An image display method and apparatus

Abstract

The application provides an image display method and device, and relates to the technical field of image processing. The method comprises the following steps: a first device can display a first interface of a target application, and the first interface comprises at least one image set, and the image structure similarity between images in a target image set in the at least one image set meets a preset similarity condition. The first device can also display a second interface of the target application in response to a triggering operation on the target image set, and the second interface comprises multiple images in the target image set. By classifying images according to image structures, the classification accuracy of images is improved.

Description

Technical Field

[0001] This application relates to the field of image processing technology, and in particular to an image display method and device. Background Technology

[0002] With the widespread use of smart devices, users frequently take photos in their daily lives, resulting in a massive amount of user photos stored on these devices. Therefore, how to categorize user photos and quickly and easily find photos that meet specific criteria from this vast collection has become a pressing issue.

[0003] Typically, to meet users' categorization needs, photo categorization tools on smart devices can classify large numbers of photos by time, people, or events, allowing users to quickly filter and delete duplicate or similar photos. However, these categorization methods are too crude. For example, categorizing by time or people will group photos from different users together, or categorizing by events will group photos from different scenes together, making it inconvenient and time-consuming for users to find photos that meet specific criteria. This, in turn, reduces the user experience. Summary of the Invention

[0004] This application provides an image display method and device that classifies images according to their structure, with sufficiently fine classification dimensions, thereby improving the accuracy of image classification.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] In a first aspect, this application provides an image display method applied to a first device, the method comprising:

[0007] The first device can display a first interface of the target application, which includes at least one image set, wherein the image structure similarity between images in the target image set satisfies a preset similarity condition. The first device can also, in response to a trigger operation on the target image set, display a second interface of the target application, which includes multiple images from the target image set.

[0008] The first device in this application embodiment is capable of classifying multiple images to be sorted in a target application and displaying at least one set of classified images.

[0009] In this context, the image structure similarity between images in an image set meets a preset similarity condition. That is, the image structures of images within the same image set are highly similar, meaning that the images in the same set are either highly similar scenes or highly similar people.

[0010] Thus, this embodiment of the application classifies images according to their structure, with sufficiently fine dimensions to completely categorize people and scenes without merging images with different structures. This improves the accuracy of image classification. Simultaneously, it fully displays the image details that users care about, enhancing the efficiency of image searching and the overall user experience.

[0011] In one possible implementation, the method further includes:

[0012] The first device can display a third interface of the target application, the third interface including the first control, or the first control can be displayed floating on the third interface;

[0013] Display the first interface of the target application, including:

[0014] The first device can display a first interface in response to a first operation on the first control.

[0015] Thus, the embodiments of this application can provide users with user-friendly operations for image organization, such as one-click organization. The interaction process is convenient, enhancing the user experience.

[0016] In one possible implementation, the first interface includes a second control, or the second control is displayed floating on the first interface;

[0017] The method also includes:

[0018] The first device can switch the first interface to the third interface in response to a second operation on the second control.

[0019] Thus, the embodiments of this application can provide users with user-friendly operations for image organization, such as one-click undoing of organization operations. The interaction process is convenient, enhancing the user experience.

[0020] In one possible implementation, the shooting information parameters between images in the target image set meet preset conditions, and the shooting information parameters include at least one of shooting time, shooting location, and shooting object type.

[0021] Thus, in this embodiment, the shooting information parameters between images in the target image set meet the preset conditions. By further combining the shooting information parameters and image structure for classification, the classification dimensions are sufficiently fine, enabling complete classification of people and scenes without merging images with different structures. This improves the accuracy of image classification. Simultaneously, it fully displays the image details that users care about, improving the user's image search efficiency and overall user experience.

[0022] In one possible implementation, the shooting information parameters meet preset conditions, including the shooting time meeting preset time requirements and / or the shooting location being the same.

[0023] Thus, this embodiment of the application can perform an initial classification of images among multiple images to be sorted that meet preset time requirements and / or have the same shooting location. In this way, images with similar or identical shooting times and / or shooting locations can be grouped into an image set, and after subsequent secondary classification, the images in this image set have a high degree of image structural similarity.

[0024] Thus, this embodiment of the application classifies images by introducing the dimensions of shooting time and location, providing sufficiently fine-grained classification. This improves the accuracy of image classification. Simultaneously, it fully displays the image details that users care about, enhancing the efficiency of image search and improving the user experience.

[0025] In one possible implementation, the method further includes:

[0026] The first device can acquire shooting information parameters corresponding to multiple images to be processed in the target application. The shooting information parameters include shooting time and shooting location.

[0027] The first device can also divide multiple images to be processed into O image sets according to preset conditions and shooting information parameters, where O is an integer greater than 1;

[0028] The first device can also perform image structure similarity analysis on the images in any one of the O image sets, and divide multiple images in the image set whose image structure similarity meets the preset similarity conditions into one image set, thereby obtaining at least one image set in the first interface.

[0029] Thus, this embodiment of the application performs secondary classification based on shooting information parameters and image structure. The classification dimensions are sufficiently fine to completely categorize people and scenes, improving the accuracy of image classification. This, in turn, enhances the user's image search efficiency and overall user experience.

[0030] In one possible implementation, the image with the highest image quality index in the target image set is displayed as the cover image of the target image set in the first interface, where the image quality index is used to characterize image quality.

[0031] Thus, in this embodiment, the image with the highest image quality index in any image set is set as the cover image for that image set and displayed. This allows for the display of high-quality images with clearer and richer colors, enhancing visual appeal. Simultaneously, high-quality images help accurately convey image information, avoiding misunderstandings caused by poor image quality. This makes it easier for users to accurately understand the image content of the corresponding image set, improving the user experience.

[0032] In one possible implementation, multiple images in the target image set in the second interface are displayed in descending order of image quality index, which is used to characterize image quality.

[0033] Thus, this embodiment sorts and displays images in a target image set according to an image quality index. This helps to accurately convey which images in the target image set are of higher quality and which are of lower quality. This makes it easier for users to accurately understand the image content in the image set, improving the user experience.

[0034] In one possible implementation, the second interface further includes a first prompt identifier, which prompts the sharing of a first image. The first image is one of the top M images in the second interface with the highest image quality index, where M is an integer greater than or equal to 1. And / or, the second interface further includes a second prompt identifier, which prompts the deletion of a second image. The second image is one of the bottom N images in the second interface with the lowest image quality index, where N is an integer greater than or equal to 1. The image quality index is used to characterize image quality.

[0035] Thus, this application embodiment, through the above-described method, recommends sharing high-quality images and recommends deleting low-quality images, enabling the sharing of high-quality images while avoiding the occupation of device storage space by low-quality images. This provides users with more user-friendly functionality and enhances the user experience.

[0036] In one possible implementation, the second interface further includes a first display area and / or a second display area. The first display area is used to display a first image to be shared, which is one of the top M images in the second interface with the highest image quality index, where M is an integer greater than or equal to 1. And / or, the second display area is used to display a second image to be deleted, which is one of the bottom N images in the second interface with the lowest image quality index, where N is an integer greater than or equal to 1. The image quality index is used to characterize image quality.

[0037] Thus, this embodiment of the application, through the above-described method, recommends sharing images with higher quality and recommends deleting images with lower quality, enabling the sharing of high-quality images while avoiding the occupation of device storage space by low-quality images. This provides users with more user-friendly functionality and enhances the user experience.

[0038] In one possible implementation, the image quality index is determined based on a first parameter and / or a second parameter. The first parameter includes at least one of resolution, exposure, sharpness, noise, contrast, and color saturation.

[0039] The second parameter includes at least one of the following: face angle parameter, face expression parameter, face occlusion parameter, face skin color parameter, face makeup parameter, and face texture parameter.

[0040] Thus, by employing the aforementioned first and / or second parameters to perform multi-dimensional image quality evaluation, this embodiment of the application can more comprehensively and accurately grasp the key features in the image. This helps users filter out high-quality image content and enhances the user experience.

[0041] In one possible implementation, at least one image set includes a first image set, wherein the images in the first image set include human faces; the method further includes:

[0042] The first device can acquire the first parameter and the second parameter of each image in the first image set, and calculate the image quality index of each image in the first image set based on the first parameter and the second parameter of each image in the first image set.

[0043] Thus, this embodiment of the application employs the aforementioned first and second parameters to perform multi-dimensional image quality evaluation, thereby gaining a more comprehensive and accurate understanding of the key features in the image. For example, for images of people, it focuses more on facial angles, expressions, and whether there is occlusion, while also combining evaluation from multiple dimensions such as image resolution, clarity, and exposure to better reflect the characteristics of the person in the image. This helps users filter out high-quality image content and improves the user experience.

[0044] In one possible implementation, the image quality index of each image in the first image set is calculated based on a first parameter and a second parameter of each image in the first image set, including:

[0045] The first device can use the weights of the first parameter and the second parameter to calculate the image quality index of each image in the first image set, where the weight of any one of the first parameters is greater than the weight of any one of the second parameters.

[0046] Therefore, by assigning different weights to each dimension based on its importance, the resulting image quality index better reflects actual needs and highlights the importance of certain key dimensions in the image. For example, giving a greater weight to any parameter in the first parameter than any parameter in the second parameter emphasizes the quality features in the image. This allows the image quality index to better reflect the true state of the image. Consequently, it helps users filter out high-quality image content and improves the user experience.

[0047] In one possible implementation, the method further includes:

[0048] The first device sends an image quality assessment request to the server, which requests the server to perform an image quality assessment on images in at least one image set.

[0049] The first device receives the sorting results sent by the server. The sorting results include the image name and image quality index corresponding to the images in each image set.

[0050] In one possible implementation, the method further includes:

[0051] The first device sends a classification request to the server. The classification request is used to request the server to classify the preliminary classification results. The preliminary classification results include the name of the first set corresponding to each of the O image sets and the image data in each image set.

[0052] The first device receives the final classification result sent by the server. The final classification result includes the name of the second set corresponding to each of the P image sets and the image name of the image in each image set, where P is an integer greater than 1 and P is greater than or equal to 0.

[0053] Thus, in this embodiment, some processing steps in the first device can be delegated to the server, reducing the processing burden on the first device. Simultaneously, the server possesses powerful computing capabilities, enabling it to handle a large number of complex computational tasks simultaneously, improving image processing efficiency. This, in turn, enhances the user experience.

[0054] Secondly, embodiments of this application also provide an image display method applied to a first device, the method comprising:

[0055] The first device can acquire the first set of images for the target application.

[0056] The first device can also display a target interface, which includes multiple images from a first image set. The order of the multiple images is determined based on a first parameter and a second parameter of each image in the first image set.

[0057] The first parameter includes at least one of resolution, exposure, sharpness, noise, contrast, and color saturation. The second parameter includes at least one of face angle parameter, face expression parameter, face occlusion parameter, face skin color parameter, face makeup parameter, and face texture parameter.

[0058] Thus, in this embodiment, the first device performs multi-dimensional image quality assessment and sorting using the aforementioned first and second parameters, and displays the images after determining their corresponding arrangement order. A more comprehensive and accurate grasp of the key features in the images helps users filter out high-quality image content. Furthermore, when users view images, they can see high-quality images first and obtain the clearest and most accurate information immediately, thus enhancing the user experience.

[0059] In some embodiments of this application, the images in the first image set include human faces, and the method further includes:

[0060] The first device can calculate the image quality index for each image in the first image set based on the first parameter and the second parameter of each image. The image quality index is used to characterize the image quality. The first device can also sort multiple images in the first image set based on the image quality index.

