing the sharpness of each target image. The test image cards can include multiple evaluation elements, such as real-scene elements. Adding real-scene elements to the test image cards helps to make the test scenarios or test objects for evaluating the mobile device's imaging capabilities closer to the user's actual shooting scenarios or objects, thereby enabling a more objective and accurate evaluation of the mobile device's actual shooting effects and contributing to improving the user's shooting experience. Currently, with the continuous iteration and optimization of mobile device hardware and software, the computing power of mobile devices has significantly increased compared to the past. To provide users with imaging effects closer to those of professional camera tools, current mobile devices on the market typically optimize the captured images based on image algorithms after using their cameras, and then present the optimized images to the user. Regarding sharpness testing tools, in existing technologies, technicians can evaluate the imaging capabilities of mobile devices by photographing test charts with multiple line pairs. However, after mobile devices optimize captured images using image algorithms, issues such as texture errors and uneven image quality may arise. Test charts with only line pairs cannot fully reveal these imaging problems during mobile device imaging tests. In other words, existing test charts have limitations and cannot cover the main imaging issues of current mobile devices. These shortcomings can lead to testers being unable to accurately assess the shooting performance of mobile devices, which is detrimental to user experience. In contrast, the method in this application provides a new test chart containing real-world elements, making the testing scenario or conditions for mobile device imaging capabilities closer to user scenarios or conditions. This makes the testing more objective and accurate, enabling a more comprehensive and accurate evaluation of the mobile device's imaging capabilities. In terms of sharpness testing and evaluation, existing technologies evaluate the sharpness imaging capabilities of mobile devices by comparing the similarity between the target image obtained by the mobile device from the test chart and a standard image, where the standard image is obtained based on the test chart. The difference is that the terminal device in the method of this application embodiment can calculate the first sharpness score corresponding to each evaluation element in each target image acquired by the mobile device; further, the terminal device in the method of this application embodiment can also determine the comprehensive sharpness score of each evaluation element based on the first sharpness score of each evaluation element in each target image; even further, the terminal device in the method of this application embodiment can also determine the sharpness score corresponding to the mobile device based on the comprehensive sharpness score of each evaluation element, thereby achieving the purpose of objectively and digitally evaluating the imaging capability of the mobile device.

[0009] Implementing the method provided in the first aspect, in some embodiments, controlling a mobile device to capture test image cards to obtain multiple target images may include:

[0010] Under multiple different shooting conditions, control the mobile device to shoot test charts to obtain multiple target images. Shooting conditions may include shooting distance and / or lighting parameters.

[0011] By implementing the method provided in the above embodiments, the terminal device can control the mobile device to capture images of the test image card under different shooting conditions, which helps to more comprehensively and objectively evaluate the imaging capabilities of the mobile device under different shooting conditions. Specifically, the terminal device can achieve the purpose of providing multiple different shooting conditions by adjusting the shooting distance between the mobile device and the test image card, and the lighting parameters of the lighting device.

[0012] In some embodiments, the method provided in the first aspect is implemented such that a connection exists between the terminal device and the lighting device;

[0013] Under multiple different shooting conditions, controlling a mobile device to capture test charts to obtain multiple target images may include:

[0014] The shooting distance between the mobile device and the test chart was adjusted multiple times;

[0015] After each adjustment of the shooting distance, adjust the lens focal length of the mobile device and the lighting parameters of the lighting equipment;

[0016] Control the mobile device to capture test charts to obtain multiple target images.

[0017] By implementing the method provided in the above embodiments, the terminal device can provide multiple shooting conditions by repeatedly adjusting the shooting distance between the test mobile device and the test image card, and by adjusting the lens focal length of the mobile device and the lighting parameters of the lighting device after each adjustment of the shooting distance. By providing multiple shooting conditions for the mobile device, it is helpful to more comprehensively and accurately evaluate the imaging capabilities of the mobile device, thereby ensuring the user experience.

[0018] In implementing the method provided in the first aspect, in some other embodiments, the method may further include:

[0019] After each adjustment of the shooting distance, the lighting parameters were adjusted multiple times;

[0020] After each adjustment of the lighting parameters, the mobile device is controlled to capture a test image of the test chart to obtain a target image.

[0021] By implementing the method provided in the above embodiments, the terminal device can adjust the lighting parameters multiple times after each adjustment of the shooting distance, thereby simulating the actual shooting scenarios or conditions of the user using the mobile device in a more diverse way. This helps to more objectively and accurately evaluate the imaging capabilities of the mobile device and ensure the user's experience.

[0022] In implementing the method provided in the first aspect, in some other embodiments, the lighting parameters may include color temperature and / or light intensity.

[0023] In implementing the method provided in the first aspect, in some other embodiments, an image region corresponds to one or more grayscale contrasts;

[0024] Based on the grayscale contrast of multiple image regions in each target image, a comprehensive sharpness score for each evaluation element is determined, which may include:

[0025] For any one of the multiple evaluation elements, the target gray-level contrast of any one evaluation element in each target image is determined based on one or more gray-level contrasts corresponding to each image region in each target image.

[0026] Based on the target grayscale contrast corresponding to any evaluation element in each target image, determine the overall sharpness score corresponding to any evaluation element.

[0027] By implementing the method provided in the above embodiments, the terminal device can first determine the target grayscale contrast of each evaluation element in each target image, and then determine the comprehensive sharpness score corresponding to each evaluation element, which helps to more accurately evaluate the imaging capability of the mobile device for each evaluation element.

[0028] In some embodiments, the image region includes multiple evaluation sites when implementing the method provided in the first aspect.

[0029] Determining the target grayscale contrast of the image region corresponding to any evaluation element in each target image can include:

[0030] For any evaluation element in any target image, determine multiple first gray-level contrasts corresponding to multiple evaluation sites in the image region, with one evaluation site corresponding to one first gray-level contrast.

[0031] Based on preset conditions, the target grayscale contrast is determined from multiple first grayscale contrast ratios.

[0032] By implementing the method provided in the above embodiments, the image region may include multiple evaluation sites, and the terminal device can determine multiple first grayscale contrasts within the corresponding image region in any target image. Furthermore, the terminal device can also determine the target grayscale value of the evaluation element corresponding to the image region in the target image from the multiple first grayscale contrasts, which helps to subsequently obtain a comprehensive sharpness score for the evaluation element, thereby helping testers to more objectively and accurately evaluate the imaging capabilities of mobile devices for various evaluation elements.

[0033] Implementing the method provided in the first aspect, in some other embodiments, determining a target grayscale contrast from a plurality of first grayscale contrasts based on preset conditions may include:

[0034] The grayscale contrast that is greater than a preset threshold and has the smallest difference from the preset threshold among multiple first grayscale contrasts is determined as the target grayscale contrast.

[0035] In implementing the method provided in the first aspect, in some other embodiments, the image region may include a texture region;

[0036] For any evaluation element in any target image, determining multiple first gray-level contrasts corresponding to multiple evaluation sites in the image region can include:

[0037] For any evaluation element in any target image, determine the local contrast and global contrast corresponding to each evaluation point in the image region. The local contrast represents the grayscale contrast of the texture area in the corresponding image region, and the global contrast represents the grayscale contrast between the texture area and the image region in the corresponding image region.

[0038] Based on local contrast and overall contrast, the first grayscale contrast corresponding to each evaluation point in the image region is determined.

[0039] By implementing the method provided in the above embodiments, the terminal device can determine the first grayscale contrast corresponding to each evaluation point based on local contrast and overall contrast. This helps to subsequently select a suitable target grayscale contrast, thereby assisting testers in more objectively and accurately evaluating the imaging capabilities of the mobile device for various evaluation elements. Local contrast can represent the grayscale contrast of the texture area within the corresponding image region, while overall contrast can represent the grayscale contrast between the texture area and the image region within the corresponding image region; both can be used to measure the imaging capabilities of the mobile device.

[0040] In implementing the method provided in the first aspect, in some other embodiments, the local contrast is the difference between the mean of the peak gray values ​​of the texture region and the mean of the trough gray values ​​of the texture region;

[0041] Overall contrast is the difference between the average gray value of the texture area and the maximum gray value of the image area.

[0042] Implementing the method provided in the first aspect, in some other embodiments, determining a comprehensive sharpness score corresponding to any evaluation element based on the target grayscale contrast corresponding to any evaluation element in each target image may include:

[0043] The evaluation site corresponding to the target gray-scale contrast is determined as the target evaluation site;

[0044] Based on the target evaluation site and sharpness score mapping table, the first sharpness score corresponding to any evaluation element in any target image is determined. The sharpness score mapping table is used to record the mapping relationship between evaluation sites and sharpness scores.

[0045] Based on the first sharpness score corresponding to any evaluation element in each target image, determine the comprehensive sharpness score corresponding to any evaluation element.

[0046] By implementing the method provided in the above embodiments, the terminal device can determine the target evaluation site based on the target grayscale contrast, determine the first sharpness score of each evaluation element in any target image based on the sharpness score mapping table, and finally determine the comprehensive sharpness score corresponding to each evaluation element based on the first sharpness score of each evaluation element in each target image. This helps testers to more objectively and accurately evaluate the imaging capabilities of mobile devices for various evaluation elements.

[0047] In implementing the method provided in the first aspect, in some other embodiments, the real-world elements may include at least one of plants, text, and buildings.

[0048] By implementing the method provided in the above embodiments, the terminal device can simulate objects that the user actually takes pictures of while using the mobile device, based on at least one of plants, text, and buildings. This helps to more accurately reproduce the user's usage scenario or conditions, thereby making the assessment of the mobile device's imaging capabilities more accurate.

[0049] In implementing the method provided in the first aspect, in some other embodiments, the form of the real-world elements may include images and / or 3D models.

[0050] By implementing the methods provided in the above embodiments, technicians can choose different forms to combine real-scene elements with test charts according to actual conditions. For example, the form of real-scene elements can be pictures and / or three-dimensional models, which helps to simulate the shooting objects encountered by users in the actual use of mobile devices from different forms, thereby helping to further improve the accuracy of this evaluation of the imaging capabilities of mobile devices.

