Web page image processing method, web page image loading method and device

Abstract

The present disclosure provides a web page image processing method, a web page image loading method and a device, which relate to the field of computer technology, and in particular to the field of image processing technology. The implementation scheme is: judging whether the image to be processed is blurred, wherein the image to be processed is any image in the web page; adjusting the resolution of the image to be processed based on the result of the judgment to obtain an optimized image corresponding to the image to be processed; and storing the address of the image to be processed in association with the optimized image.

Description

Technical Field

[0001] The present disclosure relates to the field of computer technology, in particular to the field of image processing technology, and specifically to a web page image processing method and device, a web page image loading method and device, an electronic device, a computer-readable storage medium, and a computer program product. Background Art

[0002] Users are used to obtaining information through the Internet. The Internet includes a large number of web pages. Images, as a vivid and efficient information carrier, are widely present in various web pages.

[0003] The methods described in this section are not necessarily methods that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any method described in this section is considered to be prior art simply because it is included in this section. Similarly, unless otherwise indicated, the issues mentioned in this section should not be considered to have been recognized in any prior art. Summary of the invention

[0004] The present disclosure provides a web page image processing method and device, a web page image loading method and device, an electronic device, a computer-readable storage medium, and a computer program product.

[0005] According to one aspect of the present disclosure, a web page image processing method is provided, comprising: determining whether an image to be processed is blurred, wherein the image to be processed is any image in a web page; based on a result of the determination, adjusting the resolution of the image to be processed to obtain an optimized image corresponding to the image to be processed; and storing the address of the image to be processed in association with the optimized image.

[0006] According to one aspect of the present disclosure, a web page image loading method is provided, comprising: sending a query request to a first server, the query request being used to query optimization status information of a web page to be loaded; determining a first image with an optimized image and a second image without an optimized image in the web page to be loaded based on the optimization status information returned by the first server; obtaining an optimized image of the first image from the first server; obtaining the second image from a second server where the second image is located; and loading the optimized image and the second image.

[0007] According to one aspect of the present disclosure, a method for loading a web page image is provided, comprising: sending an image acquisition request to a server, the image acquisition request being used to acquire an image in a web page to be loaded; receiving a target image returned by the server, wherein the target image is the optimized image if the image has an optimized image, and the target image is the image if the image does not have an optimized image; and loading the target image.

[0008] According to one aspect of the present disclosure, a web page image processing device is provided, comprising: a judgment module, configured to judge whether an image to be processed is blurred, wherein the image to be processed is any image in a web page; an optimization module, configured to adjust the resolution of the image to be processed based on a result of the judgment to obtain an optimized image corresponding to the image to be processed; and a storage module, configured to store the address of the image to be processed in association with the optimized image.

[0009] According to one aspect of the present disclosure, a web page image loading device is provided, comprising: a query module, configured to send a query request to a first server, the query request being used to query optimization status information of a web page to be loaded; a determination module, configured to determine a first image with an optimized image and a second image without an optimized image in the web page to be loaded based on the optimization status information returned by the first server; a first acquisition module, configured to acquire an optimized image of the first image from the first server; a second acquisition module, configured to acquire the second image from a second server where the second image is located; and a loading module, configured to load the optimized image and the second image.

[0010] According to one aspect of the present disclosure, a web page image loading device is provided, comprising: a request module, configured to send an image acquisition request to a first server, wherein the image acquisition request is used to acquire an image in a web page to be loaded; a receiving module, configured to receive a target image returned by the first server, wherein the target image is the optimized image if the image has an optimized image, and the target image is the image if the image does not have an optimized image; and a loading module, configured to load the target image.

[0011] According to one aspect of the present disclosure, there is provided an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor, the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor so that the at least one processor can execute any of the methods of the above aspects.

[0012] According to one aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0013] According to one aspect of the present disclosure, a computer program product is provided, including a computer program, which implements any of the above methods when executed by a processor.

[0014] According to one or more embodiments of the present disclosure, the image can be adjusted to a suitable resolution and stored based on the blur judgment result of the web page image, thereby realizing automatic adjustment of the web page image quality and achieving a balance between image quality and storage efficiency.

[0015] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings exemplarily illustrate the embodiments and constitute a part of the specification, and together with the text description of the specification, are used to explain the exemplary implementation of the embodiments. The embodiments shown are for illustrative purposes only and do not limit the scope of the claims. In all drawings, the same reference numerals refer to similar but not necessarily identical elements.

[0017] Figure 1 A schematic diagram showing an exemplary system in which various methods described herein may be implemented according to an embodiment of the present disclosure;

[0018] Figure 2 A flowchart of a web page image processing method according to an embodiment of the present disclosure is shown;

[0019] Figure 3 A flow chart of a web page image loading method according to an embodiment of the present disclosure is shown;

[0020] Figure 4 A flowchart of a web page image loading method according to other disclosed embodiments is shown;

[0021] Figure 5 A structural block diagram of a web page image processing device according to an embodiment of the present disclosure is shown;

[0022] Figure 6 A structural block diagram of a web page image loading device according to an embodiment of the present disclosure is shown;

[0023] Figure 7 A structural block diagram of a web page image loading device according to some other embodiments of the present disclosure is shown; and

[0024] Figure 8 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0025] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0026] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0027] The terms used in the description of various examples in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. In addition, the term "and / or" used in this disclosure covers any one of the listed items and all possible combinations.