[0061] Thus, this embodiment of the application employs the aforementioned first and second parameters to perform multi-dimensional image quality evaluation, thereby gaining a more comprehensive and accurate understanding of the key features in the image. For example, for images of people, it focuses more on facial angles, expressions, and whether there is occlusion, while also combining evaluation from multiple dimensions such as image resolution, clarity, and exposure to better reflect the characteristics of the person in the image. This helps users filter out high-quality image content and improves the user experience.

[0062] In one possible implementation, during the process of the mobile phone calculating the image quality index of each image in the first image set based on the first parameter and the second parameter of each image in the first image set, the first device can use the weight of the first parameter and the weight of the second parameter to calculate the first parameter and the second parameter to obtain the image quality index of each image in the first image set, wherein the weight of any one of the first parameters is greater than the weight of any one of the second parameters.

[0063] Therefore, by assigning different weights to each dimension based on its importance, the embodiments of this application can make the resulting image quality index more in line with actual needs and highlight the importance of certain key dimensions in the image. For example, setting a higher weight for each parameter in the first parameter can highlight the quality features in the image. This allows the image quality index to better reflect the true situation corresponding to the image. Consequently, it helps users filter out high-quality image content and improves the user experience.

[0064] Thirdly, embodiments of this application also provide an image display method applied to a communication system, the communication system including a first device and a server, the method including:

[0065] The first device sends a classification request to the server. The classification request is used to request the server to classify the preliminary classification results. The classification request includes the preliminary classification results, which include the name of the first set corresponding to each of the O image sets and the image data in each image set, where O is an integer greater than 1.

[0066] The server receives a classification request and, in response, performs image structure similarity analysis on the preliminary classification results to obtain the final classification result. The final classification result includes the name of the second set corresponding to each of the P image sets and the image name of the image in each image set, where P is an integer greater than 1 and greater than or equal to 0.

[0067] The server sends the final classification result to the first device.

[0068] The first device can display the first interface of the target application based on the final classification result. The first interface includes at least one image set, and the image structure similarity between the images in the target image set in the at least one image set satisfies a preset similarity condition.

[0069] The first device can also respond to a trigger operation on the target image set by displaying a second interface of the target application, the second interface including multiple images in the target image set.

[0070] Thus, in this embodiment, the first device can perform initial classification, while secondary classification is handled by the server. This reduces the processing burden on the first device. Simultaneously, the server possesses powerful computing capabilities, enabling it to handle a large number of complex computational tasks simultaneously, improving image processing efficiency. This, in turn, enhances the user experience.

[0071] Fourthly, embodiments of this application also provide an image display method applied to a communication system, the communication system including a first device and a server, the method including:

[0072] The first device sends an image quality assessment request to the server, which requests the server to perform an image quality assessment on images in at least one image set.

[0073] The server receives an image quality assessment request, calculates the image quality index of each image in at least one image set, and sorts the images in at least one image set from high to low according to the image quality index, thus obtaining the sorting result.

[0074] The server sends the sorting results to the first device.

[0075] The first device can display the first interface of the target application based on the sorting results. The first interface includes at least one image set, and the image structure similarity between the images in the target image set in the at least one image set satisfies a preset similarity condition.

[0076] The first device can also respond to a trigger operation on the target image set by displaying a second interface of the target application, the second interface including multiple images in the target image set.

[0077] Thus, in this embodiment, the first device can perform initial and secondary classification, while the image quality assessment process is handled by the server. This reduces the processing burden on the first device. Simultaneously, the server possesses powerful computing capabilities, enabling it to handle numerous complex computational tasks simultaneously, thereby improving image processing efficiency. Ultimately, this enhances the user experience.

[0078] Fifthly, embodiments of this application also provide a communication system, which includes a first device and a server; the first device and the server establish a communication connection;

[0079] The first device is configured to send a classification request to the server. The classification request is used to request the server to classify the preliminary classification results. The classification request includes the preliminary classification results, which include the name of the first set corresponding to each of the O image sets and the image data in each image set, where O is an integer greater than 1.

[0080] The server is configured to receive classification requests and, in response to the classification requests, perform image structure similarity analysis on the preliminary classification results to obtain the final classification results. The final classification results include the name of the second set corresponding to each of the P image sets and the image name of the image in each image set, where P is an integer greater than 1 and greater than or equal to 0.

[0081] The server is also configured to send the final classification results to the first device.

[0082] The first device is also configured to display a first interface of the target application based on the final classification result. The first interface includes at least one image set, and the image structure similarity between the images in the target image set in the at least one image set satisfies a preset similarity condition.

[0083] The first device is also configured to display a second interface of the target application in response to a trigger operation on the target image set, the second interface including multiple images from the target image set.

[0084] Thus, in the communication system provided in this application embodiment, the first device can perform initial classification, while secondary classification is handled by the server. This reduces the processing burden on the first device. Simultaneously, by leveraging the server's powerful computing capabilities, the first device can quickly process complex tasks, improving image processing efficiency.

[0085] Sixthly, embodiments of this application also provide a communication system, which includes a first device and a server; the first device and the server establish a communication connection;

[0086] The first device is configured to send an image quality assessment request to the server, the image quality assessment request being used to request the server to perform an image quality assessment on images in at least one image set.

[0087] The server is configured to receive image quality assessment requests, calculate the image quality index of each image in at least one image set, and sort the images in at least one image set from high to low according to the image quality index to obtain the sorting result.

[0088] The server is configured to send the sorting results to the first device.

[0089] The first device is also configured to display a first interface of the target application based on the sorting result. The first interface includes at least one image set, and the image structure similarity between the images in the target image set in the at least one image set satisfies a preset similarity condition.

[0090] The first device is also configured to display a second interface of the target application in response to a trigger operation on the target image set, the second interface including multiple images from the target image set.

[0091] Thus, in the communication system provided in this embodiment, the first device can perform initial and secondary classification, while the image quality assessment process is handled by the server. Delegating some tasks to the server makes the first device lighter and more energy-efficient. Simultaneously, the server possesses powerful computing capabilities, enabling it to quickly process complex tasks, thereby improving overall processing efficiency.

[0092] In a seventh aspect, embodiments of this application also provide an electronic device, which includes a display screen, a memory, and one or more processors; the display screen is used to display a user interface of a target application, and the memory is coupled to the processor; wherein the memory stores computer program code, which includes computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the image display method as described in the first aspect above, or performs the image display method as described in the second aspect above.

[0093] Eighthly, embodiments of this application also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform the image display method as described in the first aspect above, or to perform the image display method as described in the second aspect above.

[0094] Ninthly, embodiments of this application also provide a computer program product comprising instructions that, when executed by an electronic device, cause the electronic device to perform the image display method as described in the first aspect above, or to perform the image display method as described in the second aspect above. Attached Figure Description

[0095] Figure 1 This is a schematic diagram of the structure of a mobile phone provided in an embodiment of this application;

[0096] Figure 2 A software structure block diagram of a mobile phone provided in an embodiment of this application;

[0097] Figure 3 A flowchart illustrating an image display method provided in an embodiment of this application;

[0098] Figure 4 A schematic diagram of an application scenario provided in this application embodiment;

[0099] Figure 5 A schematic diagram of a third interface provided in an embodiment of this application;

[0100] Figure 6 A schematic diagram of a first interface provided in an embodiment of this application;

[0101] Figure 7 A schematic diagram of a first classification scenario provided for an embodiment of this application;

[0102] Figure 8 A schematic diagram of a second classification scenario provided for an embodiment of this application;

[0103] Figure 9 A schematic diagram of a process for calculating an image quality index provided in an embodiment of this application;

[0104] Figure 10 This is a schematic diagram of a prompting information interface provided in an embodiment of this application;

[0105] Figure 11 A schematic diagram of a second interface provided in an embodiment of this application;

[0106] Figure 12 A schematic diagram of a prompting icon provided in an embodiment of this application. Figure 1 ;

[0107] Figure 13 A schematic diagram of a prompting icon provided in an embodiment of this application. Figure 2 ;

[0108] Figure 14 An interactive illustration of a classification system provided in this application embodiment. Figure 1 ;

[0109] Figure 15 An interactive illustration of a classification system provided in this application embodiment. Figure 2 ;

[0110] Figure 16 This is a schematic diagram of the hardware structure of a mobile phone provided in an embodiment of this application. Detailed Implementation

[0111] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. In the description of this application, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. "And / or" in this application 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, where A and B can be singular or plural. Furthermore, in the description of this application, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. In addition, in order to clearly describe the technical solutions of the embodiments of this application, the terms "first" and "second" are used in the embodiments of this application to distinguish the same or similar items with basically the same function and effect.

[0112] Those skilled in the art will understand that the terms "first," "second," etc., do not limit the quantity or order of execution, and that "first," "second," etc., are not necessarily different. Furthermore, in some embodiments of this application, words such as "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner for ease of understanding.

[0113] Furthermore, the device architecture and business scenarios described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of device architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0114] With the widespread use of smart devices, people frequently take photos in their daily lives, resulting in a large accumulation of pictures in their albums, many of which are similar or duplicates. This large number of photos takes up considerable storage space on smart devices, and finding specific photos among them is very difficult. Therefore, users' need for photo organization is increasing daily.

[0115] In related technologies, smart terminal devices can automatically classify photos, and smart terminal devices can also support users to manually classify photos.

[0116] For example, smart terminal devices can automatically categorize photos by time, such as classifying photos by year, grouping photos from 2023 into one category and photos from 2024 into another.

[0117] As another example, smart terminal devices can automatically categorize events, such as grouping party photos into one category, travel photos into another, and conference photos into yet another.

[0118] For another example, smart terminal devices can categorize items by type, such as grouping food photos into one category, landscape photos into another, and document photos into yet another. Of course, each of these item categories can correspond to a tag, and the smart terminal device can then categorize items based on these tags.

[0119] As another example, smart terminal devices can also classify photos according to their shooting location, such as grouping photos taken at home into one category and photos taken at school into another.

[0120] As another example, smart terminal devices can also categorize photos by people, such as group photos and selfies.

[0121] However, on the one hand, categorizing photos by time or people would group photos from different users together, or by event would group photos from different scenes together, resulting in a crude classification method. Furthermore, categorizing by time ignores the relationships between photos, such as relationships between people or between people and locations, leading to a simplistic and inflexible classification result. Simultaneously, when receiving photos from other devices, categorizing by time can cause timeline confusion, mixing received photos with those taken, thus hindering users' ability to view their own photos.

[0122] On the other hand, for low-quality or blurry photos, smart terminal devices for facial recognition, location recognition, and scene recognition may not be able to accurately identify them, thus affecting the accuracy of photo classification.

[0123] The aforementioned sorting method makes it inconvenient and time-consuming for users to find photos that meet specific criteria, thus reducing the user experience.

[0124] To address the aforementioned problems, embodiments of this application provide an image display method. The method includes: a first device displaying a first interface of a target application, the first interface including at least one image set, wherein the image structure similarity between images in the target image set satisfies a preset similarity condition. The first device can also, in response to a trigger operation on the target image set, display a second interface of the target application, the second interface including multiple images from the target image set.

[0125] The first device in this application embodiment is capable of classifying multiple images to be sorted in a target application and displaying at least one set of classified images.

[0126] In this context, the image structure similarity between images in an image set meets a preset similarity condition. That is, the image structures of images within the same image set are highly similar, meaning that the images in the same set are either highly similar scenes or highly similar people.

[0127] Thus, this embodiment of the application classifies images according to their structure, with sufficiently fine dimensions to completely categorize people and scenes without merging images with different structures. This improves the accuracy of image classification. Simultaneously, it fully displays the image details that users care about, enhancing the efficiency of image searching and the overall user experience.

[0128] In the embodiments of this application, the first device may be a mobile phone, tablet computer, wearable device, vehicle-mounted device, augmented reality (AR) / virtual reality (VR) device, mobile phone, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), etc. The embodiments of this application do not impose any restrictions on the specific type of the first device.