[0051] Secondly, embodiments of this application provide a terminal device that is connected to a mobile device. The terminal device may include a control module and a processing module.

[0052] The control module can be used to control the mobile device to capture test charts to obtain multiple target images. The test charts can include multiple evaluation areas, and different evaluation areas correspond to different evaluation elements. Evaluation elements can include real-world elements.

[0053] The processing module can be used to determine multiple image regions in each of multiple target images that correspond to multiple evaluation regions, wherein one image region corresponds to one evaluation region and one image region contains one evaluation element;

[0054] The processing module can also be used to determine the overall sharpness score of each evaluation element based on the grayscale contrast of multiple image regions in each target image;

[0055] The processing module can also be used to determine the image sharpness score of a mobile device based on a comprehensive sharpness score of each evaluation element.

[0056] In implementing the method provided in the second aspect, in some embodiments, the terminal device may further include:

[0057] The control module can also be used to control a mobile device to capture test charts to obtain multiple target images under multiple different shooting conditions. Shooting conditions may include shooting distance and / or lighting parameters.

[0058] In some embodiments, the method provided in the second aspect is implemented such that a connection exists between the terminal device and the lighting device.

[0059] The terminal device may also include:

[0060] The control module can also be used to adjust the shooting distance between the mobile device and the test chart multiple times;

[0061] The control module can also be used to adjust the lens focal length of the mobile device and the lighting parameters of the lighting device after each adjustment of the shooting distance;

[0062] The control module can also be used to control mobile devices to capture test charts to obtain multiple target images.

[0063] In implementing the method provided in the second aspect, in some other embodiments, the terminal device may further include:

[0064] The control module can also be used to adjust the lighting parameters multiple times after each adjustment of the shooting distance;

[0065] The control module can also be used to control the mobile device to capture a test chart to obtain a target image after each adjustment of the lighting parameters.

[0066] In implementing the method provided in the second aspect, in some other embodiments, the lighting parameters may include color temperature and / or light intensity.

[0067] In implementing the method provided in the second aspect, in some other embodiments, an image region corresponds to one or more grayscale contrasts;

[0068] The terminal device may also include:

[0069] The processing module can also be used to determine the target gray-level contrast of any evaluation element in each target image based on one or more gray-level contrasts corresponding to each image region in each target image for any evaluation element among multiple evaluation elements.

[0070] The processing module can also be used to determine the overall sharpness score corresponding to any evaluation element based on the target grayscale contrast corresponding to any evaluation element in each target image.

[0071] In implementing the method provided in the second aspect, in some embodiments, the image region contains multiple evaluation sites;

[0072] The terminal device may also include:

[0073] The processing module can also be used to determine multiple first gray-level contrasts corresponding to multiple evaluation sites in the image region corresponding to any evaluation element in any target image, with one evaluation site corresponding to one first gray-level contrast.

[0074] The processing module can also be used to determine the target grayscale contrast from multiple first grayscale contrasts based on preset conditions.

[0075] In implementing the method provided in the second aspect, in some other embodiments, the terminal device may further include:

[0076] The processing module can also be used to determine the grayscale contrast that is greater than a preset threshold and has the smallest difference from the preset threshold among multiple first grayscale contrasts as the target grayscale contrast.

[0077] In implementing the method provided in the second aspect, in some other embodiments, the image region may include a texture region;

[0078] The terminal device may also include:

[0079] The processing module can also be used to determine the local contrast and global contrast of each evaluation point in the image region corresponding to any evaluation element in any target image. The local contrast represents the grayscale contrast of the texture area in the corresponding image region, and the global contrast represents the grayscale contrast between the texture area and the image region in the corresponding image region.

[0080] The processing module can also be used to determine the first grayscale contrast corresponding to each evaluation point in the image region based on local contrast and overall contrast.

[0081] In implementing the method provided in the second aspect, in some other embodiments, the local contrast is the difference between the mean of the peak gray values ​​of the texture region and the mean of the trough gray values ​​of the texture region;

[0082] Overall contrast is the difference between the average gray value of the texture area and the maximum gray value of the image area.

[0083] In implementing the method provided in the second aspect, in some other embodiments, the terminal device may further include:

[0084] The processing module can also be used to determine the evaluation site corresponding to the target grayscale contrast as the target evaluation site;

[0085] The processing module can also be used to determine the first sharpness score corresponding to any evaluation element in any target image based on the target evaluation site and the sharpness score mapping table. The sharpness score mapping table is used to record the mapping relationship between the evaluation site and the sharpness score.

[0086] The processing module can also be used to determine the overall sharpness score corresponding to any evaluation element based on the first sharpness score corresponding to any evaluation element in each target image.

[0087] In implementing the method provided in the second aspect, in some other embodiments, the real-world elements may include at least one of plants, text, and buildings.

[0088] In implementing the method provided in the second aspect, in some other embodiments, the form of the real-world elements may include images and / or 3D models.

[0089] Thirdly, embodiments of this application provide a terminal device, which may include one or more processors and one or more memories; wherein the one or more memories are coupled to one or more processors, and the one or more memories are used to store computer program code, the computer program code including computer instructions, which, when the one or more processors execute the computer instructions, cause the execution of the method described in the first aspect and any possible implementation thereof.

[0090] Fourthly, embodiments of this application provide a chip including logic circuitry and an interface, wherein the logic circuitry and the interface are coupled; the interface is used to input and / or output code instructions, and the logic circuitry is used to execute the code instructions to cause the method in the first aspect or any possible implementation thereof to be executed.

[0091] Fifthly, this application provides a computer-readable storage medium including instructions that, when executed on a terminal device, cause the execution of the method described in the first aspect and any possible implementation thereof.

[0092] In a sixth aspect, this application provides a computer program product containing instructions that, when the computer program product is run on a terminal device, causes the terminal device to perform the method described in the first aspect and any possible implementation thereof.

[0093] It is understood that the terminal devices provided in the second and third aspects, the chip provided in the fourth aspect, the computer-readable storage medium provided in the fifth aspect, and the computer program product provided in the sixth aspect are all related to the method for evaluating the image clarity of a mobile device provided in the first aspect, and can be used to execute the method provided in this application. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects of the method for evaluating the image clarity of a mobile device in the corresponding first aspect, and will not be repeated here. Attached Figure Description

[0094] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0095] Figure 1 This is a schematic diagram of a scenario for evaluating the imaging capabilities of a mobile device, provided in an embodiment of this application.

[0096] Figure 2 This is a schematic diagram of a system architecture for evaluating the imaging sharpness of a mobile device, provided in an embodiment of this application.

[0097] Figure 3 This is a flowchart illustrating a method for evaluating the imaging sharpness of a mobile device according to an embodiment of this application;

[0098] Figure 4 This is a schematic diagram illustrating the composition of a test chart provided in an embodiment of this application;

[0099] Figure 5 This is a schematic diagram illustrating the composition of a positioning image region provided in an embodiment of this application;

[0100] Figure 6 This is a schematic diagram illustrating the composition of a target image provided in an embodiment of this application;

[0101] Figure 7 This is a schematic diagram of a scene for determining the first grayscale contrast provided in an embodiment of this application;

[0102] Figure 8 This is a schematic diagram of a scene for calculating grayscale contrast provided in an embodiment of this application;

[0103] Figure 9This is a schematic diagram of a scenario for determining the target grayscale value corresponding to a text area, provided in an embodiment of this application.

[0104] Figure 10 This is a comparative schematic diagram illustrating the differences in imaging capabilities between different devices, provided in an embodiment of this application.

[0105] Figure 11 This is a flowchart illustrating another method for evaluating the imaging sharpness of a mobile device provided in an embodiment of this application;

[0106] Figure 12 This is a schematic diagram of the composition of a terminal device provided in an embodiment of this application;

[0107] Figure 13 This is a schematic diagram of the hardware structure of a terminal device;

[0108] Figure 14 It is a software structure diagram. Detailed Implementation

[0109] The technical solutions in the embodiments of this application will be clearly and thoroughly described below with reference to the accompanying drawings. The terminology used in the following embodiments of this application is for the purpose of describing specific embodiments only and is not intended to be a limitation of this application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "the," "the," "the," and "this" are intended to include the plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that in the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B; "and / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist, for example, A and / or B can represent: A alone, A and B simultaneously, and B alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0110] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature, and in the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more.

[0111] The term "user interface (UI)" used in the following embodiments of this application refers to the medium interface through which an application or operating system interacts and exchanges information with the user. It realizes the conversion between the internal form of information and the form that the user can accept. The user interface is source code written in a specific computer language such as Java or Extensible Markup Language (XML). The interface source code is parsed and rendered on the terminal device, ultimately presenting content that the user can recognize. A common form of user interface is the graphical user interface (GUI), which refers to a user interface related to computer operation displayed graphically. It can be visible interface elements such as text, icons, buttons, menus, tabs, text boxes, dialog boxes, status bars, navigation bars, and widgets displayed on the terminal device's screen.

[0112] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the related technologies is given first:

[0113] (1) Resolution test card

[0114] A resolution test chart is a specialized tool used to evaluate the performance of various imaging devices, such as cameras, scanners, printers, and monitors. By setting precise patterns and details on the resolution test chart, it is possible not only to quantitatively evaluate the device's resolution but also to assess key indicators such as contrast, sharpness, color accuracy, and distortion.

[0115] (2) Color temperature and luminous intensity

[0116] Color temperature is a unit of measurement indicating the color component in light. It is a physical quantity expressed in Kelvin (K) and used to describe the color characteristics of a light source. The concept of color temperature is based on the blackbody radiation theory: when a blackbody is heated to a certain temperature, the color of the light it emits is the same as the color of a certain light source; this temperature is then defined as the color temperature of that light source.

[0117] Luminous intensity, often simply called light intensity or luminance in photometry, is a physical quantity used to express the luminous flux per unit solid angle in a given direction of a light source. Its SI unit is the candela (cd). The definition of luminous intensity takes into account human visual factors and optical characteristics, and is based on human vision. Because the relative visibility of the human eye differs for different wavelengths of light, the luminous flux of light of equal radiant power is not equal.