[0028] There are a large number of web pages containing images on the Internet. In the related art, in order to save server storage resources and network transmission bandwidth, the images in the web pages are usually compressed (for example, reducing the resolution, deleting redundant information in the images, etc.) and stored.

[0029] However, the quality of images on different web pages varies, and some images are blurry and of poor quality. After compression, the image quality will be further reduced, making it even more difficult for users to see the image content clearly.

[0030] In response to the above problems, the embodiments of the present disclosure provide a web page image processing method, which can adjust the image to a suitable resolution and store it based on the blur judgment result of the web page image, thereby realizing automatic adjustment of the web page image quality and achieving a balance between image quality and storage efficiency.

[0031] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0032] Figure 1 FIG. 1 is a schematic diagram of an exemplary system 100 in which various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. Figure 1, the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 may be configured to execute one or more applications.

[0033] In an embodiment of the present disclosure, the server 120 may run one or more services or software applications that enable the execution of the web page image processing method and / or the web page image loading method.

[0034] In some embodiments, server 120 may also provide other services or software applications that may include non-virtualized environments and virtualized environments. In some embodiments, these services may be provided as web-based services or cloud services, such as provided to users of client devices 101, 102, 103, 104, 105, and / or 106 under a software as a service (SaaS) model.

[0035] exist Figure 1 In the configuration shown, the server 120 may include one or more components that implement the functions performed by the server 120. These components may include software components, hardware components, or a combination thereof that can be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 may in turn utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from the system 100. Therefore, Figure 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

[0036] The user may use client devices 101, 102, 103, 104, 105, and / or 106 to navigate. The client device may provide an interface that enables the user of the client device to interact with the client device. The client device may also output information to the user via the interface. Figure 1 Only six client devices are depicted, but one skilled in the art will appreciate that the present disclosure may support any number of client devices.

[0037] Client devices 101, 102, 103, 104, 105 and / or 106 may include various types of computer devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptop computers), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, game systems, thin clients, various messaging devices, sensors or other sensing devices, etc. These computer devices may run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, Linux or Linux-like operating systems (such as GOOGLE Chrome OS); or include various mobile operating systems, such as MICROSOFT Windows Mobile OS, iOS, Windows Phone, Android. Portable handheld devices may include cellular phones, smart phones, tablet computers, personal digital assistants (PDAs), etc. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Game systems may include various handheld game devices, Internet-enabled game devices, etc. Client devices are capable of executing various different applications, such as various Internet-related applications, communication applications (such as email applications), short message service (SMS) applications, and may use various communication protocols.

[0038] The network 110 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP / IP, SNA, IPX, etc. By way of example only, the one or more networks 110 may be a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a public switched telephone network (PSTN), an infrared network, a wireless network (e.g., Bluetooth, Wi-Fi), and / or any combination of these and / or other networks.

[0039] Server 120 may include one or more general purpose computers, dedicated server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain a server's virtual storage device). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0040] The computing units in the server 120 may run one or more operating systems including any of the above operating systems and any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and / or middle-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, etc.

[0041] In some implementations, server 120 may include one or more applications to analyze and consolidate data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105, and 106. Server 120 may also include one or more applications to display data feeds and / or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and 106.

[0042] In some embodiments, the server 120 may be a server of a distributed system, or a server combined with a blockchain. The server 120 may also be a cloud server, or an intelligent cloud computing server or intelligent cloud host with artificial intelligence technology. A cloud server is a host product in a cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and virtual private servers (VPS) services.

[0043] The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as music files. The databases 130 may reside in various locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The databases 130 may be of different types. In some embodiments, the databases used by the server 120 may be, for example, relational databases. One or more of these databases may store, update, and retrieve data to and from the databases in response to commands.

[0044] In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the applications may be different types of databases, such as a key-value store, an object store, or a conventional store backed by a file system.

[0045] Figure 1The system 100 may be configured and operated in various ways to enable the application of various methods and apparatuses described in the present disclosure.

[0046] For the purpose of the embodiments of this disclosure, Figure 1 In the example, the client devices 101, 102, 103, 104, 105 and 106 may include a client application for browsing web pages, and the client application may be, for example, a browser, or an application including a WebView web page view control, etc.

[0047] The user can access the search engine website in the client application, and accordingly, the server 120 can be a server corresponding to the search engine website. The server 120 may include a service program, which can provide web search and browsing services to the user based on the web resources (including HTML files, images, audio, video, etc.) stored in the database 130. For example, the user can initiate a search request in the search engine website to search for web pages containing specific information (usually, the user can enter a search term in the search engine website to search for web pages containing the search term). Accordingly, the server 120 performs a web search in response to the search request, obtains web pages containing the specific information that the user wants to search, and returns these web pages to the client application to present them to the user.

[0048] The server 120 can execute the web page image processing method of the embodiment of the present disclosure, optimize the images in the captured web pages, and store the optimized images to achieve a balance between image quality and storage efficiency. The client devices 101, 102, 103, 104, 105 and 106 can execute the web page image loading method of the embodiment of the present disclosure, and when accessing a web page, they can obtain the optimized images in the web page from the server 120, so that users can obtain high-quality web page images and improve the user's web browsing experience.