[0129] The operating system installed on the first device includes, but is not limited to, Alternatively, other operating systems may be used. This application does not limit the specific type of the first device or, if an operating system is installed, the type of operating system.

[0130] For example, taking a mobile phone as the first device, Figure 1 A structural schematic diagram of mobile phone 100 is shown.

[0131] Mobile phone 100 may include processor 110, external memory interface 120, internal memory 121, Universal Serial Bus (USB) interface 130, charging management module 140, power management module 141, battery 142, antenna 1, antenna 2, mobile communication module 150, wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, headphone jack 170D, sensor module 180, buttons 190, motor 191, indicator 192, camera 193, display screen 194, and Subscriber Identification Module (SIM) card interface 195, etc.

[0132] The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, a barometric pressure sensor 180C, a magnetic sensor 180D, an accelerometer sensor 180E, a distance sensor 180F, a proximity sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, an ambient light sensor 180L, a bone conduction sensor 180M, and an image sensor 180N.

[0133] It is understood that the structures illustrated in the embodiments of this application do not constitute a specific limitation on the mobile phone 100. In other embodiments of this application, the mobile phone 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.

[0134] Processor 110 may include one or more processing units, such as an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). These different processing units may be independent devices or integrated into one or more processors.

[0135] The controller can serve as the central nervous system and command center of the mobile phone 100. Based on the instruction operation code and timing signals, the controller generates operation control signals to control the fetching and execution of instructions.

[0136] The processor 110 may also include a memory for storing instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. This memory can store instructions or data that the processor 110 has just used or that are used repeatedly. If the processor 110 needs to use the instruction or data again, it can retrieve it directly from the memory. This avoids repeated accesses, reduces the waiting time of the processor 110, and thus improves the efficiency of the system.

[0137] The wireless communication function of mobile phone 100 can be realized through antenna 1, antenna 2, mobile communication module 150, wireless communication module 160, modem processor and baseband processor.

[0138] The wireless communication module 160 can provide solutions for wireless communication applications on the mobile phone 100, including Wireless Local Area Networks (WLAN) (such as Wireless Fidelity (Wi-Fi) networks), Bluetooth (BT), Global Navigation Satellite System (GNSS), Frequency Modulation (FM), Near Field Communication (NFC), and Infrared (IR) technologies. The wireless communication module 160 can be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via antenna 2, modulates and filters the electromagnetic wave signals, and sends the processed signal to processor 110. The wireless communication module 160 can also receive signals to be transmitted from processor 110, modulate and amplify them, and then convert them into electromagnetic waves for radiation via antenna 2.

[0139] In some embodiments, antenna 1 of mobile phone 100 is coupled to mobile communication module 150, and antenna 2 is coupled to wireless communication module 160, enabling mobile phone 100 to communicate with networks and other devices via wireless communication technology. Wireless communication technology may include Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time-Division Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technologies, etc. GNSS can include the Global Positioning System (GPS), the Global Navigation Satellite System (GLONASS), the Beidou Navigation Satellite System (BDS), the Quasi-Zenith Satellite System (QZSS), and / or Satellite Based Augmentation Systems (SBAS).

[0140] The mobile phone 100 implements display functions through a GPU, a display screen 194, and an application processor. The GPU is a microprocessor for image processing, connected to the display screen 194 and the application processor. The GPU is used to perform mathematical and geometric calculations and for graphics rendering. The processor 110 may include one or more GPUs, which execute program instructions to generate or modify display information.

[0141] The display screen 194 is used to display images, videos, etc. The display screen 194 includes a display panel. In some embodiments, the mobile phone 100 may include one or N displays screens 194, where N is a positive integer greater than 1.

[0142] The mobile phone 100 can achieve shooting functions through ISP, camera 193, video codec, GPU, display 194 and application processor.

[0143] The ISP (Image Signal Processor) is used to process data fed back from the camera 193. For example, when taking a picture, the shutter is opened, and light is transmitted through the lens to the camera's photosensitive element. The light signal is converted into an electrical signal, and the camera's photosensitive element transmits the electrical signal to the ISP for processing, transforming it into an image visible to the naked eye. The ISP can also perform algorithmic optimization on image noise and brightness. The ISP can also optimize parameters such as exposure and color temperature of the shooting scene. In some embodiments, the ISP can be set in the camera 193.

[0144] Camera 193 is used to capture still images or videos. An object is projected onto a photosensitive element by generating an optical image through the lens. The photosensitive element can be a charge-coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the light signal into an electrical signal, which is then passed to an ISP for conversion into a digital image signal. The ISP outputs the digital image signal to a DSP for processing. The DSP converts the digital image signal into image signals in standard RGB, YUV, or other formats. In some embodiments, mobile phone 100 may include one or N cameras 193, where N is a positive integer greater than 1.

[0145] Here, the camera 193 can be located within the mobile phone 100. Alternatively, it can be a component of the first device. In some implementations, the camera can also be located externally to the first device and connected via wired or wireless means. For example, the camera can connect to the first device via Bluetooth or a mobile hotspot. The first device can control the camera by sending or receiving commands.

[0146] The mobile phone 100 may also include a camera module, which can be located within the camera 193. Alternatively, it can be located in other positions within the mobile phone 100. The camera module includes a lens, a focusing motor, a base, a circuit board, and an image sensor.

[0147] The base is fixedly connected to one side of the circuit board. The focusing motor is located on the side of the base away from the circuit board and is fixedly connected to the periphery of the base. The lens is mounted in the center of the focusing motor. The image sensor is fixed to the side of the circuit board facing the lens.

[0148] The lens is used to capture the light signal reflected from the subject. The focusing motor is used to drive the lens to move in a direction parallel to the optical axis. The optical axis refers to the line passing through the center of the lens. In some embodiments, the mobile phone 100 can control the focusing motor to move the lens to the focusing position, thereby completing the focusing process.

[0149] In some embodiments, the focusing motor may be a voice coil motor (VCM), a shape memory alloy (SMA) motor, a ceramic motor (Piezo Motor, PM), or a stepper motor (STM), etc.

[0150] The external storage interface 120 can be used to connect an external storage card, such as a Micro SD card, to expand the storage capacity of the mobile phone 100. The external storage card communicates with the processor 110 through the external storage interface 120 to perform data storage functions. For example, music, video, and other files can be saved on the external storage card.

[0151] The mobile phone 100 can achieve audio functions such as music playback and recording through the audio module 170, speaker 170A, receiver 170B, microphone 170C, headphone jack 170D, and application processor.

[0152] The image sensor 180N can be used to detect objects within the range captured by the camera, with each photosensitive unit corresponding to a pixel in the image sensor. The image sensor 180N may include a color (red, green, blue, RGB) image sensor, a monochrome image sensor, and an infrared image sensor, etc., but this embodiment does not limit the specific type. The image sensor 180N is used to acquire raw images, which may include RGB images, RYB images, monochrome images, and infrared images, etc.

[0153] For ease of description, the 180N image sensor will be used as an example, with an RGB image sensor as well as an example of an RGB image as the original image. For instance, the original image can be a single frame of an RGB image. Each photosensitive unit is covered with an RGB (red, green, blue) filter. Thus, after receiving light, the photosensitive unit generates a corresponding current, the magnitude of which corresponds to the light intensity. Therefore, the electrical signal directly output by the photosensitive unit is analog. This analog electrical signal is then converted into a digital signal, and finally, all the resulting digital signals are output as a digital image matrix to a dedicated DSP processing chip for processing. The RGB image sensor outputs a full-frame image of the captured area in frame format.

[0154] In some embodiments, multiple image sensors can be arranged in the same camera. For example, a single-lens dual-sensor camera integrates both an RGB image sensor and a motion sensor within a single camera. Other examples include dual-lens dual-sensor cameras and single-lens triple-sensor cameras, where these sensors are used to image the same subject. When the number of lenses is less than the number of sensors, a beam splitter can be placed between the lenses and sensors to distribute the light entering through one lens across multiple sensors, ensuring that each sensor receives light. Furthermore, the number of processors in these cameras can be one or more. This application does not specifically limit the arrangement or number of components.

[0155] In other embodiments, only one image sensor may be provided in the same camera.

[0156] In some embodiments, camera calibration is typically performed before the image sensor leaves the factory to make the image information acquired by the image sensor more accurate.

[0157] In some embodiments, the camera 193 can capture still images or moving images. The display screen 194 is used to display the still images or moving images.

[0158] The first device provided in this application embodiment can run an operating system (OS). This operating system can be various operating systems used in industry, such as an operating system developed based on OpenHarmony, for example... Or other operating systems, such as The iOS mobile operating system; it can also be various open-source operating systems or their derivatives, such as Linux. This includes other embedded operating systems; it can also refer to future new operating systems, such as AI operating systems based on artificial intelligence. An operating system is a set of interconnected system software programs that manage and control the operation of a primary device, utilize and run hardware and software resources, and provide public services to organize user interactions.

[0159] The operating system in the first device connects downwards to the physical devices of the hardware layer and upwards to provide a runtime environment for application software.

[0160] An operating system typically includes a kernel layer, a middleware layer, and an application layer. The application layer comprises applications, which can include system applications and third-party applications. The middleware layer includes a suite of software providing various services to application developers, or frameworks providing services such as databases, multimedia, and graphics, or capabilities such as distributed scheduling and system scaling.

[0161] For example, the middleware layer may include a framework layer and / or a system service layer. The framework layer provides application programming interfaces (APIs) and programming frameworks for applications in the application layer. The system service layer includes the core capabilities of the system and provides services to applications through the framework layer. The kernel layer is the layer between hardware and software. The kernel layer may include hardware drivers and the operating system kernel. In addition to providing hardware drivers, the kernel layer also supports functions such as memory management and system process management.

[0162] The types and forms of first devices we use in our daily lives vary greatly, and the scenarios in which they are applied are also very wide. Therefore, based on the different forms and functions of first devices, different application scenarios, and different user needs, the operating systems used on first devices may also be different. The basic functions implemented by the first device provided in this application can be implemented using a general-purpose operating system or a dedicated operating system.

[0163] To more clearly illustrate the implementation of the embodiments of this application under a specific operating system, the following is shown. Based on the architecture, those skilled in the art can deduce the implementation of the embodiments of this application under other specific operating systems, such as... Implementation under operating systems, etc.

[0164] Figure 2 This is a software structure block diagram of the mobile phone 100 according to an embodiment of this application.

[0165] The software architecture of Mobile Phone 100 can be divided into several layers. In some embodiments, from bottom to top, these layers are: kernel layer, system service layer, framework layer, and application layer. Layers communicate with each other through software interfaces. System functions can be tailored, added, or combined at the subsystem level depending on the deployment scenario of different device forms. Each subsystem can also be tailored, added, or combined at the functional level.

[0166] The kernel layer includes the kernel abstraction layer, the kernel subsystem, and the driver subsystem.

[0167] The Kernel Abstraction Layer (KAL) provides basic kernel capabilities to upper layers by shielding the differences between multiple kernels, including but not limited to process / thread management, memory management, file system, network management, and peripheral device management.

[0168] Kernel Subsystem: Supports the selection of a suitable OS kernel for different resource-constrained devices, including but not limited to Linux kernel, HarmonyOS kernel, LiteOS (Lite Operating System), etc.

[0169] Driver Subsystem: The driver framework is the foundation for the open system hardware ecosystem, providing unified peripheral access capabilities and a framework for driver development and management. The driver framework includes: display drivers, camera drivers, audio drivers, Bluetooth drivers, sensor drivers, etc.