[0118] (3) Dynamic range of the image

[0119] The dynamic range of an image refers to the range of brightness between the brightest and darkest parts of the image. Dynamic range can be expressed in decibels (dB), bits, or stops. In photography and image processing, dynamic range is often used to describe the range of light intensity distribution from the darkest shadows to the brightest highlights in an image. The larger the dynamic range, the richer the tonal range that the image can represent, and the wider the color space it encompasses.

[0120] In photography, dynamic range is also known as latitude. A wider dynamic range means that a camera can record details in both bright and dark areas of a high-contrast scene without blown-out highlights or lost shadow details. A camera's dynamic range is generally 10-15 stops, while higher-end cameras or professional video camcorders can achieve even greater ranges. During actual shooting, it's crucial to pay attention to the actual dynamic range of the scene and the camera's ability to capture that range, ensuring that all details from dark to bright areas are recorded; otherwise, "buried white" or "buried black" images may appear.

[0121] (4) Texture, detail texture, and image texture test

[0122] Texture generally refers to the visual and tactile experience derived from the quality of an object's surface.

[0123] Texture detail refers to mid-to-high spatial frequency image content, such as leaves, grass, and fabric. Texture detail can also be understood as the representation of the textural details of objects in an image scene, and it is one of the standards for judging image quality. Loss of texture detail in an image means that some high-frequency information has been lost in areas of dense texture.

[0124] Image texture testing can be used to evaluate image quality, especially for image content with mid-to-high spatial frequencies (such as detailed textures of leaves, grass, and fabric). By performing image texture testing, it can be determined whether some high-frequency information has been lost during dense texture processing, thus assessing the overall image quality. Image texture testing can analyze the loss of low contrast and detailed textures in images due to noise reduction or other image processing techniques, thereby helping to optimize image processing algorithms, reduce the loss of detailed textures during processing, and improve the visual effect of the image.

[0125] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating a scenario for evaluating the imaging capabilities of a mobile device, provided in an embodiment of this application. Figure 1 As shown, there is a connection between the terminal device 100 and the mobile device 110. The terminal device 100 can evaluate the imaging capability of the mobile device 110 by analyzing the image obtained by the mobile device 110 from the resolution test card.

[0126] like Figure 1 As shown, the resolution test chart can be configured with various line pairs to help detect the visual resolution and limit resolution of the mobile device 110 (or the camera of the mobile device 110). Optionally, the resolution test chart is photographed by the mobile device 110 to obtain... Figure 1 After viewing the first image, the terminal device 100 can evaluate the imaging capability of the mobile device 110 based on the grayscale value of the first image or the image similarity between the first image and the test chart.

[0127] In some possible implementations, suppose that during pre-shipment testing, technicians determine that the imaging capability of the mobile device 110 meets factory standards based on a resolution test card. If, during actual use, due to defects in the image optimization algorithm, the second image captured by the user using the mobile device 110 for the first scene exhibits... Figure 1 The imaging problems shown, such as incorrect texture generation and uneven image quality, indicate that the testing conducted on the mobile device 110 before it left the factory was incomplete and inaccurate. Figure 1 It can be seen that relying solely on test charts with line pairs is insufficient to fully reveal the aforementioned imaging problems during the imaging test of the mobile device 110. Figure 1 The resolution test charts in the text have limitations and cannot cover the main imaging issues of mobile devices 110. Understandably, the inadequacy of the testing technology can easily lead to testers being unable to accurately evaluate the shooting performance of mobile devices, which is detrimental to ensuring a good user experience.

[0128] Therefore, to ensure a more comprehensive and accurate assessment of the imaging capabilities of the mobile device 110, this application provides a method for evaluating the image clarity of a mobile device. The terminal device 100 can obtain multiple target images by controlling the mobile device to capture images from a test chart, and evaluate the imaging capabilities of the mobile device by analyzing the clarity of each target image. The test chart may include multiple evaluation elements, such as real-scene elements. Adding real-scene elements to the test chart helps to make the test scenario or test object for the mobile device's imaging capabilities closer to the user's actual shooting scenario or object, thereby enabling a more comprehensive and accurate evaluation of the mobile device's actual shooting effect and contributing to improving the user's shooting experience.

[0129] Please see Figure 2 , Figure 2 This is a schematic diagram of a system architecture for evaluating the imaging sharpness of a mobile device, provided as an embodiment of this application. Figure 2As shown, the system may include: a terminal device 100, a mobile device 110, a sliding rail 120, a lighting device 130, and a test chart 140, wherein the terminal device 100 is connected to the mobile device 110, the sliding rail 120, and the lighting device 130. Optionally, a bracket 121 is provided on the sliding rail 120, and the mobile device 110 can be fixed to the bracket 121.

[0130] Optionally, the terminal device 100 can adjust the shooting distance between the mobile device 110 and the test chart using the sliding rail 120 and the bracket 121; the terminal device 100 can also adjust the lens focal length of the mobile device 110; the terminal device 100 can also adjust the lighting parameters of the lighting device 130, wherein the lighting parameters may include color temperature and / or light intensity. It should be noted that the terminal device 100 can also be referred to as User Equipment (UE), user terminal, access terminal equipment, UE unit, UE station, mobile device, UE terminal equipment, mobile terminal, wireless communication equipment, UE agent, or UE device, etc. Its specific form can be a personal computer, a handheld or wearable device, or other intelligent terminal device that requires a device to run a system; it is an entity on the user side used to receive or transmit signals. It can be a handheld or wearable device, specifically a laptop, desktop computer, mobile phone, tablet, smartwatch, or other wearable device, etc. The specific structure of the terminal device 100 can be referenced. Figures 12 to 14 The relevant details will not be elaborated upon here.

[0131] Optionally, the sliding direction of the bracket 121 on the sliding track 120 can be the front-to-back direction, or it can be understood as "the direction perpendicular to the plane where the test chart 140 is located, and closer to or farther from the test chart 140".

[0132] Optionally, the mobile device 110 is equipped with a camera, which can be understood as a device with imaging capabilities.

[0133] In some possible implementations, the connectivity between the terminal device 100 and the mobile device 110, the sliding rail 120, and the lighting device 130 can be a wired connection based on a data cable, or a wireless connection based on Bluetooth or other wireless communication methods. It should be noted that the specific connection method is set by a technician according to the actual situation, and this application embodiment does not limit it.

[0134] In some other possible implementations, additional light sources may be added near the test chart 140 to give the target image a different dynamic range.

[0135] Please see Figure 3 , Figure 3 This is a flowchart illustrating a method for evaluating the image sharpness of a mobile device, provided as an embodiment of this application. Figure 3 As shown, the method may include:

[0136] S301: The terminal device controls the mobile device to capture test image cards to obtain multiple target images.

[0137] The test chart can include multiple evaluation areas, with different evaluation areas corresponding to different evaluation elements, which can include real-world elements.

[0138] Optionally, the real-world elements may include at least one of plants, text, and architecture. The real-world elements may take the form of images and / or 3D models.

[0139] For example, please see Figure 4 , Figure 4 This is a schematic diagram illustrating the composition of a test chart provided in an embodiment of this application. Figure 4 As shown, the test chart 140 may include multiple evaluation areas, such as a positioning area, a straight line area, a curve area, a text area, a plant area, a building area, a texture test area, a diagonal line test area, and a resolution test area.

[0140] The positioning area can be used to help the terminal device 100 determine the image region corresponding to the test image card 140 from various target images captured by the mobile device 110. For example, please refer to... Figure 5 , Figure 5 This is a schematic diagram illustrating the composition of a positioning image region provided in an embodiment of this application. For example... Figure 5 As shown, the mobile device 110 obtained a third image by capturing a test image card 140. It can be seen that the third image not only contains a first region corresponding to the test image card 140, but also a second region corresponding to the shooting scene. To ensure that the terminal device 100 can accurately evaluate the imaging capability of the mobile device 110, the terminal device 100 needs to precisely locate the first region and perform sharpness analysis and evaluation based on the first region. To achieve this, the terminal device 100 can use a positioning area to precisely locate the first region in the third image, thereby performing subsequent evaluation operations. Optionally, the evaluation element corresponding to the positioning area can be called a positioning element, or the positioning area may not contain evaluation elements.

[0141] Optionally, the straight line area and the curved line area can be set as follows: Figure 1The multiple line pairs with varying densities shown can be used to test or evaluate the resolution of the mobile device 110. Understandably, the evaluation elements corresponding to the straight line areas can be straight line pairs, and the evaluation elements corresponding to the curved line areas can be curved line pairs. In the line pair areas such as the straight line areas and / or curved line areas, numerical labels can be marked to the left of the line pair elements (e.g., ...). Figure 4 The numbers 4, 5, 6, 7, 8, 10, 12, 14, and 16 shown are used to facilitate the subsequent determination of the first sharpness score of the line pair elements based on the sharpness score mapping table.

[0142] Optionally, the text area, plant area, and building area can be understood as evaluation areas corresponding to real-world elements. These areas can be used to evaluate the imaging capabilities of the mobile device 110 for real-world elements, making the test scenarios or test objects for evaluating the mobile device's imaging capabilities closer to the user's actual shooting scenarios or objects. This allows for a more objective and accurate evaluation of the mobile device's actual shooting effects and helps improve the user's shooting experience. Understandably, the evaluation elements for the text area can be Chinese characters, English letters, etc.; the evaluation elements for the plant area can be local details of plants (such as flowers, leaves, branches, etc.) or the overall shape of plants; and the evaluation elements for the building area can be various types of buildings.

[0143] For example, the evaluation elements of the plant area can be printed onto the corresponding area of ​​the test card 140 by printing images, or plant models or media containing plant images (such as paper) can be pasted onto the corresponding area of ​​the test card 140. For example, an image corresponding to the vision chart can be placed in the text area and the test card 140 can be printed out, or a plant photograph can be pasted onto the plant area, or a plant-shaped toy or model can be pasted onto the plant area.