[0049] Figure 2 FIG. 2 is a flow chart of a web page image processing method 200 according to an embodiment of the present disclosure. As described above, the method 200 is usually performed on a server (e.g. Figure 2 The execution of each step of the method 200 may be performed at the server 120 shown in FIG. Figure 2 The server 120 shown in .

[0050] like Figure 2 As shown, the method 200 includes:

[0051] Step 210, determining whether the image to be processed is blurred, the image to be processed being any image in the web page;

[0052] Step 220: Based on the result of the above determination, the resolution of the image to be processed is adjusted to obtain an optimized image corresponding to the image to be processed; and

[0053] Step 230: Store the address of the image to be processed in association with the optimized image.

[0054] According to the embodiments of the present disclosure, by determining whether an image in a web page is blurred and adjusting the image to a suitable resolution for storage based on the determination result, automatic adjustment of web page image quality is achieved, thereby achieving a balance between image quality and storage efficiency.

[0055] Each step of method 200 is described in detail below.

[0056] According to some embodiments, a server (eg, a search engine server) may crawl web pages on the Internet regularly or irregularly. Accordingly, the web page in step 210 may be any web page crawled by the server.

[0057] The image to be processed in step 210 may be any image in a web page. Specifically, the image to be processed may be every image in a web page, that is, each image in a web page is used as an image to be processed and processed by method 200 .

[0058] Considering that a web page may include some low-resolution (e.g., less than 64*64) images such as icons and design materials, these images contain less information and are usually not images of interest to users, so there is no need to use method 200 to process these images. Accordingly, according to some embodiments, the images to be processed may be images in the web page whose resolution is greater than or equal to the first threshold, that is, only images in the web page whose resolution is greater than or equal to the first threshold are processed, and images in the web page whose resolution is less than the first threshold are not processed. As a result, low-resolution images such as icons and design materials in the web page can be filtered out, the number of images to be processed is reduced, and image processing efficiency is improved.

[0059] According to some embodiments, a neural network model (e.g., a convolutional neural network) may be used to determine whether the image to be processed is blurred in step 210. Specifically, the image to be processed may be input into the neural network model, which extracts feature information of the image to be processed and outputs a determination result of whether the image to be processed is blurred.

[0060] According to some other embodiments, edge detection can be used to determine whether the image to be processed is blurred. For example, edge detection can be performed on the image to be processed (for example, by taking the second-order derivative of the image to be processed to obtain the edge therein), and the total number of edge points of the image to be processed is obtained. If the total number of edge points is less than a certain threshold, the image to be processed is determined to be blurred. For another example, edge detection can be used to obtain edge points in the image to be processed, and then the variance of the pixel values ​​of each edge point is calculated. If the variance is less than a certain threshold, the image to be processed is determined to be blurred.

[0061] In step 220, based on different blur judgment results, different resolution adjustment processes may be performed on the object to be processed.

[0062] According to some embodiments, in step 220, in response to determining that the image to be processed is blurred, the resolution of the image to be processed is increased. That is, super-resolution processing is performed on the image to be processed. Image blur is usually caused by too little information contained in the image (low resolution). By increasing the image resolution, the image quality can be optimized and the image can be made clearer, thereby facilitating the user to obtain information of interest from the image. Specifically, super-resolution processing can be achieved through deep learning models, interpolation algorithms, and the like.

[0063] According to some embodiments, improving the resolution of an image to be processed includes: performing super-resolution processing on the image to be processed at least once according to a preset magnification step size to obtain at least one intermediate image, and extracting feature information of each intermediate image; for each super-resolution processing, matching the first feature information of the intermediate image obtained by this super-resolution processing with the second feature information of the intermediate image obtained by the previous super-resolution processing, wherein the intermediate image obtained by the previous super-resolution processing of the first super-resolution processing is the image to be processed; and in response to determining that the first feature information matches the second feature information, using the intermediate image obtained by the previous super-resolution processing as the optimized image.

[0064] According to the above embodiment, by matching the feature information of the intermediate image obtained by two adjacent super-resolution processes (i.e., the first feature information and the second feature information), the resolution of the image to be processed can be automatically magnified to an appropriate multiple, i.e., the minimum multiple that makes the image clear, thereby improving image quality while saving storage resources.

[0065] The magnification step is used to indicate the interval between the magnifications of two super-resolution processes. For example, if the magnification step is 1, it indicates that the interval (difference) between the magnifications of two super-resolution processes is 1, that is, the target area is subjected to super-resolution processing in the order of 2 times, 3 times, 4 times, ... For another example, if the magnification step is 2, it indicates that the interval (difference) between the magnifications of two super-resolution processes is 2, that is, the target area is subjected to super-resolution processing in the order of 2 times, 4 times, 8 times, ...

[0066] It is understood that the resolution is usually expressed by the product of the number of pixels in the horizontal and vertical directions. It should be noted that in the embodiments of the present disclosure, the magnification of the resolution refers to the magnification of the number of pixels in a single direction. For example, the magnification of the resolution of image p (resolution c×d) is 2 times, which means that the number of pixels in the horizontal direction and the number of pixels in the vertical direction of image a are magnified to twice the original image, that is, the resolution of the enlarged image p' is 2c×2d.