[0170] The system service layer comprises the core capabilities of the system, providing services to applications through the framework layer. This layer includes, but is not limited to, the following subsystems:

[0171] The system's basic capability subsystem set provides fundamental capabilities for the operation, scheduling, and migration of distributed applications across multiple devices. This set may include distributed soft bus, distributed data management, distributed task scheduling, and Ark multi-language runtime; it may also include multi-modal input subsystem, graphics subsystem, security subsystem, and AI business subsystem.

[0172] Basic software service subsystem set: provides public and general software services; the basic software service subsystem set may include event notification subsystem, telephone service subsystem, multimedia subsystem, etc.

[0173] Enhanced software service subsystem suite: Provides differentiated enhanced software services for different devices; the enhanced software service subsystem suite may include smart screen proprietary business subsystem, wearable proprietary business subsystem, IoT proprietary business subsystem, etc.

[0174] Hardware service subsystem set: Provides hardware services; the hardware service subsystem set may include location service subsystem, user IAM (Identity and Access Management) subsystem, wearable proprietary hardware service subsystem, biometric identification subsystem, IoT proprietary hardware service subsystem, etc.

[0175] Distributed task scheduling enables distributed service management (discovery, synchronization, registration, and invocation), supporting remote startup, remote invocation, remote connection, and migration of applications across devices.

[0176] Distributed data management enables data synchronization, data storage, data sharing, and data access across all scenarios and devices.

[0177] The distributed soft bus provides communication-related capabilities for seamless interconnection between multiple devices, including: WLAN service capabilities, Bluetooth service capabilities, soft bus, inter-process communication RPC (Remote Procedure Call), and StarFlash communication capabilities.

[0178] Ark Multilingual Runtime is a unified compilation runtime platform designed to support the joint compilation and execution of multiple programming languages ​​and multiple chip platforms.

[0179] The framework layer provides application programming interfaces (APIs) and programming frameworks for applications in the application layer. The framework layer includes: the ArkUI framework (which provides a complete infrastructure for UI development of system applications, including UI functions such as components, layouts, animations, and interactive events, as well as a real-time interface preview tool), the user application framework, and the Ability framework (an Ability is a lightweight application; the Ability framework schedules and manages the operation and lifecycle of Abilities). Different devices may have different operating systems, and the APIs they support may also differ.

[0180] The HarmonyOS API is designed to support... HarmonyOS API provides a range of open capabilities for application development. It can be configured at the framework layer or independently of it. The HarmonyOS API includes the Audio API, Push API, and Account API, among others.

[0181] In some examples, the framework layer also includes a file subsystem, a media framework subsystem, an AI framework, and capability development services.

[0182] The file subsystem includes a media library and a media library service. The media framework subsystem and the media library can store static images and dynamic images.

[0183] The AI ​​framework provides AI capabilities, which can include speech and vision service services. These services can include a vision engine and a speech engine. The vision engine provides capabilities such as subject segmentation, face detection, and text recognition. The speech engine provides capabilities such as speech translation, natural language understanding, speech recognition, and speech synthesis.

[0184] Capability development services include image analysis business and image analysis capabilities. Among them, image analysis capabilities can include image text recognition, natural language understanding in images, subject segmentation, and face detection capabilities.

[0185] Applications can include system apps and extended / third-party apps. System apps can include the desktop, control bar, settings, contacts, input method, gallery, etc., while extended / third-party apps can include social apps, travel apps, etc.

[0186] In some examples, the application layer may also include a human-computer interaction module, a shooting information parameter acquisition module, an image structure similarity classification module, a face classification module, an image quality assessment module, a quality inverted index module, and a folding and hiding module.

[0187] The human-computer interaction module is used to receive user input trigger operations and control the subsequent program execution.

[0188] The shooting information parameter acquisition module is used to acquire the shooting information parameters corresponding to the image.

[0189] The image structure similarity classification module is used to perform image structure similarity analysis on images.

[0190] The face classification module is used to detect faces in images.

[0191] The image quality assessment module is used to calculate the image quality index corresponding to the image.

[0192] The quality inverted sorting module is used to sort multiple images based on their corresponding image quality indices.

[0193] The collapse and hide module is used to collapse and hide multiple images.

[0194] The following embodiments will be described in conjunction with the accompanying drawings, with the first device as an example. Figure 1 Taking a mobile phone with the structure shown as an example, the image display method provided in this application embodiment will be described. See also... Figure 3 The method may include:

[0195] S301, The phone displays the third interface of the target application.

[0196] The third interface includes the first control corresponding to the target application, or the first control is displayed floating on the third interface.

[0197] In some embodiments of this application, a user can use a target application on their mobile phone to view captured images. The target application may include a gallery application, an image application with image viewing functionality, and an image application with both image viewing and editing functionality, etc. The user can use the target application to achieve image viewing and editing functions. Of course, the mobile phone can also use other methods besides the target application to achieve this, and this application does not specifically limit this.

[0198] Specifically, users can instruct their phones to launch specific applications. There are several ways a phone can launch a target application.

[0199] Typically, users can select the icon corresponding to a target application on their phone's home screen to launch the application. This launch can be achieved by tapping the application's icon or by using voice commands to open the target file.

[0200] For example, Figure 4 This is a schematic diagram of the interface for an application scenario illustrated in an embodiment of this application.

[0201] like Figure 4 As shown in (A), a user can click the target application icon on the phone's home screen, and the phone will launch the target application in response to the user's click.

[0202] like Figure 4 As shown in (B), the phone responds to the user's tap and displays the third interface of the target application. At this point, the target application is launched. Alternatively, the phone can also launch the target application in response to a user's voice command. Afterward, the user can use the target application to view and edit images, such as viewing and sharing captured images.

[0203] See also Figure 4 In (B), the third interface of the target application includes a first control and a portion of the images to be organized corresponding to the target application. Users can trigger the organization of the images in the target application by clicking the first control and inputting organization commands for multiple images to be organized.

[0204] The images to be organized in the target application can be all the images in the target application. Alternatively, the images to be organized in the target application can be a subset of images selected by the user within the target application. This subset may include images displayed in the third interface, or images not displayed in the third interface.

[0205] It should be noted that the embodiments of this application do not specifically limit the number of images to be processed.

[0206] In some embodiments of this application, the first control in the third interface can be presented as a floating control, which allows the user to move it to any position on the display screen. It should be noted that the first control in the third interface can also exist in a function menu.

[0207] It should be noted that the first control can be a control corresponding to the target application, meaning that after the target application starts running, it displays a third interface that includes the first control. Of course, the first control can also be displayed floating on the third interface.

[0208] For example, see Figure 5In section (A), the third interface may include functional controls corresponding to the target application. Users can click on these controls to trigger the phone's menu for accessing the target application. The phone responds to the user's click on the functional controls by displaying the target application's menu. See also... Figure 5 In (B), the target application's function menu includes a first control. This allows the user to click the first control in the function menu to trigger the phone to categorize the images to be organized.

[0209] In some embodiments of this application, the first control described above may also exist independently of the target application, such as an operating system control. The first control may also be displayed floating on a third interface. This application does not specifically limit the implementation form of the first control.

[0210] S302, the mobile phone responds to a first operation on a first control and displays a first interface of a target application, the first interface including at least one set of images.

[0211] In some embodiments of this application, the mobile phone can classify multiple images to be sorted to obtain at least one image set, and display at least one image set.

[0212] For example, see Figure 6 In (A), the user can click on the first control in the third interface. The phone can categorize multiple images to be sorted and display the first interface of the target application. See also Figure 6 In (B) of this document, the first interface may include multiple image sets. It should be noted that the aforementioned image sets may also be referred to as albums, photo sets, etc., and this embodiment of the application does not specifically limit them in this way.

[0213] It should be noted that, for a single image set displayed on the first interface, both the cover image and the number of images in the image set can be displayed simultaneously. For multiple image sets displayed on the first interface, these image sets can be presented in a grid view, list view, or carousel. For example, the images in each image set (excluding the cover image) can be collapsed and displayed alongside the cover image, and multiple image sets can be arranged sequentially into a list for vertical or horizontal display; this application does not limit this approach.

[0214] In some embodiments of this application, during the process of classifying multiple images to be sorted, the mobile phone can perform image structure similarity analysis on the multiple images to be sorted, and divide the multiple images whose image structure similarity meets the preset similarity conditions into an image set, thereby obtaining at least one image set in the first interface.

[0215] In other words, in an image set containing multiple images, the images within each image set must satisfy a preset similarity condition. Satisfying the preset similarity condition can include the image structural similarity between images being greater than or equal to a preset threshold, or multiple images having an image structural similarity with the same image that is greater than or equal to a preset threshold.

[0216] Image structural similarity can be used to measure the degree of similarity between images. Structural similarity can be evaluated from three aspects: brightness, contrast, and structure. Brightness refers to the overall brightness of the image; contrast is the difference between bright and dark areas in the image; and structure refers to information such as the shape and arrangement of objects in the image. For example, the Structural Similarity Index (SSIM) can be used to measure the similarity between two images.

[0217] For example, a mobile phone can use a similarity model to calculate the image structural similarity of multiple images to be sorted.

[0218] Specifically, the mobile phone can use a similarity model to traverse multiple images to be sorted.

[0219] For example, the preset threshold can be 0.8. Multiple images to be processed include image A, image B, image C, image D, and image E. The similarity model can use image A as a reference and iterate through images A, B, C, D, and E. The image structure similarity between image B and image A is 0.85. The image structure similarity between image C and image A is 0.88. The image structure similarity between image D and image A is 0.6. The image structure similarity between image E and image A is 0.2. Thus, the image structure similarity between images B and C and image A is greater than the preset threshold, and images A, B, and C can be grouped into one image set. Then, the similarity model can continue to perform the image structure similarity process using image D as a reference. This embodiment does not specifically limit the specific value corresponding to the preset threshold or the generation process corresponding to the preset threshold.

[0220] For example, continuing with the preset threshold of 0.8, consider multiple images to be processed, including image A, image B, image C, image D, and image E. The similarity model can iterate through images A, B, C, D, and E. The structural similarity between image B and image A is 0.85. The structural similarity between image C and image A is 0.88. The structural similarity between image B and image C is 0.81. Therefore, the structural similarity between image B and image A is greater than the preset threshold, the structural similarity between image C and image A is greater than the preset threshold, and the structural similarity between image B and image E is greater than the preset threshold. Thus, images A, B, and C can be grouped into one image set.

[0221] This application embodiment also trains a similarity model. When training the similarity model, a large number of images can be used as training samples to enable the similarity model to learn the ability to recognize structural similarities in images (such as the ability to output SSIM). This application embodiment does not limit the type of model corresponding to the similarity model, such as neural network models, artificial intelligence (AI) models, etc. It should be noted that this application embodiment does not limit the specific implementation of image structural similarity between images.

[0222] In one feasible approach, the aforementioned multiple images to be processed can be all images in the target application.

[0223] For example, the target application includes images of different people and different scenes. After the phone performs image structure similarity analysis on multiple images to be processed, resulting in at least one image set, the images within the same image set exhibit strong similarity. That is, images of people will not be grouped into the same image set as images of scenes. The same image set can include images all about person A, images all about person B, images all about building A, and so on.

[0224] In another possible approach, the aforementioned multiple images to be sorted can be a subset of images selected by the user from a target application.

[0225] For example, multiple images to be processed include images taken at the same location and at a time that meets preset time requirements (e.g., location A, preset time requirements including month-based time requirements, such as images taken in January 2024). After the mobile phone performs image structure similarity analysis on the above images to be processed to obtain at least one image set, the same image set may include images all about location A, taken in January 2024, and featuring person C, or images all about location A, taken in January 2024, and featuring item A, etc.