[0144] For example, the assessment elements in the text area can be printed onto the corresponding area of ​​the test card 140 as printed images, or paper with text or a medium containing text images (such as paper) can be pasted onto the corresponding area of ​​the test card 140. For instance, placing the corresponding image of the vision chart in the text area and printing out the test card 140, or pasting the vision chart into the text area. The text area can be labeled with numerical codes to the right of the text elements (e.g., ...). Figure 4 The numbers shown are 5, 6, 7, 8, 9, 10, to facilitate the subsequent determination of the first clarity score of the text element based on the clarity score mapping table.

[0145] For example, the evaluation elements of the building area can be printed onto the corresponding area of ​​the test card 140 by printing images, or building models or media containing building images (such as paper) can be pasted onto the corresponding area of ​​the test card 140. For example, placing an image of the Eiffel Tower in the building area and printing out the test card 140, or pasting a model of the Eiffel Tower into the building area. The building area can be marked with an "X" shaped structure (e.g., ...) at different locations of the building elements. Figure 4 The bottom layer X, lower middle layer X, lower upper layer X, upper upper layer X, and upper middle layer X shown are used to facilitate the subsequent determination of the first sharpness score of the building elements based on the sharpness score mapping table.

[0146] It should be noted that the examples of real-scene elements given above are only for more specific and detailed illustration of the methods in the embodiments of this application, and do not mean that real-scene elements can only be the above-mentioned text, plants and buildings. Those skilled in the art can choose appropriate types of real-scene elements based on the actual situation, and the embodiments of this application do not limit them here.

[0147] Optionally, the texture test area can be used to perform image texture testing on the mobile device 110, enabling the terminal device 100 to evaluate whether the image optimization algorithm of the mobile device 110 will cause problems such as loss of detail texture, thereby allowing the terminal device 100 to more comprehensively and accurately evaluate the imaging capabilities of the mobile device 110. It is understood that the aforementioned imaging capabilities can include the hardware capabilities of the mobile device 110's camera and the software capabilities of the mobile device 110's image optimization. It is also understood that the evaluation element corresponding to the texture test area can be a texture test pattern.

[0148] Optionally, the diagonal test area can be used to perform a sharpening test on the mobile device 110, allowing the terminal device 100 to evaluate the image sharpening level of the mobile device 110. Understandably, the evaluation elements corresponding to the diagonal test area can be diagonal line pairs.

[0149] Optionally, the resolution test area can be used to perform contrast resolution testing on the mobile device 110. Specifically, the terminal device 100 can measure the different brightness levels between the brightest white and the darkest black in the discernible object's bright and dark areas in the target image by calculating the contrast resolution of the target image obtained by the mobile device 110 from the test image card 140. Higher contrast resolution results in clearer images and stronger sense of depth. Understandably, the evaluation element corresponding to the resolution test area can be a contrast resolution test pattern.

[0150] It should be noted that, Figure 4 The location settings for various evaluation areas are merely for illustrating the methods in the embodiments of this application in a more specific and detailed manner, and do not imply that various evaluation areas can only be set according to... Figure 4The corresponding area locations are set, and technicians can adjust the positions of various evaluation areas in the test chart according to the actual situation. This application embodiment does not limit this.

[0151] In some possible implementations, controlling a mobile device to capture test image cards to obtain multiple target images may include:

[0152] Under various shooting conditions, the terminal device controls the mobile device to capture test charts to obtain multiple target images. Shooting conditions may include shooting distance and / or lighting parameters.

[0153] Optionally, lighting parameters may include color temperature and / or light intensity.

[0154] For example, the terminal device 100 can set different shooting conditions by controlling the shooting distance between the mobile device 110 and the test chart 140. Optionally, the terminal device 100 can also set different shooting conditions by controlling the color temperature and light intensity of the light emitted by the lighting device 130. Optionally, the terminal device 100 can set multiple different shooting conditions by repeatedly adjusting the above-mentioned shooting distance and / or the above-mentioned lighting parameters. It can be seen that by setting different shooting conditions, the terminal device 100 helps to more comprehensively and objectively evaluate the imaging capabilities of the mobile device under different shooting conditions.

[0155] In other possible implementations, controlling a mobile device to capture test charts to obtain multiple target images under multiple different shooting conditions may include:

[0156] The terminal device repeatedly adjusted the shooting distance between the mobile device and the test image card;

[0157] After each adjustment of the shooting distance, the terminal device adjusts the lens focal length of the mobile device and the lighting parameters of the lighting equipment;

[0158] The terminal device controls the mobile device to capture test image cards to obtain multiple target images.

[0159] For example, suppose the terminal device 100 adjusts the shooting distance m times, and after each adjustment, adjusts the lens focal length of the mobile device 110. This helps ensure that the target image acquired by the mobile device 110 is clear. Optionally, while adjusting the lens focal length of the mobile device 110, the terminal device 100 can also adjust the lighting parameters of the lighting device, thereby setting multiple different shooting conditions. This helps to more comprehensively and accurately evaluate the imaging capabilities of the mobile device, thus ensuring the user experience.

[0160] In other possible implementations, the method of this application embodiment may further include:

[0161] After each adjustment of the shooting distance, the terminal device adjusted the lighting parameters multiple times;

[0162] After each adjustment of the lighting parameters, the terminal device controls the mobile device to capture a test image of the test chart to obtain a target image.

[0163] For example, suppose terminal device 100 can adjust the shooting distance between mobile device 110 and test image card 140 in m steps, and after each adjustment of the shooting distance, terminal device 100 can adjust the illumination parameters of illumination device 130 in n steps. Further, after each adjustment of the illumination parameters, terminal device 100 can control mobile device 110 to capture a target image of the test image card. Understandably, in the imaging capability test of mobile device 110, terminal device 100 can control mobile device 110 to acquire m*n target images, where m and n are both positive integers.

[0164] For example, the terminal device 100 adjusts the shooting distance between the mobile device 110 and the test chart 140 m times, and adjusts the lighting parameters of the lighting device 130 once after each adjustment of the shooting distance. Further, while (or after) adjusting the lighting parameters of the lighting device 130, the terminal device 100 can adjust the lens focal length of the mobile device 110 once (set as the first lens focal length), and acquire a target image once after completing the adjustment of the lens focal length and lighting parameters. Furthermore, during the subsequent n-1 adjustments of the lighting parameters, the lens focal length of the mobile device 110 can always maintain the aforementioned first lens focal length. That is, during the m-times of adjusting the shooting distance, the terminal device 100 can adjust the lens focal length of the mobile device 110 m times (one shooting distance corresponds to one lens focal length); or, each time the lighting parameters of the lighting device 130 are adjusted, the terminal device 100 can also adjust the lens parameters of the mobile device 110 once. That is, during the m*n-times of adjusting the lighting parameters, the terminal device 100 can adjust the lens focal length of the mobile device 110 m*n times (each adjustment of the lighting parameters corresponds to one adjustment of the lens focal length).

[0165] Optionally, the types of lighting parameters can be the same or different for different shooting distances.

[0166] For example, suppose terminal device 100 adjusts the shooting distance 4 times. For the i-th shooting distance (i≤4, i is a positive integer), the lighting parameters are adjusted 3 times. These three lighting parameters are the first lighting parameter, the second lighting parameter, and the third lighting parameter, respectively. If the types of lighting parameters corresponding to different shooting distances are the same, then the shooting environment corresponding to the target image captured by mobile device 110 during this test can be referred to Table 1.

[0167] Table 1

[0168]

[0169]

[0170] For example, suppose the terminal device 100 adjusts the shooting distance 4 times. For the i-th shooting distance (i≤4, i is a positive integer), the lighting parameters are adjusted 3 times. If the types of lighting parameters corresponding to different shooting distances are the same, then the shooting environment corresponding to the target image captured by the mobile device 110 during this test can be referred to Table 2.

[0171] Table 2

[0172]

[0173] S302: The terminal device determines multiple image regions in each of the multiple target images that correspond to multiple evaluation regions.

[0174] In this system, one image region corresponds to one evaluation region, and one image region contains one evaluation element.

[0175] For example, please see Figure 6 , Figure 6 This is a schematic diagram illustrating the composition of a target image provided in an embodiment of this application. For example... Figure 6 As shown, the target image contains a first region corresponding to the test image card 140, and a second region corresponding to the shooting scene. Within the first region, there are also a first image region, a second image region, a third image region, a fourth image region, a fifth image region, a sixth image region, a seventh image region, an eighth image region, a ninth image region, a tenth image region, an eleventh image region, and a twelfth image region.

[0176] Combination Figure 4 It can be assumed that the first, second, third, and fourth image regions correspond to the positioning area, the fifth image region corresponds to the straight line area, the sixth image region corresponds to the curve area, the seventh image region corresponds to the text area, the eighth image region corresponds to the plant area, the ninth image region corresponds to the building area, the tenth image region corresponds to the texture test area, the eleventh image region corresponds to the diagonal line test area, and the twelfth image region corresponds to the resolution test area.

[0177] Optionally, the evaluation elements contained in (or corresponding to) the fifth image region can be considered as straight line pairs, the evaluation elements contained in (or corresponding to) the sixth image region as curved line pairs, the evaluation elements contained in (or corresponding to) the seventh image region as text, the evaluation elements contained in (or corresponding to) the eighth image region as plants, the evaluation elements contained in (or corresponding to) the ninth image region as buildings, the evaluation elements contained in (or corresponding to) the tenth image region as texture test patterns, the evaluation elements contained in (or corresponding to) the eleventh image region as diagonal line pairs, and the evaluation elements contained in (or corresponding to) the twelfth image region as contrast resolution test patterns.

[0178] S303: The terminal device determines the overall sharpness score of each evaluation element based on the grayscale contrast of multiple image regions in each target image.

[0179] One image region corresponds to one or more grayscale contrasts.