[0067] According to some embodiments, the feature information includes category information and first position information of the object, as well as content, second position information and confidence information of the text block. Accordingly, extracting the feature information of each intermediate image includes: performing object recognition on the intermediate image to determine the category information and first position information of at least one object included in the intermediate image; and performing optical character recognition (OCR) on the intermediate image to determine the content, second position information and confidence information of at least one text block included in the intermediate image. The first position information and the second position information can be represented by a bounding box (bbox for short).

[0068] It can be understood that as the magnification continues to increase, the blurred content in the image to be processed will gradually become clear, so that the number of recognized objects and text blocks increases, and the confidence of the text blocks increases. For example, the image to be processed includes a person and a cup, but because the image to be processed is relatively blurry, the object recognition of the image to be processed may only recognize the person, but not the cup; and the confidence of the text block recognition is low, which may be only 40%. By performing super-resolution processing on the image to be processed, a clearer intermediate image can be obtained, and object recognition is performed on the intermediate image, and both the person and the cup are recognized, and the confidence of the text block recognition is improved, for example, to 90%.

[0069] Therefore, the above information of the object and text block can be used as feature information for determining the magnification. When the feature information of the intermediate images obtained by two adjacent super-resolution processes is roughly the same (matches), the intermediate image is considered to be clear and the magnification is stopped.

[0070] When the feature information (i.e., the first feature information and the second feature information) of the intermediate images obtained by two adjacent super-resolution processes match, it is considered that both intermediate images are clear, and the super-resolution process of the image to be processed is stopped. In this case, the intermediate image with lower resolution among the two intermediate images, i.e., the intermediate image obtained by the previous super-resolution process, is used as the optimized image, thereby improving the display quality of the image to be processed while saving storage resources.

[0071] Specifically, according to some embodiments, the determination condition that the first feature information matches the second feature information may include:

[0072] Condition 1: The category information of the objects is the same;

[0073] Condition 2: the similarity of the first position information is greater than a certain threshold (for example, the intersection and union ratio of the two rectangular boxes is greater than a certain threshold, such as 90%);

[0074] Condition 3: The text content included in the text block is the same;

[0075] Condition 4: the similarity of the second position information is greater than a certain threshold (for example, the intersection and union ratio of the two rectangular frames is greater than a certain threshold, such as 90%);

[0076] Condition 5: The values ​​of the confidence information of the text blocks are all greater than a certain threshold (for example, 80%), and the similarity between the two confidence information is greater than a certain threshold (for example, 90%).

[0077] According to some embodiments, when all of the above conditions 1-5 are met, it is determined that the first feature information matches the second feature information.

[0078] According to some other embodiments, it may also be arranged that when some of the above conditions 1-5 are met, it is determined that the first characteristic information matches the second characteristic information.

[0079] According to some embodiments, a maximum magnification may also be preset. When the feature information of the intermediate images obtained by two adjacent super-resolution processes do not match, but the magnification of the last super-resolution process has reached the maximum magnification, the super-resolution process of the image to be processed is stopped, and the intermediate image obtained by the last super-resolution process is used as the optimized image.

[0080] According to some embodiments, the method 200 further includes: in response to determining that the image to be processed is blurred and the resolution is greater than a first threshold, performing enhancement processing on the image to be processed without changing the resolution, the enhancement processing including at least one of adjusting brightness, adjusting contrast, improving sharpness, and removing noise points. The first threshold can be set to, for example, a multiple of the screen resolution of a general-purpose mobile device (e.g., 1x, 2x).

[0081] Generally, the screen size of a mobile device is limited, and an image resolution that is too large may be redundant for the mobile device, and the user may not need to see the various details of a large-resolution image. And in some cases (for example, the image to be processed itself is a blurred abstract painting), there is no need to increase the resolution of the image to be processed. Based on the above embodiment, when the resolution of the image to be processed is already large (greater than the first threshold), its resolution is no longer increased, but the display quality of the image is optimized from other aspects such as brightness and contrast, so as to balance the image quality and storage resource consumption.

[0082] According to some embodiments, in step 220, in response to determining that the image to be processed is not blurred, the resolution of the image to be processed is reduced. The image to be processed is not blurred (clear), indicating that the quality of the image to be processed can meet the viewing needs of the user. In this case, by reducing the image resolution, the storage space occupied can be further reduced while ensuring the image quality, saving storage resources.

[0083] According to some embodiments, reducing the resolution of an image to be processed includes: performing at least one reduction process on the resolution of the image to be processed according to a preset reduction ratio step to obtain at least one intermediate image, and extracting feature information of each intermediate image; for each reduction process, matching the first feature information of the intermediate image obtained by this reduction process with the second feature information of the intermediate image obtained by the previous reduction process, and the intermediate image obtained by the previous reduction process of the first reduction process is the image to be processed; and in response to determining that the first feature information does not match the second feature information, determining an optimized image based on the intermediate image obtained by the previous reduction process.

[0084] According to the above embodiment, by matching the feature information of the intermediate image obtained by two adjacent reduction processes (i.e., the first feature information and the second feature information), the resolution of the image to be processed can be automatically reduced to an appropriate ratio, thereby reducing the storage space occupied while ensuring the image quality.