[0226] For example, multiple images to be processed include images taken at the same location and at a time that meets preset time requirements (e.g., location A, preset time requirements including day-based time requirements, such as images taken on August 1, 2024). After the mobile phone performs image structure similarity analysis on the above images to be processed to obtain at least one image set, the same image set can include images all related to location A, dated August 1, 2024, and featuring person C, or images all related to location A, dated August 1, 2024, and featuring item A, etc.

[0227] Thus, this embodiment of the application classifies images according to their structure, with sufficiently fine dimensions to completely categorize people and scenes without merging images with different structures. This improves the accuracy of image classification. Simultaneously, it fully displays the image details that users care about, enhancing the efficiency of image searching and the overall user experience.

[0228] In some embodiments of this application, when there are multiple videos in the target application, the mobile phone can also classify the multiple videos.

[0229] Specifically, the mobile phone can classify multiple videos based on the target image frame corresponding to each video, resulting in at least one video set. The target image frame may include the first frame image. In other words, the mobile phone can perform image structure similarity analysis on the first frame images of multiple videos, and group multiple videos whose image structure similarity of the first frame images meets the preset similarity conditions into one image set, thus obtaining at least one video set.

[0230] In some embodiments of this application, in addition to classifying images based on structural similarity, the mobile phone can also classify multiple images to be sorted by combining shooting information parameters.

[0231] The shooting information parameters include at least one of the following: shooting time, shooting location, and subject type. Subject type can include people, landscapes, animals, and buildings, etc.

[0232] It should be noted that for images received and stored by the mobile phone, i.e., images not taken by the mobile phone, the time of receiving or storing the image can be assumed to be the time of its capture. This application does not limit this aspect.

[0233] For example, a mobile phone can first classify multiple images to be sorted based on the shooting information parameters, and then perform a second classification based on the similarity of image structures.

[0234] In one feasible approach, the shooting information parameters may include the shooting time and location. The mobile phone can acquire the shooting information parameters of multiple images to be processed and perform an initial classification of images among the multiple images to be processed that meet the preset time requirements and / or have the same shooting location.

[0235] Meeting the preset time requirement can include meeting a preset time period, such as 1 day or 1 hour. Meeting the preset time requirement can also include the time interval between the capture of adjacent images in the image set being less than a preset time interval, such as 10 minutes, 20 minutes, or 30 minutes. This application does not specifically limit the preset time interval.

[0236] Meeting the requirement of the same shooting location can include the shooting locations of images in the image set meeting a preset distance requirement, such as the shooting locations of images being 5 meters, 10 meters, 100 meters, or 500 meters apart. This application embodiment does not specifically limit the preset distance requirement. In other words, if the shooting locations of images meet the preset distance requirement, they can be considered to have the same shooting location.

[0237] Specifically, the mobile phone can obtain shooting information parameters corresponding to multiple images to be sorted in the target application. The shooting information parameters include shooting time and shooting location. Based on preset conditions and shooting information parameters, the multiple images to be sorted are divided into O image sets, where O is an integer greater than 1. The mobile phone can also perform image structure similarity analysis on the images in any of the O image sets, and group multiple images in the image set whose image structure similarity meets the preset similarity conditions into one image set, thus obtaining at least one image set in the first interface.

[0238] For example, see Figure 7 The preset time requirement was 1 hour, and images from the same shooting location were grouped together. From 17:00 to 20:00 on November 4, 2024, images were taken between locations A and C, passing through location B. From 18:00 to 19:00 on November 4, 2024, images were taken at locations A, B, and C respectively, for a total of 42 images. Of these, 23 images were taken at location A, 8 at location B, and 11 at location C.

[0239] The images to be processed can be divided into five groups according to the shooting time and different shooting locations, resulting in the following preliminary classification results: Group 1: 15 images from location A between 18:00 and 19:00 on November 4, 2024; Group 2: 8 images from location B between 18:00 and 19:00 on November 4, 2024; Group 3: 3 images from location C between 18:00 and 19:00 on November 4, 2024; Group 4: 8 images from location A between 17:00 and 18:00 on November 4, 2024; and Group 5: 8 images from location C between 19:00 and 20:00 on November 4, 2024.

[0240] The mobile phone can also perform secondary classification on the preliminary classification results based on the similarity of image structures.

[0241] See Figure 8 Taking Group 1 (15 images of location A from 18:00-19:00 on November 4, 2024) from the preliminary classification results as an example, this group of images can be further divided. The mobile phone can group images with a structural similarity greater than or equal to a preset threshold into one category. Since the same person or object photographed in the same scene may have low image structural similarity due to different angles, this embodiment of the application can maximize the grouping of images with structural similarity into one category for easier user selection and querying.

[0242] As can be seen, after secondary classification, the images in Group 1 above yield 10 classification results, i.e., 10 image sets. Groups 1, 2, and 3 depict the same person; due to different backgrounds, their image structural similarity is low, thus achieving differentiation. Groups 4, 5, and 6 are images of object A taken from different angles; due to different shooting perspectives, their image structural similarity is low, thus achieving differentiation. Groups 7 and 8 are images of building A taken from two different perspectives, divided into two groups. Groups 9 and 10 are images of building B taken from the same angle; due to different lighting and seasons, they are divided into two groups. It should be noted that the specific implementation of image structural similarity can be found in the above embodiments, and will not be repeated here.

[0243] In another feasible approach, the shooting information parameters can include only the shooting time or only the shooting location. The phone can perform an initial classification of multiple images to be processed based on the shooting time, and then perform a secondary classification based on image structural similarity. Alternatively, the phone can perform an initial classification of multiple images to be processed based on the shooting location, and then perform a secondary classification based on image structural similarity.

[0244] In another feasible approach, the shooting information parameters can include the type of the subject and the shooting time. The phone can perform an initial classification of multiple images to be processed based on the subject type and shooting time, such as grouping images whose shooting time meets a preset requirement and whose shooting type is the same. Then, the phone performs a secondary classification based on the image structural similarity.

[0245] In another feasible approach, the shooting information parameter can be the type of the captured object. The phone can perform an initial classification of multiple images to be processed based on the type of the captured object, such as grouping images of the same shooting type together. Then, the phone performs a secondary classification based on the similarity of image structure.

[0246] This application embodiment can classify multiple images to be sorted in a target application and display at least one set of images after classification.

[0247] This allows for the classification of multiple images to be processed based on factors such as whether the captured information parameters meet preset conditions and the similarity of image structures.

[0248] In other words, the images are highly similar in terms of both shooting information parameters and image structure, indicating a close relationship. This allows images with similar or identical shooting times, locations, or content to be grouped into an image set, where the images within that set exhibit a high degree of structural similarity, such as consisting of highly similar scenes or people.

[0249] Thus, this embodiment classifies images according to shooting information parameters and image structure. The classification dimensions are sufficiently fine to completely categorize people and scenes without merging images with different structures. This improves the accuracy of image classification. At the same time, it fully displays the image details that users care about, improving the efficiency of image searching and the user experience.

[0250] In some embodiments of this application, the mobile phone can perform image quality evaluation on images in at least one image set and set the image with the highest image quality as the cover image.

[0251] In some examples, while displaying the first interface of the target application, the mobile phone may display a cover image corresponding to at least one set of images, which may be displayed as a thumbnail.

[0252] For example, for a target image set, the mobile phone can display the image with the highest image quality index in the target image set as the cover image of the target image set in the first interface. The image quality index is used to characterize the image quality.

[0253] Specifically, the mobile phone can calculate the image quality index of each image in any image set within at least one image set. For example, considering a target image set within at least one image set, the mobile phone can calculate the image quality index of each image in the target image set and sort the images in the target image set from highest to lowest image quality index. The mobile phone can also set the top-ranked image in the sorting result as the cover image corresponding to the target image set and display that cover image.

[0254] In this embodiment, the mobile phone can evaluate the image quality of all images in each image set and set the image with the highest image quality as the cover image of the image set. All other photos except the highest quality image are then folded up and hidden behind the cover image to form an image set. Subsequently, when a user clicks on the cover image of an image set, all images in the set can be displayed flat.

[0255] Thus, in this embodiment, the image with the highest image quality index, ranked first in any image set, is set as the cover image for that image set and displayed. This allows for the display of high-quality images with clearer and richer colors, enhancing visual appeal. Simultaneously, high-quality images help accurately convey image information, avoiding misunderstandings caused by poor image quality. This makes it easier for users to accurately understand the image content of the corresponding image set, improving the user experience.

[0256] In some embodiments of this application, the image quality index is determined based on a first parameter and / or a second parameter.

[0257] The first parameter includes at least one of resolution, exposure, sharpness, noise, contrast and color saturation, and the second parameter includes at least one of face angle parameter, face expression parameter, face occlusion parameter, face skin color parameter, face makeup parameter and face texture parameter.

[0258] Facial makeup parameters can include features modification parameters, such as eyebrow type, eye size, eye makeup color, eyeshadow range, eyeliner thickness, eyelash length and density, lip color, lip line thickness, lip fullness, blush color, blush position, blush position and intensity, highlight parameters, shadow parameters, and skin smoothing parameters.

[0259] In one feasible approach, the first parameters include resolution, exposure, and sharpness, while the second parameters include face angle parameters, face expression parameters, and face occlusion parameters.

[0260] In another possible implementation, the second parameter includes facial expression parameters.

[0261] In another possible implementation, the second parameter includes a face occlusion parameter.

[0262] In another possible implementation, the second parameter includes facial expression parameters and facial occlusion parameters.

[0263] In some embodiments of this application, at least one image set includes a first image set, wherein the images in the first image set include human faces. See also Figure 9The mobile phone can obtain the first parameter and the second parameter of each image in the first image set (S901). The mobile phone can also calculate the image quality index of each image in the first image set based on the first parameter and the second parameter of each image in the first image set (S902).

[0264] For example, the mobile phone can perform face detection and image detection on the images in the first image set to obtain a first parameter and a second parameter for each image in the first image set. The first parameter includes resolution, exposure, and sharpness, and the second parameter includes face angle parameter, face expression parameter, and face occlusion parameter. The mobile phone can convert the specific values ​​of resolution, exposure, sharpness, face angle parameter, face expression parameter, and face occlusion parameter into a uniform quality value of 0-10.

[0265] For example, higher image resolution indicates greater image richness and a higher image quality score. Higher or lower exposure indicates overexposure or underexposure, resulting in a lower image quality score. Higher sharpness indicates greater image clarity and a higher image quality score. Face angle represents a frontal view of the face, indicating a more complete image and a higher image quality score. Face occlusion parameters represent an unobstructed face, indicating a more complete image and a higher image quality score. Facial expression parameters represent a smiling face, indicating a more positive mood and a higher image quality score.

[0266] In this way, the mobile phone can sum up the quality values ​​corresponding to resolution, exposure, sharpness, face angle parameters, face expression parameters, and face occlusion parameters to determine the image quality index of each image in the first image set.

[0267] In another possible approach, the mobile phone can determine the image quality index of the image based on any one or more of the first and second parameters mentioned above, without any specific limitation in this application.

[0268] For example, lower image noise indicates that details in the image may be excessively attenuated, making the image blurry. Higher image noise can cause color deviations or unevenness; that is, excessively high or low noise corresponds to a lower image quality score. Higher image contrast can cause bright areas to be too bright and dark areas to be too dark, resulting in the hiding of regional information. Lower contrast makes the difference between bright and dark areas in the image less obvious; that is, excessively high or low contrast corresponds to a lower image quality score.

[0269] Higher color saturation in an image makes it appear less realistic, while lower color saturation makes it appear dull and lifeless. In other words, excessively high or low color saturation results in a lower image quality score. Similarly, lower skin tone parameters indicate lower skin tone hue, brightness, and saturation, leading to a lower image quality score. Excessively high or low makeup parameters suggest a poor complexion and lack of vitality, also affecting aesthetics and resulting in a lower image quality score. Higher facial texture parameters indicate deeper and more numerous wrinkles, larger and denser pores, indicating poorer skin texture and a lower image quality score.