[0180] Optionally, in the method of this application embodiment, the terminal device 100 can determine the comprehensive sharpness score of each evaluation element based on the grayscale contrast of multiple image regions in each target pattern. Since each image region can contain multiple evaluation points, and each evaluation point corresponds to a first grayscale contrast, one image region can correspond to multiple grayscale contrasts. Furthermore, the terminal device 100 selects a target grayscale contrast from the multiple grayscale contrasts corresponding to each image region as the basis for the sharpness score of the evaluation element corresponding to each image region, which helps to more accurately evaluate the imaging capability of the mobile device for each evaluation element.

[0181] In some possible implementations, determining the overall sharpness score for each evaluation element based on the grayscale contrast of multiple image regions in each target image may include:

[0182] For any one of the multiple evaluation elements, the terminal device determines the target gray-level contrast of any one evaluation element in each target image based on one or more gray-level contrasts corresponding to each image region in each target image.

[0183] The terminal device determines the overall sharpness score corresponding to any evaluation element based on the target grayscale contrast of any evaluation element in each target image.

[0184] For example, combined Figure 6Suppose that terminal device 100 controls mobile device 110 to acquire m*n target images (where m corresponds to the number of times the shooting distance is adjusted, and n corresponds to the number of times the lighting parameters are adjusted at a single shooting distance, and m and n are positive integers). If it is necessary to calculate the comprehensive sharpness score of the line pair, then it is necessary to calculate the first sharpness score corresponding to the line pair in each of the m*n target images, and finally determine the comprehensive sharpness score of the line pair based on the m*n first sharpness scores. Optionally, terminal device 100 can use the mean or weighted average of the m*n first sharpness scores as the comprehensive sharpness score of the line pair. The first sharpness score corresponding to the line pair in each of the m*n target images is related to the target grayscale contrast corresponding to the line pair in each of the m*n target images.

[0185] Similarly, the overall sharpness score for other types of evaluation elements (such as real-world elements, diagonal lines, texture test patterns, etc.) can also be calculated in the same way. It should be noted that the calculation method for determining the overall sharpness score based on m*n first sharpness scores is not limited to using an average or weighted average. Technicians can set an appropriate calculation method according to the actual situation, and this application does not impose any restrictions here.

[0186] In some other possible implementations, determining the target grayscale contrast of the image region corresponding to any evaluation element in each target image may include:

[0187] For any evaluation element in any target image, the terminal device determines multiple first gray-level contrasts corresponding to multiple evaluation sites in the image region, with one evaluation site corresponding to one first gray-level contrast.

[0188] Based on preset conditions, the terminal device determines the target grayscale contrast from multiple first grayscale contrast ratios.

[0189] For example, please see Figure 7 , Figure 7 This is a schematic diagram illustrating a scene for determining a first grayscale contrast, provided as an embodiment of this application. Figure 7 As shown, let the straight line image 710 be the straight line region corresponding to a certain target image among m*n target images (the corresponding evaluation element is a straight line pair). There are a first evaluation site, a second evaluation site, and a third evaluation site in the straight line image 710. If the first gray-level contrast corresponding to the second evaluation site meets the preset conditions, then the first gray-level contrast corresponding to the second evaluation site can be determined as the target gray-level contrast corresponding to the straight line pair in the target image.

[0190] In some other possible implementations, the image region may include a texture region; for the image region corresponding to any evaluation element in any target image, determining multiple first gray-level contrasts corresponding to multiple evaluation sites in the image region may include:

[0191] For any evaluation element in any target image, the terminal device determines the local contrast and global contrast corresponding to each evaluation point in the image region. The local contrast represents the grayscale contrast of the texture area in the corresponding image region, and the global contrast represents the grayscale contrast between the texture area and the image region in the corresponding image region.

[0192] The terminal device determines the first grayscale contrast corresponding to each evaluation point in the image region based on local contrast and overall contrast.

[0193] Wherein, local contrast is the difference between the average peak value and the average trough value of grayscale in the texture area, and overall contrast is the difference between the average grayscale value of the texture area and the maximum grayscale value of the image area. For example, the formula for calculating the first grayscale contrast can be referred to as follows:

[0194]

[0195] Wherein, Contrast1 is the aforementioned local contrast, Contrast2 is the aforementioned overall contrast, and Ratio is the aforementioned first grayscale contrast.

[0196] For example, combined Figure 7 and Figure 8 ,in, Figure 8 This is a schematic diagram illustrating a scene for calculating grayscale contrast, provided as an embodiment of this application. Figure 8 As shown, for a single image region of a single target image, the terminal device 100 can determine the first gray-level contrast corresponding to each evaluation point by drawing a gray-level polygonal plot corresponding to each evaluation point in the image region. Wherein, Figure 7 The area containing a straight line can be understood as the texture area of ​​that straight line region, combined with... Figure 8 It can be seen that the local contrast of the texture region corresponding to the first evaluation site is higher than that of the second and third evaluation sites. Therefore, it can be considered that the first evaluation site is clearer than the second and third evaluation sites. Furthermore, from... Figure 8As can be seen, the local contrast of the texture area corresponding to the third evaluation site is very low (refer to the polyline portion corresponding to the texture area in the grayscale polyline diagram of the third evaluation site). Therefore, it can be considered that the imaging sharpness of the third evaluation site is low. Optionally, the grayscale polyline diagram corresponding to each evaluation site in the image area can be drawn based on the grayscale values ​​of each pixel in the pixel row where each evaluation site is located. See also Figure 8 In the grayscale polyline graph corresponding to the straight line area, a polyline fluctuation area corresponding to the texture area will appear. The local contrast can be determined based on the grayscale value of the fluctuation area.

[0197] In other possible implementations, determining the target grayscale contrast from a plurality of first grayscale contrasts based on preset conditions may include:

[0198] The grayscale contrast that is greater than a preset threshold and has the smallest difference from the preset threshold among multiple first grayscale contrasts is determined as the target grayscale contrast.

[0199] For example, combined Figure 7 and Figure 8 Assuming the preset threshold is 0.3, from Figure 8 It can be seen that the local contrast corresponding to the first evaluation site is 180-113=67, and the overall contrast is 230-150=80. Therefore, the first grayscale contrast corresponding to the first evaluation site is 0.84. The local contrast corresponding to the second evaluation site is 164-130=31, and the overall contrast is 230-145=85. Therefore, the first grayscale contrast corresponding to the second evaluation site is 0.36. The local contrast corresponding to the third evaluation site is 0, and the overall contrast is 230-160=70. Therefore, the first grayscale contrast corresponding to the third evaluation site is 0. Among them, the first grayscale values ​​that are greater than the preset threshold are 0.84 corresponding to the first evaluation site and 0.36 corresponding to the second evaluation site. Since 0.84 > 0.36 > 0.3, the first grayscale value corresponding to the second evaluation site can be determined as the target grayscale value.

[0200] It should be noted that, for a single image region of a single target image, the terminal device 100 can directly determine the first gray-level contrast of each evaluation point based on the gray-level values ​​corresponding to each evaluation point contained in that image region. Figure 8 The descriptions of the embodiments and related examples are only for the purpose of describing the methods of the embodiments of this application in more detail and accurately, and do not mean that the calculation process of grayscale contrast in this application must include "drawing a grayscale line graph", and should not be construed as limiting this application.

[0201] In other possible implementations, determining the overall sharpness score corresponding to any evaluation element based on the target grayscale contrast corresponding to any evaluation element in each target image may include:

[0202] The terminal device determines the evaluation site corresponding to the target grayscale contrast as the target evaluation site;

[0203] The terminal device determines the first sharpness score corresponding to any evaluation element in any target image based on the target evaluation site and sharpness score mapping table. The sharpness score mapping table is used to record the mapping relationship between the evaluation site and the sharpness score.

[0204] Based on the first sharpness score corresponding to any evaluation element in each target image, determine the comprehensive sharpness score corresponding to any evaluation element.

[0205] For example, combined Figure 8 Based on the relevant embodiments, the second evaluation site can be determined as the target evaluation site. Optionally, if the sharpness score mapping table for line pairs is shown in Table 3, then the first sharpness score of the line pair in the target image corresponding to the line image 710 can be determined to be 20. The line pair readings in Table 3 can be understood as... Figure 4 and / or Figure 6 The numbers shown in the straight line area are located to the left of the line pair elements.

[0206] Table 3

[0207]

[0208] For example, suppose terminal device 100 controls mobile device 110 to capture test image card 140 and obtain 5 target images. If the first sharpness scores of the straight line pair in these 5 target images are 20, 60, 50, 55, and 40 respectively, then the comprehensive sharpness score of the straight line pair can be determined as (20+60+50+55+40) / 5 = 45. It should be noted that the above calculation method of the comprehensive sharpness score of the straight line pair based on the average of the 5 first sharpness scores is only to more vividly illustrate the method of the embodiments of this application. Those skilled in the art can set a suitable method for calculating the comprehensive sharpness score according to the actual situation, and this application does not limit it here.

[0209] For example, combined Figure 4 and Figure 6 For text evaluation elements, the corresponding clarity scoring mapping table is shown in Table 4. The text counts in Table 4 can be understood as... Figure 4 and / or Figure 6 The numerical designation shown in the Chinese text area, located to the right of the text element.

[0210] Table 4

[0211] Assessment site number 1 2 3 4 5 6 Text indication 5 6 7 8 9 10 Score 0 20 40 60 80 100

[0212] For example, combined Figure 4 and Figure 6 For architectural assessment elements, the corresponding clarity scoring mapping table is shown in Table 5. The architectural identifiers in Table 5 can be understood as... Figure 4 and / or Figure 6 The “X” shaped structure shown in the central building area is located within the architectural elements.

[0213] Table 5

[0214]

[0215] Optionally, based on the characteristic that different characters are independent when written or typed, and each character can be regarded as an independent individual, the terminal device 100 can determine the target evaluation point corresponding to the character evaluation element based on the first gray-level contrast and the sharpness percentage. Specifically, the terminal device 100 can calculate the corresponding first gray-level contrast for each fluctuation area, and then determine the sharpness percentage corresponding to the single evaluation point based on multiple first gray-level contrasts corresponding to a single evaluation point and a preset threshold.