[0085] The specific content and extraction steps of the feature information can be found in the super-resolution processing steps above and will not be repeated here.

[0086] The determination conditions for the mismatch between the first characteristic information and the second characteristic information may include:

[0087] Condition 1: The category information of the object is different;

[0088] Condition 2: the similarity of the first position information is lower than a certain threshold (for example, the intersection-and-union ratio of the two rectangular frames is greater than a certain threshold, such as 90%);

[0089] Condition 3: The text content included in the text block is different;

[0090] Condition 4: the similarity of the second position information is lower than a certain threshold (for example, the intersection-and-union ratio of the two rectangular frames is greater than a certain threshold, for example, 90%);

[0091] Condition 5: the value of the confidence information of at least one text block is lower than a certain threshold (e.g., 80%);

[0092] Condition 6: The similarity of the confidence information of the text block is lower than a certain threshold (eg, 90%).

[0093] According to some embodiments, when one or more of the above conditions 1-6 are met, it is determined that the first location information does not match the second location information.

[0094] When the feature information (i.e., the first feature information and the second feature information) of the intermediate image obtained by two adjacent reduction processes do not match, the reduction process of the image to be processed is stopped. It can be understood that in this case, the reduction ratio of the last reduction process is the minimum reduction ratio that can ensure the image quality.

[0095] According to some embodiments, determining the optimized image based on the intermediate image obtained by the last reduction process includes: enlarging the resolution of the intermediate image obtained by the last reduction process according to a preset magnification factor, and using the enlarged image as the optimized image.

[0096] It can be understood that the reduction ratio of the last reduction process is the minimum reduction ratio that can ensure the image quality, but the minimum granularity of the image content that can be recognized by the computer is larger than that of the human eye. For an image determined by the computer to be clear, it may still appear blurry to the human eye. According to the above embodiment, by setting the optimized image to be slightly larger than the intermediate image obtained by the last reduction process (multiplied by the magnification factor), the image conforms to the visual habits of the human eye and ensures the image display effect.

[0097] According to some embodiments, the method 200 further includes: in response to determining that the image to be processed is not blurred and the resolution is less than a preset threshold, compressing the image to be processed without changing the resolution. Based on the above embodiment, when the resolution of the image to be processed is already small (less than the preset threshold), the resolution is no longer reduced (to avoid blurring the image to be processed), and other compression processing algorithms (such as removing redundant information in the image) are used to compress the image to be processed, thereby saving storage space.

[0098] After obtaining the optimized image corresponding to the object to be processed through step 220, step 230 is executed to store the address of the image to be processed in association with the optimized image.

[0099] According to some embodiments, step 230 further includes: calculating a first ratio of the larger and smaller of the resolution of the optimized image and the resolution of the image to be processed; and in response to determining that the first ratio is greater than or equal to a second threshold, storing the address of the image to be processed in association with the optimized image.

[0100] The first ratio may represent the difference in resolution between the optimized image and the image to be processed. If the difference in resolution between the two is large, the gain of adjusting the resolution on image quality or storage efficiency is large, and it is necessary to store the optimized image; conversely, if the difference in resolution between the two is small, the gain of adjusting the resolution on image quality or storage efficiency is small, and the optimized image may be discarded to further save storage resources.

[0101] According to some embodiments, step 230 further includes: calculating a second ratio of the number of images in the web page having corresponding optimized images to the total number of images included in the web page; and in response to determining that the second ratio is greater than a third threshold, storing the address of the image to be processed in association with the optimized image.

[0102] The second ratio may indicate the degree of optimization of the web page. If the second ratio is large, it is considered that the optimized image has a large gain on the web page quality or storage efficiency, and therefore it is necessary to store the optimized image; conversely, if the second ratio is small, it is considered that the optimized image has a small gain on the web page quality or storage efficiency, and the optimized image may be discarded to further save storage resources.

[0103] According to some embodiments, method 200 further includes: storing the address of the webpage in association with optimization status information of the webpage, wherein the optimization status information is used to indicate an image with an optimized image in the webpage. Thus, when the user accesses the webpage, a high-quality optimized image can be provided to the user, and the consumption of network bandwidth resources can be reduced.

[0104] According to an embodiment of the present disclosure, a web page image loading method is also provided. Figure 3FIG. 3 is a flowchart of a web page image loading method 300 according to an embodiment of the present disclosure. The method 300 is generally performed on a client device (eg, Figure 2 The execution of each step of the method 300 may be performed at the client device 101-106 shown in FIG. Figure 2 Client devices 101-106 are shown in FIG.

[0105] like Figure 3 As shown, the method 300 includes:

[0106] Step 310: Send a query request to the first server, where the query request is used to query optimization status information of the web page to be loaded;

[0107] Step 320: Determine a first image with an optimized image and a second image without an optimized image in the webpage to be loaded based on the optimization status information returned by the first server;

[0108] Step 330: Obtain an optimized image of the first image from the first server;

[0109] Step 340: Acquire the second image from the second server where the second image is located; and

[0110] Step 350: Load the optimized image and the second image.

[0111] According to the embodiments of the present disclosure, users can obtain high-quality web page images, and the network bandwidth resource consumption when transmitting web page images is reduced, thereby improving the user's web browsing experience.