[0270] Thus, this embodiment of the application employs the aforementioned first and second parameters to perform multi-dimensional image quality evaluation, thereby gaining a more comprehensive and accurate understanding of the key features in the image. For example, for images of people, it focuses more on facial angles, expressions, and whether there is occlusion, while also combining evaluation from multiple dimensions such as image resolution, clarity, and exposure to better reflect the characteristics of the person in the image. This helps users filter out high-quality image content and improves the user experience.

[0271] It should be noted that the first parameter mentioned above may include resolution, exposure, and noise, while the second parameter includes face angle parameters, face expression parameters, and face texture parameters. Alternatively, the first parameter may include resolution and noise, while the second parameter includes face angle parameters and face texture parameters. Furthermore, the first parameter may include resolution, exposure, color saturation, and noise, while the second parameter includes face angle parameters, face expression parameters, face texture parameters, face skin tone parameters, and face texture parameters. This application does not limit the specific parameters corresponding to the first and second parameters.

[0272] In some embodiments of this application, when a mobile phone calculates the image quality index of each image in the first image set based on the first parameter and the second parameter of each image in the first image set, it can use a first weighting rule to calculate the first parameter and the second parameter of each image in the first image set to obtain the image quality index of each image in the first image set.

[0273] For example, the first weighting rule may include a weight of 1 for both the first and second parameters. The mobile phone can directly sum the quality values ​​corresponding to resolution, exposure, sharpness, face angle parameters, face expression parameters, and face occlusion parameters to obtain the image quality index of each image in the first image set.

[0274] For another example, the mobile phone uses the weights of the first parameter and the second parameter to calculate the image quality index of each image in the first image set. The weight of any parameter in the first parameter is greater than the weight of any parameter in the second parameter. Of course, the weights corresponding to each parameter in the first parameter can be the same or different. Similarly, the weights corresponding to each parameter in the second parameter can also be the same or different.

[0275] For example, the weights for resolution, exposure, and sharpness are 0.5, 0.6, and 0.7, respectively. The weights for face angle, face expression, and face occlusion are 0.1, 0.2, and 0.3, respectively. (This is repeated four times in the original text.) In this way, the phone can multiply the quality values ​​corresponding to resolution, exposure, and sharpness with the weight of each parameter to obtain the first result. The phone can also multiply the quality values ​​corresponding to face angle parameters, face expression parameters, and face occlusion parameters with the weight of each parameter to obtain the second result. The first result and the second result are then summed to obtain the image quality index of each image in the first image set.

[0276] It should be noted that the weight of any one of the second parameters can be greater than the weight of any one of the first parameters. This application does not specifically limit this. Further, the image quality index of each image can be the sum of the calculated quality values ​​of the first and second parameters. Of course, the image quality index can also be the quality value of the first parameter or the quality value of the second parameter. If the quality values ​​of the first parameters of two images are the same, the image quality can be evaluated based on the quality value of the second parameter; that is, the image quality index can be determined based on the second parameter. Conversely, if the quality values ​​of the second parameters of two images are the same, the image quality can be evaluated based on the quality value of the first parameter; that is, the image quality index can be determined based on the quality value of the first parameter. This application does not specifically limit the implementation form of the image quality index.

[0277] Therefore, by assigning different weights to each dimension based on its importance, the embodiments of this application can make the obtained image quality index more in line with actual needs and highlight the importance of certain key dimensions in the image. For example, setting a higher weight for each parameter in the first parameter can highlight the image quality features in the image. Similarly, setting a higher weight for each parameter in the second parameter can highlight facial features in a person image. This allows the image quality index to better reflect the actual situation corresponding to the image. Consequently, it helps users filter out high-quality image content and improves the user experience.

[0278] In some embodiments of this application, see also [link to previous document]. Figure 9 At least one image set includes a second image set, wherein the images in the second image set do not include human faces. The mobile phone can obtain a first parameter for each image in the second image set (S903). The mobile phone can also calculate an image quality index for each image in the second image set based on the first parameter for each image in the second image set (S904).

[0279] It should be noted that the process of determining the image quality index of each image in the second image set based on the first parameter and the first parameter is similar to the above embodiment, and will not be repeated here.

[0280] In some embodiments of this application, the mobile phone can also use a second weighting rule to calculate the first parameter of each image in the second image set to obtain the image quality index of each image in the second image set.

[0281] For example, the second weighting rule may include giving each parameter in the first set the same weight. For instance, the weights for resolution, sharpness, and exposure are all 1. The phone can directly sum the quality values ​​corresponding to resolution, exposure, and sharpness to obtain the image quality index for each image in the second image set.

[0282] For another example, the second weighting rule may include different weights for each parameter in the first parameter. For instance, the weights for resolution, sharpness, and exposure are all 0.5:0.2:1. The phone can sum half the quality value corresponding to resolution, one-fifth of the quality value corresponding to sharpness, and the quality value corresponding to exposure to obtain the image quality index for each image in the second image set.

[0283] Furthermore, the mobile phone can set corresponding weights for any one or more of the specific parameters, including the first parameter and the second parameter, to determine the image quality index. This application does not specifically limit this aspect.

[0284] Therefore, this embodiment of the application uses the aforementioned first parameter and other dimensions to evaluate image quality, thereby gaining a more comprehensive and accurate understanding of the key features in the image and comprehensively considering the quality of the image. This helps users filter out high-quality image content and improves the user experience. At the same time, through reasonable weighting rules, it can better reflect the true value of the image in a specific purpose, facilitating the subsequent selection of the cover image corresponding to the image set.

[0285] In some embodiments of this application, the mobile phone can perform face detection on each image in at least one image set to determine the aforementioned first image set and second image set. For example, the mobile phone can perform face detection on each image in at least one image set, and label the first image set containing faces as faces, such as assigning a face label Portrait = 1. The second image set not containing faces is labeled as non-face, such as assigning a non-face label Portrait = 0.

[0286] Thus, this embodiment of the application determines the first image set and the second image set by performing face detection on each image in at least one image set. This allows for the classification and management of image sets according to facial features, facilitating subsequent image quality assessment and improving the efficiency of the quality assessment.

[0287] In some embodiments of this application, the mobile phone can also iterate through the image quality index of the images in each image set and sort the multiple images in the same image set from high to low according to the image quality index.

[0288] Therefore, by sorting the images in each image set according to the image quality index, this embodiment of the application can help accurately convey which images in the same image set are of higher quality and which are of lower quality. This makes it easier for users to accurately understand the image content in the image set and improves the user experience.

[0289] In some embodiments of this application, the mobile phone may also output a prompt message indicating that the images in at least one image set have been sorted from high to low according to the image quality index.

[0290] For example, the prompt information may include one or more of text prompts, voice prompts, and video prompts.

[0291] For example, see Figure 10 The phone can display a notification message indicating that the images in the image set have been sorted from highest to lowest image quality index. This lets the user know that the images in the categorized image set are sorted according to their quality.

[0292] Thus, this embodiment of the application can not only classify multiple images to be sorted to obtain at least one image set, but also sort the images in the image set by image quality and notify the user that the sorting is complete. This enhances the user's interaction with the mobile phone and the user experience.

[0293] In some embodiments of this application, the first interface includes a second control, or the second control is displayed floating on the first interface. The mobile phone can also switch the first interface to a third interface in response to a second operation on the second control. That is, the mobile phone also supports users reversing image editing operations.

[0294] The second control can be the same as the first control, but their display effects differ. For example, the displayed text, text style, and fill effect may differ between the two controls. Alternatively, the second control can be a different control from the first control. This application does not limit the implementation form of the second control.

[0295] For example, a user can long-press the second control on the first screen, and the phone can restore at least one image set to the display of multiple images to be sorted before classification, that is, cancel the classification process of multiple images to be sorted. The phone can then switch from the first screen to the third screen.

[0296] It should be noted that the first operation and the second operation described above can be the same operation or different operations, and the embodiments of this application do not specifically limit them.

[0297] Thus, the embodiments of this application can provide users with user-friendly operations for image organization, such as one-click organization and one-click undoing of organization operations. The interaction process is convenient, enhancing the user experience.

[0298] S303, In response to a trigger operation on a target image set, the mobile phone displays a second interface of the target application, the second interface including multiple images in the target image set.

[0299] In some embodiments of this application, a user can click on a target image set in at least one image set to trigger entry into the image interface corresponding to the target image set to view the images in the target image set.

[0300] For example, see Figure 11 In the context of (A), the target image set is image set A. The user can tap image set A, and the phone, in response to the trigger action on image set A, displays a second interface of the target application. This second interface includes three images from image set A, such as... Figure 11 As shown in (B) in the diagram.

[0301] It should be noted that at least one image in the second interface described above can be displayed in a grid view, list view, or carousel.

[0302] For example, multiple images can be arranged in order into a list for vertical or horizontal display. Another example is displaying multiple images in a grid, with each image occupying one grid cell.

[0303] For example, an image carousel can be set up to display multiple images in a fixed area, showing only one image at a time. Users can manually or automatically switch between images to browse other images. Of course, other presentation methods are also possible, and this application does not limit this approach.

[0304] In some embodiments of this application, multiple images in the target image set in the second interface are displayed in descending order of image quality index.

[0305] Based on the above embodiments, the mobile phone can sort multiple images in the same image set from high to low according to their image quality index. Therefore, the display order of the three images in image set A in the second interface is determined by sorting them from high to low image quality index. That is, the image quality index of the first image is greater than that of the second image, and the image quality index of the second image is greater than that of the third image.

[0306] Thus, by displaying images in the image set from high to low according to their image quality index, this embodiment of the application ensures that when a user views images, they can first see the high-quality images and obtain the clearest and most accurate information immediately, thereby enhancing the user experience.

[0307] In some embodiments of this application, the mobile phone can not only display images in the image set from high to low according to the image quality index, but also recommend images to be shared and images to be deleted in the image set according to the image quality index.

[0308] In one possible implementation, the second interface also includes a first prompt identifier, which is used to prompt the sharing of a first image. The first image is one of the top M images in the second interface with the highest image quality index, where M is an integer greater than or equal to 1.

[0309] And / or, the second interface also includes a second prompt icon, which is used to prompt the deletion of the second image. The second image is one of the N images with the lowest image quality index in the second interface, where N is an integer greater than or equal to 1.

[0310] Specifically, the mobile phone can filter out a first image and a second image from a target image set based on an image quality index and preset rules. The first image can be a recommended image for sharing, and the second image can be a recommended image for deletion. The image quality index of the first image is greater than that of the second image.

[0311] For example, the preset rules may include designating the M images with the highest image quality index as the first image and the N images with the lowest image quality index as the second image.

[0312] For example, the preset rule may include the top M images with the highest image quality index as the first image, and the images other than the first image as the second image. This application does not limit the specific implementation of the preset rule.

[0313] Furthermore, the mobile phone can determine M and N based on the index threshold corresponding to the image quality index. An image whose image quality index is greater than the index threshold is determined as the first image and M is obtained. An image whose image quality index is less than or equal to the index threshold is determined as the second image and N is obtained. The index threshold may also include a first threshold and a second threshold. An image whose image quality index is greater than the first threshold is determined as the first image and M is obtained. An image whose image quality index is less than the second threshold is determined as the second image and N is obtained. This embodiment does not specifically limit the determination method of M and N described above.

[0314] In some examples, the phone can set corresponding prompts for the first and second selected images.

[0315] For example, see Figure 12 The first image in image set A is the first image in the second interface, and the second image in image set A is the third image in the second interface. The phone can display a first prompt corresponding to the first image, such as "To be shared" in the first image. The phone can also display a second prompt corresponding to the third image, such as "To be deleted" in the third image.