[0216] For example, combined Figure 6 and Figure 9 ,in, Figure 9 This is a schematic diagram illustrating a scenario for determining the target grayscale value corresponding to a text area, as provided in an embodiment of this application. Figure 9 As shown, the area containing each character in the text area is a texture area (e.g., Figure 9 The text area contains the first, second, third, and fourth texture regions, and the grayscale line graph corresponding to each evaluation point in the text area will also show multiple line fluctuation regions corresponding to the texture regions (and...). Figure 8The reflection fluctuation area in the mid-grayscale line graph is similar (and will not be elaborated here). Optionally, let the preset threshold be 0.5 and the preset percentage be 60%. If the first grayscale contrast corresponding to the fourth evaluation point is 0.6, 0.58, 0.51, and 0.55 respectively, it can be determined that there are four first grayscale contrasts greater than the preset threshold, accounting for a proportion of 4÷4=1, that is, the sharpness percentage corresponding to the fifth evaluation point is 100% (greater than the preset percentage of 60%); if the first grayscale contrast corresponding to the fifth evaluation point is 0.6, 0.4, 0.4, 0.51, and 0.55 respectively, it can be determined that there are three The proportion of the first grayscale contrast values ​​greater than the preset threshold is 3 ÷ 5 = 0.6, meaning the sharpness percentage corresponding to the fifth evaluation point is 60% (equal to the preset percentage of 60%). If the first grayscale contrast values ​​corresponding to the sixth evaluation point are 0.57, 0.55, 0.4, 0.5, 0.39, 0.4, and 0.43, it can be determined that two of the first grayscale contrast values ​​are greater than the preset threshold, with a proportion of 3 ÷ 7 = 0.43, meaning the sharpness percentage corresponding to the sixth evaluation point is 43% (less than the preset percentage of 60%).

[0217] Optionally, the terminal device 100 can determine the sharpness percentage that is greater than a preset percentage and has the smallest difference from the preset percentage as the target sharpness percentage, and determine the evaluation point corresponding to the target sharpness percentage as the target evaluation point. Among them, the sharpness percentages greater than the preset percentage are 100% of the fourth evaluation point and 60% of the fifth evaluation point. Since 100% > 60% = 60%, 60% can be determined as the target sharpness percentage, and the fifth evaluation point is the target evaluation point.

[0218] Optionally, the method for determining the corresponding target assessment sites using plant assessment elements and / or building assessment elements can refer to the assessment method for determining target assessment sites using textual assessment elements, which will not be elaborated here.

[0219] S304: The terminal device determines the image sharpness score of the mobile device based on the comprehensive sharpness score of each evaluation element.

[0220] Optionally, the terminal device 100 may use an average or weighted average method to determine the imaging sharpness score of the mobile device 110 based on the comprehensive sharpness score of each evaluation element.

[0221] For example, please refer to Table 6, which is a mobile device imaging sharpness rating table provided in an embodiment of this application.

[0222] Table 6

[0223]

[0224]

[0225] As shown in Table 6, different mobile devices exhibit varying imaging capabilities for different evaluation elements. For example, the first mobile device demonstrates strong online imaging capabilities for resolution (which can be understood as a resolution test), but weak imaging capabilities for text and architecture. This can be interpreted as the first mobile device performing well in tests using standard resolution test charts, but exhibiting poor imaging performance for real-world objects such as text and architecture during actual user use. Conversely, the second mobile device shows weaker online imaging capabilities for resolution (which can be understood as a resolution test), but stronger imaging capabilities for text and architecture. This can be interpreted as the first mobile device performing poorly in tests using standard resolution test charts, but exhibiting good imaging performance for real-world objects such as text and architecture during actual user use.

[0226] For example, please see Figure 10 , Figure 10 This is a comparative schematic diagram illustrating the differences in imaging capabilities of different devices, provided as an embodiment of this application. For example... Figure 10 As shown, regarding the imaging effect of line pair evaluation elements, the image captured by the first mobile device is clearer than the image captured by the second mobile device; regarding the imaging effect of text evaluation elements, the image captured by the second mobile device is clearer than the image captured by the first mobile device; and regarding the imaging effect of architectural evaluation elements, the image captured by the second mobile device is clearer than the image captured by the first mobile device. Understandably, to more comprehensively and accurately evaluate the imaging capabilities of mobile devices, the test chart needs to include evaluation elements that are closer to the actual objects photographed by users.

[0227] For example, please see Figure 11 , Figure 11 This is a flowchart illustrating another method for evaluating the imaging sharpness of a mobile device, provided as an embodiment of this application. Figure 10 As shown, the method may include:

[0228] S1101: Set up the shooting location.

[0229] Alternatively, technicians can arrange shooting props (such as test charts, lighting equipment, sliding tracks, mobile devices, and terminal devices) according to... Figure 2 The connection method and placement position shown are used to set up the shooting scene.

[0230] S1102: Move the slider to a preset distance and switch the mobile device to a preset focal length corresponding to the preset distance.

[0231] This can be understood as adjusting the shooting distance between the mobile device and the test chart, and the focal length of the mobile device's lens. For details, please refer to... Figure 3 And its related embodiments.

[0232] S1103: Adjust the light source to different preset color temperatures and light intensities, and then take an image.

[0233] This can be understood as adjusting the lighting parameters of the lighting equipment to preset lighting parameters. For details, please refer to... Figure 3 And its related embodiments.

[0234] S1104: Determine whether the shooting was completed under all preset distances and their corresponding preset focal lengths, preset color temperatures, and light intensity conditions.

[0235] If the determination is yes, then proceed to step S1105; if the determination is no, then proceed to step S1102.

[0236] For example, if there are m preset distances and n color temperatures and light intensities, then step S1102 needs to be executed m times and step S1103 needs to be executed m*n times, and corresponding m*n images need to be captured. Furthermore, after the m*n images have been captured, it can be considered that the shooting has been completed under all preset distances and their corresponding preset focal lengths, preset color temperatures, and light intensities. In this case, the judgment result of step S1104 can be affirmative.

[0237] S1105: Calculate the sharpness score for each element in a single image.

[0238] Optionally, for details, please refer to Figure 3 The method for calculating the first sharpness score in its related embodiments.

[0239] S1106: Calculate the sharpness score of a single image.

[0240] For example, if a single image (which can also be understood as the target image mentioned above) contains 7 image regions, and the sharpness scores corresponding to each image region are 40, 40, 50, 45, 30, 50, 60 and 45 respectively, then based on the averaging algorithm, the sharpness score corresponding to a single image can be determined as (40+40+50+45+30+50+60)÷7=45.

[0241] S1107: Statistical analysis of the overall clarity score of mobile devices.

[0242] For example, if a mobile device captures 6 images from a test image card, and the sharpness scores of these 6 images are 45, 50, 33, 61, 60 and 51 respectively, then based on the averaging algorithm, the sharpness score of the mobile device can be determined to be (45+50+33+61+60+51)÷6=50.

[0243] It should be noted that the above calculation method for the sharpness score corresponding to a single image and the sharpness score corresponding to a mobile device (i.e., the above method of taking the average value) is only for the purpose of explaining the method of the embodiments of this application in a more popular way. It does not mean that the sharpness score can only be obtained based on the average algorithm. The specific calculation method can be set by the technicians based on the actual situation, and this application does not impose any restrictions here.

[0244] As can be seen, the method of this application embodiment can make the test scene or test object for the imaging capability of the mobile device closer to the user's actual shooting scene or shooting object by adding real scene elements such as text, plants or buildings to the test image card. This allows for a more objective and accurate evaluation of the actual shooting effect of the mobile device and also helps to improve the user's shooting experience. The method of this application embodiment can also determine multiple first gray-level contrasts in the corresponding image area in any target image, and determine the target gray value of the evaluation element corresponding to the image area in the target image from the multiple first gray-level contrasts. This helps to obtain the comprehensive sharpness score of the evaluation element, thereby helping testers to more objectively and accurately evaluate the imaging capability of the mobile device for various evaluation elements. Specifically, the method of this application embodiment can determine the first gray-level contrast corresponding to each evaluation point based on local contrast and overall contrast. This helps to obtain parameters that are more suitable for describing the effect of the image area (i.e., the aforementioned first gray-level contrast), thereby enabling a more accurate evaluation of the imaging capability of the mobile device.

[0245] Please see Figure 12 , Figure 12 This is a schematic diagram illustrating the composition of a terminal device provided in an embodiment of this application. For example... Figure 9 As shown, the terminal device 100 may include: a control module 910 and a processing module 920;

[0246] The control module 910 can be used to control the mobile device to capture test image cards to obtain multiple target images. The test image cards can include multiple evaluation areas, and different evaluation areas correspond to different evaluation elements. The evaluation elements can include real-world elements.

[0247] Processing module 920 can be used to determine multiple image regions in each of multiple target images that correspond to multiple evaluation regions, wherein one image region corresponds to one evaluation region and one image region contains one evaluation element;

[0248] The processing module 920 can also be used to determine the overall sharpness score of each evaluation element based on the grayscale contrast of multiple image regions in each target image.

[0249] The processing module 920 can also be used to determine the imaging sharpness score of a mobile device based on the comprehensive sharpness score of each evaluation element.

[0250] In some possible implementations, the terminal device may further include:

[0251] The control module 910 can also be used to control the mobile device to capture test charts to obtain multiple target images under multiple different shooting conditions. The shooting conditions may include shooting distance and / or lighting parameters.

[0252] In some other possible implementations, there is a connection between the terminal device and the lighting device;

[0253] The terminal device may also include:

[0254] The control module 910 can also be used to adjust the shooting distance between the mobile device and the test chart multiple times;

[0255] The control module 910 can also be used to adjust the lens focal length of the mobile device and the lighting parameters of the lighting device after each adjustment of the shooting distance;

[0256] The control module 910 can also be used to control a mobile device to capture test image cards to obtain multiple target images.

[0257] In some other possible implementations, the terminal device may further include:

[0258] The control module 910 can also be used to adjust the lighting parameters multiple times after each adjustment of the shooting distance;

[0259] The control module 910 can also be used to control the mobile device to capture a test chart to obtain a target image after each adjustment of the lighting parameters.