[0112] According to some embodiments, the first server in the method 300 may be a server for executing the aforementioned method 200 .

[0113] According to an embodiment of the present disclosure, another web page image loading method is also provided. Figure 4 FIG. 4 is a flowchart of a web page image loading method 400 according to an embodiment of the present disclosure. The method 400 is generally performed on a client device (eg, Figure 2 The execution of each step of the method 400 may be performed at the client device 101-106 shown in FIG. Figure 2 Client devices 101-106 are shown in FIG.

[0114] like Figure 4 As shown, the method 400 includes:

[0115] Step 410: Send an image acquisition request to the server, where the image acquisition request is used to acquire an image in the web page to be loaded;

[0116] Step 420: Receive a target image returned by the server, wherein the target image is the optimized image if the image has an optimized image, and the target image is the image if the image does not have an optimized image; and

[0117] Step 430: Load the target image.

[0118] According to the embodiments of the present disclosure, users can obtain high-quality web page images, and the network bandwidth resource consumption when transmitting web page images is reduced, thereby improving the user's web browsing experience.

[0119] According to some embodiments, the server in method 400 may be a server for executing the aforementioned method 200 .

[0120] According to some embodiments, the server of method 400 stores the original image and the optimized image of each web page image. Figure 4 As shown, the server can perform step 440A to search for the image currently requested by the user and determine whether the image has an optimized image. If so, the optimized image is obtained from the local computer as the target image; if not, the image (original image) is obtained from the local computer as the target image.

[0121] According to some other embodiments, the server of method 400 only stores the optimized images of each web page image, but does not store the original images of each web page image. Figure 4 As shown, the server can perform step 440A to search for the image requested by the current user and determine whether the image has an optimized image. If so, the optimized image is obtained locally as the target image. If not, step 440B is further performed to request the second server where the image is located to obtain the image and use the image as the target image.

[0122] According to an embodiment of the present disclosure, a web page image processing device is also provided. Figure 5 FIG. 5 shows a structural block diagram of a web page image processing apparatus 500 according to an embodiment of the present disclosure. Figure 5 As shown, the device 500 includes:

[0123] The determination module 510 is configured to determine whether the image to be processed is blurred, wherein the image to be processed is any image in the web page;

[0124] an optimization module 520, configured to adjust the resolution of the image to be processed based on the result of the judgment, so as to obtain an optimized image corresponding to the image to be processed; and

[0125] The storage module 530 is configured to store the address of the image to be processed in association with the optimized image.

[0126] According to the embodiments of the present disclosure, by determining whether an image in a web page is blurred and adjusting the image to a suitable resolution for storage based on the determination result, automatic adjustment of web page image quality is achieved, thereby achieving a balance between image quality and storage efficiency.

[0127] According to an embodiment of the present disclosure, a web page image loading device is also provided. Figure 6 FIG. 6 shows a structural block diagram of a web page image loading device 600 according to an embodiment of the present disclosure. Figure 6 As shown, the device 600 includes:

[0128] A query module 610 is configured to send a query request to the first server, wherein the query request is used to query the optimization status information of the web page to be loaded;

[0129] A determination module 620 is configured to determine, based on the optimization status information returned by the first server, a first image with an optimized image and a second image without an optimized image in the webpage to be loaded;

[0130] A first acquisition module 630 is configured to acquire an optimized image of the first image from the first server;

[0131] A second acquisition module 640 is configured to acquire the second image from a second server where the second image is located; and

[0132] The loading module 650 is configured to load the optimized image and the second image.

[0133] According to the embodiments of the present disclosure, users can obtain high-quality web page images, and the network bandwidth resource consumption when transmitting web page images is reduced, thereby improving the user's web browsing experience.

[0134] According to an embodiment of the present disclosure, another web page image loading device is also provided. Figure 7 FIG. 7 shows a structural block diagram of a web page image loading device 700 according to some other embodiments of the present disclosure. Figure 7 As shown, the apparatus 700 includes:

[0135] The request module 710 is configured to send an image acquisition request to the first server, wherein the image acquisition request is used to acquire an image in the web page to be loaded;

[0136] A receiving module 720 is configured to receive a target image returned by the first server, wherein the target image is the optimized image if the image has an optimized image, and the target image is the image if the image does not have an optimized image; and

[0137] The loading module 730 is configured to load the target image.

[0138] According to the embodiments of the present disclosure, users can obtain high-quality web page images, and the network bandwidth resource consumption when transmitting web page images is reduced, thereby improving the user's web browsing experience.

[0139] It should be understood that Figure 5-Figure 7 The modules or units of the apparatus 500-700 shown in FIG. 1 can be respectively connected with the reference Figure 2-Figure 4 The steps in the described methods 200-400 correspond to each other. Therefore, the operations, features and advantages described above for the methods 200-400 are also applicable to the devices 500-700 and the modules and units included therein. For the sake of brevity, some operations, features and advantages are not repeated here.

[0140] Although specific functions are discussed above with reference to specific modules, it should be noted that the functions of the various modules discussed herein may be divided into multiple modules, and / or at least some functions of multiple modules may be combined into a single module. For example, the first acquisition module 630 and the second acquisition module 640 described above may be combined into a single module in some embodiments.