[0316] It should be noted that the above-mentioned prompts can be displayed in the form of image watermarks, image fills, or other display styles. This application does not limit the display style corresponding to the prompts.

[0317] Thus, this application embodiment, through the above-described method, recommends sharing high-quality images and recommends deleting low-quality images, enabling the sharing of high-quality images while avoiding the occupation of device storage space by low-quality images. This provides users with more user-friendly functionality and enhances the user experience.

[0318] In one possible implementation, the second interface may include a first display area and / or a second display area, wherein the first display area is used to display a first image to be shared, and the first image is one of the top M images in the second interface with the highest image quality index, where M is an integer greater than or equal to 1.

[0319] And / or, the second display area is used to display the second image to be deleted, which is one of the N images with the lowest image quality index in the second interface, where N is an integer greater than or equal to 1.

[0320] For example, see Figure 13 The first image in image set A is the first image in the second interface, and the second image in image set A is the third image in the second interface. The first display area may include the first image in the second interface, and the second display area may include the third image in the second interface. The first display area may be the display area corresponding to a recommended shared image, and the second display area may be the display area corresponding to a recommended deleted image.

[0321] In some embodiments of this application, the mobile phone can send the first image to the second device in response to a sharing operation on the second interface. The mobile phone can also delete the second image in response to a delete operation on the second interface.

[0322] See also Figure 13 The second interface may also include a third display area, which can be an area other than the display areas corresponding to recommended shared images and recommended deleted images. The third display area may include the second image from the second interface.

[0323] Thus, this embodiment of the application, through the above-described method, recommends sharing images with higher quality and recommends deleting images with lower quality, enabling the sharing of high-quality images while avoiding the occupation of device storage space by low-quality images. This provides users with more user-friendly functionality and enhances the user experience.

[0324] In some embodiments of this application, the image structural similarity between images in the target image set is greater than or equal to a preset threshold. That is, after the mobile phone classifies multiple images to be sorted using image structural similarity, the image structural similarity between images in each resulting image set is greater than or equal to the preset threshold.

[0325] In some embodiments of this application, the shooting information parameters between images in the target image set can also meet preset conditions, and the shooting information parameters include at least one of shooting time, shooting location, and shooting object type.

[0326] In other words, after classifying multiple images based on structural similarity and combining shooting information parameters, the image structural similarity between images in each image set is greater than or equal to a preset threshold, and the shooting information parameters meet preset conditions.

[0327] In one possible implementation, the shooting information parameters meet preset conditions, including the shooting time meeting preset time requirements and the shooting location being the same.

[0328] In another possible implementation, the shooting information parameters meet preset conditions, including the same shooting location.

[0329] In another possible implementation, the shooting information parameters meet preset conditions, including the shooting time meeting preset time requirements.

[0330] In another possible implementation, the shooting information parameters meet preset conditions, including that the shooting object type is the same and the shooting time meets the preset time requirement.

[0331] In another possible implementation, the shooting information parameters meet preset conditions, including that the shooting object type is the same.

[0332] Thus, this embodiment classifies images according to shooting information parameters and image structure. The classification dimensions are sufficiently fine to completely categorize people and scenes without merging images with different structures. This improves the accuracy of image classification. At the same time, it fully displays the image details that users care about, improving the efficiency of image searching and the user experience.

[0333] In some embodiments of this application, the execution entity of the above-described image display method is a first device. Specifically, all steps in the image display method can be executed by a target application or by a target module other than the target application. Some steps can be executed by the target application or by a target module other than the target application. The target module can be located in the application layer of the operating system or in the framework layer of the operating system, such as a system service.

[0334] For example, the aforementioned target module can achieve a one-click image processing function, such as performing initial classification, secondary classification, and image quality assessment on multiple images. The user can perform a first operation on the first control, and the first device, in response to the user's input, executes the initial classification, secondary classification, and image quality assessment processes by calling the aforementioned target module.

[0335] In some embodiments of this application, the execution entities of the above-described image display method are a first device and a server. Specifically, all or part of the steps in the image display method can be implemented through interaction between the target application and the server, or through interaction between the target module and the server.

[0336] In some embodiments of this application, the target module can also be encapsulated as a system plugin during implementation, enabling other project software to quickly perform initial classification, secondary classification, and image quality assessment on multiple images after installing the system plugin. The multiple images can be images from a mobile phone's photo album.

[0337] For example, if the third device does not have a one-click image sorting function and the above-mentioned system plugin is installed, the system plugin has an open plugin interface. The target application in the third device can interact with the system plugin through the plugin interface to realize the one-click image sorting function.

[0338] Specifically, the third device can display a third interface of the target application, on which a first control can be displayed floating. This first control is loaded based on the system plugin. In other words, the system plugin can use the first control to present information and interact with the user. The user can perform a first operation on the first control, and the third device responds to the user's first operation by calling the corresponding plugin interface of the system plugin and running the system plugin, so that the system plugin can perform the initial classification, secondary classification, and image quality assessment processes.

[0339] It should be noted that the target application in the third device can be a system application of the third device or other third-party applications. This application embodiment does not limit this. The system plugin needs to access the target application's image data and needs to interact with the user. The system plugin will initiate a permission request to the target application, and the target application will respond to the permission request by sending the permission result to the system plugin. The permission result is used to indicate whether to grant the user permission to the system plugin. This application embodiment does not specifically limit the permission request process.

[0340] In some embodiments of this application, the execution subject of the above-described image display method is a first device. Of course, the execution subject of the above-described image display method can be either a first device or a second device. The second device can be a trusted device or a server corresponding to the first device. This application does not limit the type of device corresponding to the second device.

[0341] In some examples, the first device can perform a preliminary classification of multiple images to be sorted, and the server performs a secondary classification on the preliminary classification results.

[0342] See Figure 14In this process, the first device can acquire shooting information parameters corresponding to multiple images to be sorted in the target application, and perform preliminary classification on the multiple images to be sorted based on the shooting information parameters to obtain preliminary classification results. For example, the first device can divide the multiple images to be sorted into O image sets, where O is an integer greater than 1. That is, the preliminary classification results include the first set name corresponding to each of the O image sets and the image data in each image set.

[0343] For example, the preliminary classification results include the set name "A" corresponding to image set A and the image data of all images in image set A; the set name "B" corresponding to image set B and the image data of all images in image set B; and the set name "C" corresponding to image set C and the image data of all images in image set C. The image data of the above images may include the image and its corresponding image name.

[0344] In one feasible approach, the shooting information parameters include shooting time and shooting location. A first device can divide multiple images to be processed into O image sets based on preset time, shooting time, and shooting location.

[0345] The first device can send a classification request to the second device, such as a server. The classification request is used to request the server to classify the preliminary classification results. The classification request includes the preliminary classification results.

[0346] The server can receive classification requests and, in response, perform image structure similarity analysis on the preliminary classification results to obtain the final classification result. The final classification result includes the name of the second set corresponding to each of the P image sets and the image name of the image in each image set, where P is an integer greater than 1 and P is greater than or equal to 0.

[0347] For example, the final classification results include the set name "1" corresponding to image set 1 and the image names "image 1, image 2" for all images in image set 1; the set name "2" corresponding to image set 2 and the image names "image 4, image 5" for all images in image set 2; the set name "3" corresponding to image set 3 and the image name "image 6" for all images in image set 3; the set name "4" corresponding to image set 4 and the image name "image 7" for all images in image set 4; and the set name "5" corresponding to image set 5 and the image name "image 8" for all images in image set 5.

[0348] The server sends the final classification result to the first device. The first device receives the final classification result from the server and displays the first interface of the target application based on the final classification result. Specifically, the first device can find the corresponding image and generate at least one image set based on the image name corresponding to the image in each image set in the final classification result. In this way, the first device can subsequently perform image quality assessment on the images in at least one image set, which will not be elaborated further here.

[0349] It should be noted that image data may include image names, which are unique identifiers used to characterize the image.

[0350] Thus, in this embodiment, the first device can perform initial classification, while secondary classification is handled by the server. This reduces the processing burden on the first device. Simultaneously, the server possesses powerful computing capabilities, enabling it to handle a large number of complex computational tasks simultaneously, improving image processing efficiency. This, in turn, enhances the user experience.

[0351] In some examples, the first device can perform preliminary and secondary classification on multiple images to be sorted to obtain at least one image set, and the server can perform image quality assessment on the images in the at least one image set.

[0352] Specifically, see Figure 15 In this process, the first device can send an image quality assessment request to the server, which requests the server to perform an image quality assessment on images in at least one image set. The image quality assessment request includes the name of a third set corresponding to each image set in the at least one image set and the image data in each image set.

[0353] The server can receive image quality assessment requests, calculate the image quality index of each image in at least one image set, and sort the images in the at least one image set according to the image quality index from high to low, obtaining a sorting result. The server sends the sorting result to the first device, and the sorting result includes the name of the third set corresponding to each image set in the at least one image set, the image name corresponding to each image in the image set, and the image quality index.

[0354] The server sends the sorting results to the first device. The first device receives the sorting results and, based on the image name and image quality index corresponding to the images in each image set in the sorting results, determines the cover image corresponding to at least one image set and subsequently displays the second interface.

[0355] In some examples, the first device can perform preliminary classification on multiple images to be sorted, and the server performs secondary classification on the preliminary classification results to obtain at least one image set. Furthermore, the server can also perform image quality assessment on the images in the at least one image set.

[0356] Specifically, the first device can acquire shooting information parameters corresponding to multiple images to be sorted in the target application, and perform preliminary classification of the multiple images to be sorted based on the shooting information parameters to obtain preliminary classification results.

[0357] The first device can send a target request to the server, which requests the server to classify the preliminary classification results and perform an image quality assessment. The classification request includes the aforementioned preliminary classification results.

[0358] The server can receive target requests and, in response, perform image structure similarity analysis on the preliminary classification results to obtain final classification results. The server can also calculate the image quality index of each image in at least one image set in the final classification results, and sort the images in the at least one image set according to their image quality indices from high to low, obtaining a ranking result. Then, the server sends the ranking result to the first device. The ranking result includes the name of the third set corresponding to each image set in the at least one image set, the image name corresponding to each image in the image set, and the image quality index. It should be noted that the preliminary classification, secondary classification, and image quality assessment processes can refer to the above embodiments and will not be repeated here.

[0359] Thus, in this embodiment, the first device can perform initial and secondary classification, while the image quality assessment process is handled by the server. This reduces the processing burden on the first device. Simultaneously, the server possesses powerful computing capabilities, enabling it to handle numerous complex computational tasks simultaneously, thereby improving image processing efficiency. Ultimately, this enhances the user experience.

[0360] It should be noted that the above embodiments are illustrated by the inclusion of at least one image in the third interface. The third interface may also not display images, such as when the first device is a new device and the user has not yet used it to take pictures. Of course, the first device still has the aforementioned image processing function.

[0361] This application also provides an image display method, which can be applied to a first device. The first device can acquire a first image set of a target application. The first device can also display a target interface, which includes multiple images from the first image set, and the order of the multiple images is determined based on a first parameter and a second parameter of each image in the first image set.

[0362] Continuing with the example of a mobile phone as the first device, the phone can access any set of images in the target application, where the set includes multiple images. The phone can determine the order of these multiple images based on the first and second parameters of each image in the set. In other words, the phone can sort the multiple images and display them on the target interface according to the sorting result.

[0363] For example, a mobile phone can set the sorting order of multiple images according to their order of appearance and display them in the target interface according to the sorting order.

[0364] Thus, in this embodiment, the mobile phone can evaluate and sort the image quality of multiple images in an image set, and then display them after determining the corresponding arrangement order. When a user views images, they can see the high-quality images first and obtain the clearest and most accurate information immediately, improving the user experience.