[0260] In some other possible implementations, the lighting parameters may include color temperature and / or light intensity.

[0261] In some other possible implementations, an image region corresponds to one or more grayscale contrasts;

[0262] The terminal device may also include:

[0263] The processing module 920 can also be used to determine the target gray-level contrast of any evaluation element in each target image based on one or more gray-level contrasts corresponding to each image region in each target image for any evaluation element among multiple evaluation elements.

[0264] The processing module 920 can also be used to determine the overall sharpness score corresponding to any evaluation element based on the target grayscale contrast corresponding to any evaluation element in each target image.

[0265] In some other possible implementations, the image region contains multiple evaluation sites;

[0266] The terminal device may also include:

[0267] The processing module 920 can also be used to determine multiple first gray-level contrasts corresponding to multiple evaluation sites in the image region for any evaluation element in any target image, with one evaluation site corresponding to one first gray-level contrast.

[0268] The processing module 920 can also be used to determine a target grayscale contrast from multiple first grayscale contrasts based on preset conditions.

[0269] In some other possible implementations, the terminal device may further include:

[0270] The processing module 920 can also be used to determine the grayscale contrast that is greater than a preset threshold and has the smallest difference from the preset threshold among multiple first grayscale contrasts as the target grayscale contrast.

[0271] In some other possible implementations, the image region may include a texture region;

[0272] The terminal device may also include:

[0273] The processing module 920 can also be used to determine the local contrast and overall contrast of each evaluation point in the image region corresponding to any evaluation element in any target image. The local contrast represents the grayscale contrast of the texture area in the corresponding image region, and the overall contrast represents the grayscale contrast between the texture area and the image region in the corresponding image region.

[0274] The processing module 920 can also be used to determine the first grayscale contrast corresponding to each evaluation point in the image region based on local contrast and overall contrast.

[0275] In some other possible implementations, the local contrast is the difference between the mean of the peak gray values ​​of the texture region and the mean of the trough gray values ​​of the texture region.

[0276] Overall contrast is the difference between the average gray value of the texture area and the maximum gray value of the image area.

[0277] In some other possible implementations, the terminal device may further include:

[0278] The processing module 920 can also be used to determine the evaluation site corresponding to the target grayscale contrast as the target evaluation site;

[0279] The processing module 920 can also be used to determine the first sharpness score corresponding to any evaluation element in any target image based on the target evaluation site and the sharpness score mapping table. The sharpness score mapping table is used to record the mapping relationship between the evaluation site and the sharpness score.

[0280] The processing module 920 can also be used to determine the comprehensive sharpness score corresponding to any evaluation element based on the first sharpness score corresponding to any evaluation element in each target image.

[0281] In other possible implementations, the real-world elements may include at least one of plants, text, and architecture.

[0282] In other possible implementations, the real-world elements may take the form of images and / or 3D models.

[0283] Further, please see Figure 13 , Figure 13 This is a schematic diagram of the hardware structure of a terminal device provided in an embodiment of this application.

[0284] Terminal device 100 may include a processor 101, a memory 102, a wireless communication module 103, a mobile communication module 104, an antenna 103A, an antenna 104A, a power switch 105, a port 106, a display screen 107, etc. Port 106 may include a virtual port 106A and a physical port 106B, etc. The wireless communication module 103 may include a WLAN communication module, a Bluetooth communication module, etc. All of the above components can transmit data via a bus.

[0285] Processor 101 may include one or more processing units, such as application processors (APs), modem processors, graphics processing units (GPUs), image signal processors (ISPs), controllers, video codecs, digital signal processors (DSPs), baseband processors, and / or neural network processing units (NPUs). These different processing units may be independent devices or integrated into one or more processors.

[0286] Memory 102 can be used to store computer executable program code, which may include instructions. Processor 101 executes various functional applications and data processing of terminal device 100 by running the instructions stored in memory 102. Memory 102 may include a program storage area and a data storage area. In specific implementations, memory 102 may include high-speed random access memory, and may also include non-volatile memory, such as one or more disk storage devices, flash memory devices, or other non-volatile solid-state storage devices.

[0287] The wireless communication function of the terminal device 100 can be implemented through antenna 103A, antenna 104A, mobile communication module 104, wireless communication module 103, modem processor, and baseband processor.

[0288] Antennas 103A and 104A can be used to transmit and receive electromagnetic wave signals. Each antenna in terminal device 100 can be used to cover one or more communication frequency bands. Different antennas can also be reused to improve antenna utilization.

[0289] The mobile communication module 104 can provide wireless communication solutions, including 2G / 3G / 4G / 5G, for use on the terminal device 100.

[0290] The wireless communication module 103 can provide solutions for wireless communication applications on the terminal device 100, including Wireless Local Area Networks (WLAN), Bluetooth (BT), Global Navigation Satellite System (GNSS), Frequency Modulation (FM), Near Field Communication (NFC), Infrared (IR), and other wireless communication technologies.

[0291] Virtual port 106A can refer to a port inside terminal device 100 or within a switch or router, and is not visible.

[0292] Physical port 106B, also known as an interface, is a visible port, such as the network port on the backplane of terminal device 100, the port of a switch or router, or the data port, control port, and status port on a host.

[0293] The terminal device 100 can perform image processing functions through an ISP, video codec, GPU, display screen 107, and application processor.

[0294] The terminal device 100 can implement display functions through a GPU, a display screen 107, and an application processor. The GPU is a microprocessor for image processing, connected to the display screen 107 and the application processor. The GPU is used to perform mathematical and geometric calculations and for graphics rendering. The processor 101 may include one or more GPUs, which execute program instructions to generate or modify display information.

[0295] The display screen 107 is used to display images, videos, etc. The display screen 107 includes a display panel. In some embodiments, the terminal device 100 may include one or N display screens 107, where N is a positive integer greater than 1.

[0296] The structures illustrated in the embodiments of this application do not constitute a specific limitation on the terminal device 100. In other embodiments of this application, the terminal device 100 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0297] In the embodiments of this application:

[0298] Terminal device 100 can use any one or more of wireless communication module 103, mobile communication module 104 and port 106 to control mobile device 110 to capture test image card 140 to obtain multiple target images.

[0299] The display screen 107 is used to display the target images acquired by the aforementioned mobile device 110 and the corresponding sharpness scores for these target images. The user interface displayed on the display screen 107 can be found in the UI embodiment described above.

[0300] For details on the operations performed by each device in the terminal device 100, please refer to the relevant descriptions in the preceding method embodiments.

[0301] The software system of terminal device 100 can adopt a layered architecture, event-driven architecture, microkernel architecture, microservice architecture, or cloud architecture. This application embodiment uses a layered mobile operating system as an example to exemplify the software structure of terminal device 100.

[0302] Further, please refer to Figure 14 , Figure 14 This is a software structure block diagram provided in an embodiment of this application. For example... Figure 14 The layered architecture shown divides the software into several layers, each with a clear role and division of labor, and the layers can communicate with each other through software interfaces.

[0303] In some embodiments, the system of the terminal device 100 can be divided into five layers, from top to bottom: the application layer, the application framework layer, the system runtime library layer, the hardware abstraction layer (HAL), and the kernel layer. The descriptions of each of these layers are as follows:

[0304] The application layer may include a series of application packages. For example, the application packages in the application layer may include applications such as gallery, calendar, video, and music.

[0305] The application framework layer provides application programming interfaces (APIs) and programming frameworks for applications within the application layer. The application framework layer can include some predefined functions.

[0306] For example, the application framework layer may include an activity manager, a window manager, a content provider, a view system, a resource manager, a notification manager, and an accelerated graphics port (AGP), etc. Among them:

[0307] The Activity Manager can be used to manage the lifecycle of individual applications and the usual navigation back functionality.

[0308] A window manager can be used to manage window programs. For example, a window manager can obtain the screen size of terminal device 100, lock the screen, capture the screen, and determine whether there is a status bar, etc.

[0309] Content providers can be used to store and retrieve data, and make that data accessible to applications, enabling different applications to access or share data. For example, the data mentioned above may include videos, images, and audio, etc.

[0310] A view system includes visual controls, such as controls for displaying text and controls for displaying images. View systems can be used to build applications. A display interface can consist of one or more views. For example, an interface presenting the target image analysis process, or an interface presenting the results of a mobile device's 110 imaging capability assessment.

[0311] The file explorer provides applications with various resources, such as localized strings, icons, images, layout files, video files, etc.

[0312] The notification manager allows applications to display notifications in the status bar. These notifications can be used to convey informational messages and can disappear automatically after a short pause, requiring no user interaction. For example, the notification manager can be used to notify users of download completion, target image evaluation completion, mobile device 110 imaging capability evaluation completion, and message alerts. The notification manager can also appear as a notification in the system's top status bar as an icon or scrolling text, such as notifications from background applications, or as a notification in a dialog box on the screen. Examples include displaying text messages in the status bar, emitting alert sounds, vibrating the device, and flashing indicator lights.

[0313] AGP in the application framework layer can be used to improve the rendering performance of the graphics card, such as by providing the graphics card with more cache capacity to achieve faster image processing speed.

[0314] The system runtime layer can include system libraries and the Android runtime. Specifically:

[0315] The Android runtime consists of the core libraries and the virtual machine. The Android runtime is responsible for scheduling and managing the Android system. The core libraries comprise two parts: one part contains the functionalities that Java calls, and the other part contains the core Android libraries. The application layer and application framework layer run in the virtual machine. The virtual machine executes the Java files of the application layer and application framework layer as binary files. The virtual machine is used to perform functions such as object lifecycle management, stack management, thread management, security and exception management, and garbage collection.

[0316] System libraries can be understood as the support for the application framework, serving as a crucial link between the application framework layer and the kernel layer. The system layer can include multiple functional modules, such as a surface manager, media libraries, 3D graphics processing libraries (e.g., OpenGL ES), and 2D graphics engines (e.g., SGL). Among these:

[0317] The surface manager can be used to manage the display subsystem, such as when multiple applications are running on terminal device 100, it manages the interaction between display and access operations. The surface manager can also be used to blend two-dimensional and three-dimensional layers for multiple applications.