[0141] It should also be understood that various techniques may be described herein in the general context of software hardware elements or program modules. Figure 5-Figure 7 The various modules described can be implemented in hardware or in hardware in combination with software and / or firmware. For example, these modules can be implemented as computer program codes / instructions, which are configured to be executed in one or more processors and stored in non-transient computer-readable storage media. Alternatively, these modules can be implemented as hardware logic / circuits. For example, in some embodiments, one or more of modules 510-730 can be implemented together in a system on chip (System on Chip, SoC). SoC may include an integrated circuit chip (which includes a processor (e.g., a central processing unit (CPU), a microcontroller, a microprocessor, a digital signal processor (DSP), etc.), a memory, one or more communication interfaces, and / or one or more components in other circuits), and may optionally execute the received program code and / or include embedded firmware to perform functions.

[0142] According to an embodiment of the present disclosure, an electronic device is also provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor, the memory storing instructions executable by the at least one processor, the instructions being executed by the at least one processor so that the at least one processor can execute method 200, 300 or 400 of the embodiment of the present disclosure.

[0143] According to an embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided. The computer instructions are used to enable a computer to execute the method 200, 300 or 400 of the embodiment of the present disclosure.

[0144] According to an embodiment of the present disclosure, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the method 200, 300 or 400 of the embodiment of the present disclosure is implemented.

[0145] refer to Figure 8 , a block diagram of an electronic device 800 that can be used as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0146] like Figure 8 As shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0147] Multiple components in the electronic device 800 are connected to the I / O interface 805, including: an input unit 806, an output unit 807, a storage unit 808 and a communication unit 809. The input unit 806 can be any type of device that can input information to the device 800. The input unit 806 can receive input digital or character information, and generate key signal input related to user settings and / or function control of the electronic device, and can include but is not limited to a mouse, a keyboard, a touch screen, a trackpad, a trackball, a joystick, a microphone and / or a remote control. The output unit 807 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator and / or a printer. The storage unit 808 can include but is not limited to a disk, an optical disk. The communication unit 809 allows the device 800 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver and / or a chipset, such as Bluetooth TM devices, 802.11 devices, Wi-Fi devices, WiMAX devices, cellular communication devices, and / or the like.

[0148] The computing unit 801 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 801 performs the various methods and processes described above, such as methods 200, 300, or 400. For example, in some embodiments, methods 200, 300, or 400 may be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as a storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the methods 200, 300, or 400 described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to execute the method 200 , 300 , or 400 in any other appropriate manner (eg, by means of firmware).

[0149] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0150] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0151] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0152] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0153] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0154] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[0155] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0156] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but only by the claims after authorization and their equivalent scope. Various elements in the embodiments or examples can be omitted or replaced by their equivalent elements. In addition, each step can be performed in an order different from that described in the present disclosure. Further, the various elements in the embodiments or examples can be combined in various ways. It is important that with the evolution of technology, many elements described herein can be replaced by equivalent elements that appear after the present disclosure.

Claims

1. A web page image processing method, comprising: Determine whether the image to be processed is blurred, wherein the image to be processed is any image in the web page; Based on the result of the judgment, adjusting the resolution of the image to be processed to obtain an optimized image corresponding to the image to be processed includes: In response to determining that the image to be processed is blurred, performing at least one super-resolution process on the image to be processed according to a preset magnification step length to obtain at least one intermediate image, and extracting feature information of each intermediate image; For each super-resolution processing, matching the first feature information of the intermediate image obtained by the current super-resolution processing with the second feature information of the intermediate image obtained by the previous super-resolution processing, wherein the intermediate image obtained by the previous super-resolution processing of the first super-resolution processing is the image to be processed, and the feature information includes the category information and the first position information of the object, and the content, the second position information and the confidence information of the text block; In response to satisfying at least one of the following conditions, determining that the first feature information matches the second feature information: the category information of the objects is the same; the similarity of the first position information is greater than a fourth threshold; the content of the text blocks is the same; the similarity of the second position information is greater than a fifth threshold; both confidence information are greater than a sixth threshold and the similarity of the two confidence information is greater than a seventh threshold; and In response to determining that the first feature information matches the second feature information, using an intermediate image obtained by the last super-resolution processing as the optimized image; as well as The address of the image to be processed is associated with the optimized image and stored.

2. The method according to claim 1, wherein: The resolution of the image to be processed is greater than or equal to a first threshold.

3. The method according to claim 1, further comprising: In response to determining that the image to be processed is blurred and has a resolution greater than a first threshold, the image to be processed is enhanced without changing the resolution, and the enhancement includes at least one of adjusting brightness, adjusting contrast, improving sharpness, and removing noise points.

4. The method according to claim 1 or 2, wherein: Based on the result of the determination, adjusting the resolution of the image to be processed includes: In response to determining that the image to be processed is not blurred, reducing the resolution of the image to be processed.