[0365] In some examples, the images in the first image set include human faces. The mobile phone can calculate an image quality index for each image in the first image set based on a first parameter and a second parameter. This image quality index characterizes the image quality of each image in at least one image set. The mobile phone can then sort multiple images in the first image set based on their image quality indices.

[0366] It should be noted that the images in the first image set may not include human faces. The mobile phone can calculate the image quality index of each image in the first image set based on the first parameter of each image. The process of calculating the image quality index described above can be referred to the above embodiment, and will not be repeated here.

[0367] Thus, this embodiment of the application employs the aforementioned first and second parameters to perform multi-dimensional image quality evaluation, thereby gaining a more comprehensive and accurate understanding of the key features in the image. For example, for images of people, it focuses more on facial angles, expressions, and whether there is occlusion, while also combining evaluation from multiple dimensions such as image resolution, clarity, and exposure to better reflect the characteristics of the person in the image. This helps users filter out high-quality image content and improves the user experience.

[0368] This application embodiment also provides a communication system, which includes a first device and a server; the first device and the server establish a communication connection;

[0369] The first device is configured to send a classification request to the server. The classification request is used to request the server to classify the preliminary classification results. The classification request includes the preliminary classification results, which include the name of the first set corresponding to each of the O image sets and the image data in each image set, where O is an integer greater than 1.

[0370] The server is configured to receive classification requests and, in response to the classification requests, perform image structure similarity analysis on the preliminary classification results to obtain the final classification results. The final classification results include the name of the second set corresponding to each of the P image sets and the image name of the image in each image set, where P is an integer greater than 1 and greater than or equal to 0.

[0371] The server is also configured to send the final classification results to the first device.

[0372] The first device is also configured to display a first interface of the target application based on the final classification result. The first interface includes at least one image set, and the image structure similarity between the images in the target image set in the at least one image set satisfies a preset similarity condition.

[0373] The first device is also configured to display a second interface of the target application in response to a trigger operation on the target image set, the second interface including multiple images from the target image set.

[0374] Thus, in the communication system provided in this application embodiment, the first device can perform initial classification, while secondary classification is handled by the server. This reduces the processing burden on the first device. Simultaneously, by leveraging the server's powerful computing capabilities, the first device can quickly process complex tasks, improving image processing efficiency.

[0375] This application embodiment also provides a communication system, which includes a first device and a server; the first device and the server establish a communication connection;

[0376] The first device is configured to send an image quality assessment request to the server, the image quality assessment request being used to request the server to perform an image quality assessment on images in at least one image set.

[0377] The server is configured to receive image quality assessment requests, calculate the image quality index of each image in at least one image set, and sort the images in at least one image set from high to low according to the image quality index to obtain the sorting result.

[0378] The server is configured to send the sorting results to the first device.

[0379] The first device is also configured to display a first interface of the target application based on the sorting result. The first interface includes at least one image set, and the image structure similarity between the images in the target image set in the at least one image set satisfies a preset similarity condition.

[0380] The first device is also configured to display a second interface of the target application in response to a trigger operation on the target image set, the second interface including multiple images from the target image set.

[0381] Thus, in the communication system provided in this embodiment, the first device can perform initial and secondary classification, while the image quality assessment process is handled by the server. Delegating some tasks to the server makes the first device lighter and more energy-efficient. Simultaneously, the server possesses powerful computing capabilities, enabling it to quickly process complex tasks, thereby improving overall processing efficiency.

[0382] In some solutions, multiple embodiments of this application can be combined, and the combined solution can be implemented. Optionally, some operations in the process of each method embodiment may be combined, and / or the order of some operations may be changed. Furthermore, the execution order between the steps of each process is merely exemplary and does not constitute a limitation on the execution order between steps; other execution orders are also possible. It is not intended to indicate that the execution order is the only possible order in which these operations can be performed.

[0383] Those skilled in the art will conceive of various ways to reorder the operations described in the embodiments of this application. Furthermore, it should be noted that process details involved in one embodiment of this application are similarly applicable to other embodiments, or different embodiments can be combined.

[0384] Furthermore, some steps in the method embodiments can be equivalently replaced with other possible steps. Alternatively, some steps in the method embodiments may be optional and can be deleted in certain use cases. Or, other possible steps may be added to the method embodiments.

[0385] Furthermore, the various method embodiments can be implemented individually or in combination.

[0386] This application also provides an electronic device, such as the aforementioned mobile phone, etc. Figure 16 As shown, the mobile phone may include one or more processors 1410, memory 1420 and communication interfaces 1430.

[0387] The memory 1420, communication interface 1430, and processor 1410 are coupled together. For example, the memory 1420, communication interface 1430, and processor 1410 can be coupled together via bus 1440.

[0388] The communication interface 1430 is used for data transmission with other devices. The memory 1420 stores computer program code. The computer program code includes computer instructions, which, when executed by the processor 1410, cause the electronic device to perform the relevant method steps in the embodiments of this application.

[0389] Processor 1410 may be a processor or controller, such as a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with this disclosure. The processor may also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0390] Bus 1440 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The aforementioned bus 1440 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 16 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0391] This application also provides an electronic device, which includes a memory and one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, which includes computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the relevant method steps in the above method embodiments.

[0392] This application also provides a communication device, which includes a memory and one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, which includes computer instructions, and when the computer instructions are executed by the processor, the communication device performs the relevant method steps in the above method embodiments.

[0393] This application also provides a computer-readable storage medium storing computer program code. When the processor executes the computer program code, the electronic device executes the relevant method steps in the above method embodiments.

[0394] This application also provides a computer program product containing instructions that, when executed on a computer or processor, cause the computer or processor to perform the relevant method steps as described in the above method embodiments.

[0395] This application also provides a chip system, including: a processor coupled to a memory, the memory being used to store programs or instructions, and when the program or instructions are executed by the processor, the chip system enables the methods in any of the above method embodiments.

[0396] 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.

[0397] 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.

[0398] For example, the chip system can be a field programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a system-on-chip (SoC), a central processing 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.

[0399] The electronic devices, computer storage media, or computer program products provided in this application are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they can achieve can be referred to the beneficial effects in the corresponding methods provided above, and will not be repeated here.

[0400] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0401] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0402] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units, located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0403] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0404] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the contributing parts, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0405] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An image display method, characterized in that, Applied to a first device, the method includes: The first interface of the target application is displayed. The first interface includes at least one image set, and the image structure similarity between the images in the target image set in the at least one image set satisfies a preset similarity condition. In response to a trigger operation on the target image set, a second interface of the target application is displayed, the second interface including multiple images from the target image set.

2. The method according to claim 1, characterized in that, The method further includes: Display a third interface of the target application, wherein the third interface includes a first control, or the first control is displayed floating on the third interface; The first interface of the target application is displayed, including: In response to a first operation on the first control, the first interface is displayed.

3. The method according to claim 2, characterized in that, The first interface includes a second control, or the second control is displayed floating on the first interface; The method further includes: In response to a second operation on the second control, the first interface is switched to the third interface.

4. The method according to any one of claims 1-3, characterized in that, The shooting information parameters between images in the target image set meet preset conditions, and the shooting information parameters include at least one of shooting time, shooting location, and shooting object type.

5. The method according to claim 4, characterized in that, The shooting information parameters meet the preset conditions, including the shooting time meeting the preset time requirement and / or the shooting location being the same.

6. The method according to claim 4 or 5, characterized in that, The method further includes: Obtain shooting information parameters corresponding to multiple images to be processed in the target application, including shooting time and shooting location; Based on the preset conditions and the shooting information parameters, the multiple images to be processed are divided into O image sets, where O is an integer greater than 1; Image structure similarity analysis is performed on the images in any image set among the O image sets. Multiple images in the image set whose image structure similarity meets the preset similarity conditions are divided into one image set to obtain at least one image set in the first interface.

7. The method according to any one of claims 1-6, characterized in that, In the first interface, the image with the highest image quality index in the target image set is displayed as the cover image of the target image set, whereby the image quality index is used to characterize image quality.

8. The method according to any one of claims 1-7, characterized in that, In the second interface, multiple images in the target image set are displayed in descending order of image quality index, which is used to characterize image quality.

9. The method according to any one of claims 1-8, characterized in that, The second interface also includes a first prompt icon, which is used to prompt sharing of a first image. The first image is one of the top M images in the second interface with the highest image quality index, where M is an integer greater than or equal to 1. And / or, the second interface also includes a second prompt icon, which is used to prompt deletion of a second image. The second image is one of the bottom N images in the second interface with the lowest image quality index, where N is an integer greater than or equal to 1. The image quality index is used to characterize image quality.

10. The method according to any one of claims 1-9, characterized in that, The second interface further includes a first display area and / or a second display area. The first display area is used to display a first image to be shared, which is one of the top M images in the second interface with the highest image quality index, where M is an integer greater than or equal to 1. And / or, the second display area is used to display a second image to be deleted, which is one of the bottom N images in the second interface with the lowest image quality index, where N is an integer greater than or equal to 1. The image quality index is used to characterize image quality.

11. The method according to any one of claims 7-10, characterized in that, The image quality index is determined based on a first parameter and / or a second parameter; The first parameter includes at least one of resolution, exposure, sharpness, noise, contrast and color saturation, and the second parameter includes at least one of face angle parameter, face expression parameter, face occlusion parameter, face skin color parameter, face makeup parameter and face texture parameter.

12. The method according to claim 11, characterized in that, The at least one image set includes a first image set, wherein the images in the first image set include human faces; the method further includes: Obtain the first and second parameters for each image in the first image set; Calculate the image quality index for each image in the first image set based on the first parameter and the second parameter of each image in the first image set.

13. The method according to claim 12, characterized in that, The step of calculating the image quality index for each image in the first image set based on the first parameter and the second parameter of each image in the first image set includes: Using the weights of the first parameter and the second parameter, the image quality index of each image in the first image set is calculated, wherein the weight of any one of the first parameters is greater than the weight of any one of the second parameters.

14. An image display method, characterized in that, Applied to a first device, the method includes: Obtain the first image set for the target application; Display target interface, the target interface including multiple images of the first image set, the arrangement order of the multiple images being determined based on a first parameter and a second parameter of each image in the first image set; The first parameter includes at least one of resolution, exposure, sharpness, noise, contrast and color saturation, and the second parameter includes at least one of face angle parameter, face expression parameter, face occlusion parameter, face skin color parameter, face makeup parameter and face texture parameter.

15. The method according to claim 14, characterized in that, The images in the first image set include human faces, and the method further includes: Based on the first parameter and the second parameter of each image in the first image set, calculate the image quality index of each image in the first image set, and the image quality index is used to characterize the image quality; The images in the first image set are sorted according to the image quality index.

16. The method according to claim 14 or 15, characterized in that, The step of calculating the image quality index for each image in the first image set based on the first parameter and the second parameter of each image in the first image set includes: Using the weights of the first parameter and the second parameter, the image quality index of each image in the first image set is calculated, wherein the weight of any one of the first parameters is greater than the weight of any one of the second parameters.

17. An electronic device, characterized in that, The electronic device includes a display screen, a memory, and one or more processors; the display screen is used to display a user interface of a target application, and the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, which, when executed by the processor, cause the electronic device to perform the image display method as described in any one of claims 1-13, or to perform the image display method as described in any one of claims 14-16.

18. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform the image display method as described in any one of claims 1-13, or the image display method as described in any one of claims 14-16.

19. A computer program product, characterized in that, The computer program product includes instructions that, when executed by an electronic device, cause the electronic device to perform the image display method as described in any one of claims 1-13, or to perform the image display method as described in any one of claims 14-16.

Images