[0318] The media library supports playback and recording of various common audio and video formats, as well as still image files. It supports multiple audio and video encoding formats, such as MPEG4, H.264, MP3, AAC, AMR, JPG, and PNG.

[0319] The 3D graphics processing library is used to implement 3D graphics drawing, image rendering, compositing, and layer processing.

[0320] A 2D graphics engine can be understood as a graphics engine for 2D drawing.

[0321] The Hardware Abstraction Layer provides standard interfaces, such as the HAL (Hardware Interface Definition Language) interface or the Android Interface Definition Language (AIDL) interface.

[0322] The kernel layer can be understood as an abstraction layer between hardware and software. The kernel layer may include system services such as security, memory management, process management, power management, network protocol management, and driver management. The kernel layer may include drivers; for example, drivers may include display drivers and audio drivers. Optionally, the kernel layer may also be called the Android kernel or kernel program. For example, the hardware layer of the terminal device 100 may include a touch panel (TP), a liquid crystal display (LCD), etc.

[0323] In some embodiments, the kernel layer can be understood as kernel mode, and the other four layers (i.e., application layer, application framework layer, system runtime library layer, and HAL) can be understood as user mode.

[0324] It is understandable that various applications within the application layer can have video recording and / or playback capabilities. For example, a camera may have video recording capabilities, while applications such as photo galleries, navigation apps, and browsers may have video playback capabilities. Furthermore, the duration, resolution, and shooting parameters of the video will all affect the recording and / or playback of the video. These shooting parameters may include aperture value, shutter speed, ISO, exposure, focal length, and depth of field.

[0325] For example, the relevant parameters for video recording and / or playback supported by various applications can be stored in a sensor binary file (sensor bin), which can be stored in external storage, such as a disk or hard drive. When a user uses a specific application to record and / or play video, the application can read the relevant parameters for video recording and / or playback into memory so that the user can use the application normally and ensure a good user experience.

[0326] It should be understood that each step in the above method embodiments can be completed by integrated logic circuits in the processor hardware or by instructions in software form. The method steps disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or being executed by a combination of hardware and software modules in the processor.

[0327] This application also provides a terminal device, which may include a memory and a processor. The memory may be used to store computer programs; the processor may be used to invoke the computer programs in the memory, so that the terminal device executes the methods executed on the terminal device side in any of the above embodiments.

[0328] This application also provides a chip system, which includes at least one processor for implementing the functions involved on the terminal device side in any of the above embodiments.

[0329] In some possible designs, the chip system also includes a memory for storing program instructions and data, which may be located inside or outside the processor.

[0330] The chip system can consist of chips or include chips and other discrete components.

[0331] Optionally, the chip system may contain one or more processors. These processors can be implemented in hardware or software. When implemented in hardware, the processor can be a logic circuit, an integrated circuit, etc. When implemented in software, the processor can be a general-purpose processor, implemented by reading software code stored in memory.

[0332] Optionally, the chip system may contain one or more memories. The memory may be integrated with the processor or disposed separately from it; this application embodiment does not limit this. For example, the memory may be a non-transient processor, such as a read-only memory (ROM), which may be integrated with the processor on the same chip or disposed separately on different chips. This application embodiment does not specifically limit the type of memory or the arrangement of the memory and processor.

[0333] For example, the chip system may be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on chip (SoC), a central processor unit (CPU), a network processor (NP), a digital signal processor (DSP), a micro controller unit (MCU), a programmable logic device (PLD), or other integrated chips.

[0334] This application also provides a computer program product, which includes a computer program (also referred to as code or instructions) that, when run, causes a computer to perform the method executed on the terminal device side in any of the above embodiments.

[0335] This application also provides a computer-readable storage medium storing a computer program (also referred to as code or instructions). When the computer program is run, it causes the computer to perform the method executed on the terminal device side in any of the above embodiments.

[0336] As used in the above embodiments, depending on the context, the term "when..." can be interpreted as meaning "if...", "after...", "in response to determining...", or "in response to detecting...". Similarly, depending on the context, the phrase "when determining..." or "if (the stated condition or event) is interpreted as meaning "if determining...", "in response to determining...", "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0337] The various embodiments of this application can be combined arbitrarily to achieve different technical effects.

[0338] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0339] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0340] In summary, the above description is merely an embodiment of the technical solution of this application and is not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made based on the disclosure of this application should be included within the scope of protection of this application.

Claims

1. A method for evaluating the image sharpness of a mobile device, characterized in that, Applied to a terminal device, wherein the terminal device is connected to a mobile device, the method includes: The mobile device is controlled to capture test image cards to obtain multiple target images. The test image cards include multiple evaluation areas, and different evaluation areas correspond to different evaluation elements. The evaluation elements include real-world elements. In each of the plurality of target images, a plurality of image regions corresponding to the plurality of evaluation regions are determined, wherein one image region corresponds to one evaluation region and one image region contains one evaluation element; Based on the grayscale contrast of the multiple image regions in each target image, a comprehensive sharpness score for each evaluation element is determined; The imaging sharpness score of the mobile device is determined based on the comprehensive sharpness score of each evaluation element.

2. The method according to claim 1, characterized in that, The mobile device is controlled to capture test image cards to obtain multiple target images, including: Under multiple different shooting conditions, the mobile device is controlled to capture images of the test chart to obtain multiple target images. The shooting conditions include shooting distance and / or lighting parameters.

3. The method according to claim 2, characterized in that, There is a connection between the terminal device and the lighting device; The step of controlling the mobile device to capture images of the test chart under multiple different shooting conditions to obtain the multiple target images includes: The shooting distance between the mobile device and the test chart was adjusted multiple times; After each adjustment of the shooting distance, the lens focal length of the mobile device and the lighting parameters of the lighting device are adjusted. The mobile device is controlled to capture images of the test chart to obtain the multiple target images.

4. The method according to claim 3, characterized in that, The method further includes: After each adjustment of the shooting distance, the lighting parameters are adjusted multiple times; After each adjustment of the lighting parameters, the mobile device is controlled to capture a test image of the test chart to obtain a target image.

5. The method according to any one of claims 2-4, characterized in that, The lighting parameters include color temperature and / or light intensity.

6. The method according to any one of claims 1-5, characterized in that, One image region corresponds to one or more grayscale contrasts; The determination of the comprehensive sharpness score for each evaluation element based on the grayscale contrast of the multiple image regions in each target image includes: For any one of the multiple evaluation elements, the target gray-level contrast of the evaluation element in each target image is determined based on one or more gray-level contrasts corresponding to each image region in each target image. Based on the target grayscale contrast corresponding to any one of the evaluation elements in each target image, a comprehensive sharpness score is determined for each evaluation element.

7. The method according to claim 6, characterized in that, The image region contains multiple evaluation sites; Determining the target grayscale contrast of the image region corresponding to any evaluation element in each target image includes: For any evaluation element in any target image, determine multiple first gray-level contrasts corresponding to multiple evaluation sites in the image region, with one evaluation site corresponding to one first gray-level contrast. The target grayscale contrast is determined from the plurality of first grayscale contrasts based on preset conditions.

8. The method according to claim 7, characterized in that, Determining the target grayscale contrast from the plurality of first grayscale contrasts based on preset conditions includes: The grayscale contrast that is greater than a preset threshold and has the smallest difference from the preset threshold among the plurality of first grayscale contrasts is determined as the target grayscale contrast.

9. The method according to claim 7 or 8, characterized in that, The image region includes a texture region; The step of determining multiple first grayscale contrasts corresponding to multiple evaluation sites in the image region for any evaluation element in any target image includes: For any evaluation element in any target image, determine the local contrast and global contrast corresponding to each evaluation point in the image region. The local contrast represents the grayscale contrast of the texture area in the corresponding image region, and the global contrast represents the grayscale contrast between the texture area and the image region in the corresponding image region. Based on the local contrast and the overall contrast, the first grayscale contrast corresponding to each evaluation point in the image region is determined.

10. The method according to claim 9, characterized in that, The local contrast is the difference between the average peak value of the grayscale value in the texture region and the average trough value of the grayscale value in the texture region. The overall contrast is the difference between the average gray value of the texture area and the maximum gray value of the image area.

11. The method according to any one of claims 7-10, characterized in that, The step of determining the comprehensive sharpness score corresponding to any evaluation element based on the target grayscale contrast corresponding to any evaluation element in each target image includes: The evaluation site corresponding to the target grayscale contrast is determined as the target evaluation site; Based on the target evaluation site and sharpness score mapping table, the first sharpness score corresponding to any evaluation element in any target image is determined. The sharpness score mapping table is used to record the mapping relationship between the evaluation site and the sharpness score. Based on the first sharpness score corresponding to any one of the evaluation elements in each target image, a comprehensive sharpness score corresponding to any one of the evaluation elements is determined.

12. The method according to any one of claims 1-11, characterized in that, The real-world elements include at least one of plants, text, and architecture.

13. The method according to claim 12, characterized in that, The real-world elements may take the form of images and / or 3D models.

14. A terminal device, characterized in that, The terminal device is connected to the mobile device, and the terminal device includes a control module and a processing module. The control model is used to control the mobile device to capture test image cards to obtain multiple target images. The test image cards include multiple evaluation areas, and different evaluation areas correspond to different evaluation elements. The evaluation elements include real-scene elements. The processing module is used to determine multiple image regions in each of the multiple target images that correspond to the multiple evaluation regions, wherein one image region corresponds to one evaluation region and one image region contains one evaluation element; The processing module is also used to determine the comprehensive sharpness score of each evaluation element based on the grayscale contrast of the plurality of image regions in each target image; The processing module is also used to determine the imaging sharpness score of the mobile device based on the comprehensive sharpness score of each evaluation element.

15. A terminal device, characterized in that, The device includes a processor and a memory, the memory being used to store a computer program, the computer program including program instructions, and the processor being configured to invoke the program instructions such that the method as described in any one of claims 1-13 is executed.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, the computer program including program instructions that, when executed by a processor, cause the method as described in any one of claims 1-13 to be performed.

Images