5. The method according to claim 4, wherein: Reducing the resolution of the image to be processed includes: Reducing the resolution of the image to be processed at least once according to a preset reduction ratio step to obtain at least one intermediate image, and extracting feature information of each intermediate image; For each reduction process, matching the first feature information of the intermediate image obtained by the current reduction process with the second feature information of the intermediate image obtained by the previous reduction process, wherein the intermediate image obtained by the previous reduction process of the first reduction process is the image to be processed; In response to satisfying at least one of the following conditions, determining that the first feature information does not match the second feature information: the category information of the objects is different; the similarity of the first position information is lower than the fourth threshold; the content of the text blocks is different; the similarity of the second position information is lower than the fifth threshold; at least one confidence information is lower than the sixth threshold; the similarity of the confidence information is lower than the seventh threshold; and In response to determining that the first feature information does not match the second feature information, the optimized image is determined based on an intermediate image obtained by a previous reduction process.

6. The method according to claim 5, wherein: Determining the optimized image based on the intermediate image obtained by the last reduction process includes: The resolution of the intermediate image obtained by the last reduction process is enlarged according to a preset enlargement factor, and the enlarged image is used as the optimized image.

7. The method according to claim 1 or 5, wherein: Extracting feature information of each intermediate image includes: Performing object recognition on the intermediate image to determine category information and first position information of at least one object included in the intermediate image; and Optical character recognition is performed on the intermediate image to determine the content, second position information and confidence information of each of at least one text block included in the intermediate image.

8. The method according to claim 1, wherein: Storing the address of the image to be processed in association with the optimized image includes: Calculating a first ratio of a larger resolution to a smaller resolution of the optimized image to a resolution of the image to be processed; and In response to determining that the first ratio is greater than or equal to a second threshold, the address of the image to be processed is stored in association with the optimized image.

9. The method according to claim 1, wherein: Storing the address of the image to be processed in association with the optimized image includes: calculating a second ratio of the number of images having corresponding optimized images in the webpage to the total number of images included in the webpage; and In response to determining that the second ratio is greater than a third threshold, the address of the image to be processed is stored in association with the optimized image.

10. The method according to claim 1, further comprising: The address of the webpage is associated with the optimization status information of the webpage and stored, wherein the optimization status information is used to indicate an image with an optimized image in the webpage.

11. A web page image loading method, comprising: Sending a query request to the first server, wherein the query request is used to query optimization status information of the web page to be loaded; Determine, based on the optimization status information returned by the first server, a first image with an optimized image and a second image without an optimized image in the webpage to be loaded, wherein the first server is configured to obtain an optimized image of the first image according to the method according to any one of claims 1 to 10; acquiring an optimized image of the first image from the first server; Acquire the second image from a second server where the second image is located; as well as The optimized image and the second image are loaded.

12. A web page image loading method, comprising: Sending an image acquisition request to the server, wherein the image acquisition request is used to acquire the image in the web page to be loaded; receiving a target image returned by the server, wherein the target image is the optimized image if the image has an optimized image, and the target image is the image if the image does not have an optimized image, and the server is configured to obtain the optimized image of the image according to the method according to any one of claims 1 to 10; and Load the target image.

13. A web page image processing device, comprising: A determination module, configured to determine whether the image to be processed is blurred, wherein the image to be processed is any image in a web page; The optimization module is configured to adjust the resolution of the image to be processed based on the result of the judgment to obtain an optimized image corresponding to the image to be processed, and the optimization module is further configured to: In response to determining that the image to be processed is blurred, performing at least one super-resolution process on the image to be processed according to a preset magnification step length to obtain at least one intermediate image, and extracting feature information of each intermediate image; For each super-resolution processing, matching the first feature information of the intermediate image obtained by the current super-resolution processing with the second feature information of the intermediate image obtained by the previous super-resolution processing, wherein the intermediate image obtained by the previous super-resolution processing of the first super-resolution processing is the image to be processed, and the feature information includes the category information and the first position information of the object, and the content, the second position information and the confidence information of the text block; In response to satisfying at least one of the following conditions, determining that the first feature information matches the second feature information: the category information of the objects is the same; the similarity of the first position information is greater than a fourth threshold; the content of the text blocks is the same; the similarity of the second position information is greater than a fifth threshold; both confidence information are greater than a sixth threshold and the similarity of the two confidence information is greater than a seventh threshold; and In response to determining that the first feature information matches the second feature information, using an intermediate image obtained by the last super-resolution processing as the optimized image; as well as The storage module is configured to store the address of the image to be processed in association with the optimized image.

14. A web page image loading device, comprising: A query module, configured to send a query request to the first server, wherein the query request is used to query optimization status information of the web page to be loaded; a determination module, configured to determine, based on the optimization status information returned by the first server, a first image with an optimized image and a second image without an optimized image in the webpage to be loaded, wherein the first server is configured to obtain the optimized image of the first image according to the apparatus according to claim 13; A first acquisition module is configured to acquire an optimized image of the first image from the first server; A second acquisition module is configured to acquire the second image from a second server where the second image is located; and A loading module is configured to load the optimized image and the second image.

15. A web page image loading device, comprising: A request module, configured to send an image acquisition request to the first server, wherein the image acquisition request is used to acquire an image in the web page to be loaded; a receiving module configured to receive a target image returned by the first server, wherein the target image is the optimized image if the image has an optimized image, and the target image is the image if the image does not have an optimized image, and the server is configured to obtain the optimized image of the image according to the apparatus according to claim 13; as well as The loading module is configured to load the target image.

16. An electronic device, comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 12.

17. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to make a computer execute the method according to any one of claims 1-12.

18. A computer program product comprising a computer program, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 12 is implemented.

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