CN112488964A - Image processing method for sliding list, related device, equipment and medium - Google Patents
Image processing method for sliding list, related device, equipment and medium Download PDFInfo
- Publication number
- CN112488964A CN112488964A CN202011506218.6A CN202011506218A CN112488964A CN 112488964 A CN112488964 A CN 112488964A CN 202011506218 A CN202011506218 A CN 202011506218A CN 112488964 A CN112488964 A CN 112488964A
- Authority
- CN
- China
- Prior art keywords
- image
- processing
- fuzzy
- text
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 16
- 238000012545 processing Methods 0.000 claims abstract description 86
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 47
- 238000000034 method Methods 0.000 claims abstract description 46
- 238000004364 calculation method Methods 0.000 claims abstract description 34
- 230000006835 compression Effects 0.000 claims abstract description 15
- 238000007906 compression Methods 0.000 claims abstract description 15
- 230000009467 reduction Effects 0.000 claims abstract description 10
- 238000003384 imaging method Methods 0.000 claims abstract description 9
- 238000004590 computer program Methods 0.000 claims description 13
- 230000008569 process Effects 0.000 abstract description 18
- 238000004891 communication Methods 0.000 description 11
- 238000010586 diagram Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 7
- 230000003287 optical effect Effects 0.000 description 5
- 230000004044 response Effects 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000013500 data storage Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 239000005338 frosted glass Substances 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 238000012015 optical character recognition Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000003190 augmentative effect Effects 0.000 description 1
- 239000000470 constituent Substances 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000001356 surgical procedure Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
Images
Classifications
-
- G06T5/92—
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0481—Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
- G06F3/0482—Interaction with lists of selectable items, e.g. menus
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0484—Interaction techniques based on graphical user interfaces [GUI] for the control of specific functions or operations, e.g. selecting or manipulating an object, an image or a displayed text element, setting a parameter value or selecting a range
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T9/00—Image coding
Abstract
The embodiment of the application discloses an image processing method for a sliding list, which comprises the following steps: the terminal equipment acquires a target image of the sliding list; the terminal equipment carries out image blurring processing on a plurality of images to be subjected to blurring processing in the target images of the sliding list by adopting an asynchronous thread; the image blurring processing of the image to be blurred comprises the step of carrying out small imaging processing on the image to be blurred, and reducing the size of the image to be blurred to a target size; after pixel point compression is carried out on the small-imaged image, fuzzy calculation processing is carried out through a Gaussian fuzzy algorithm to obtain a fuzzy processed image; the pixel point dereferencing radius of the Gaussian blur algorithm is reduced along with the reduction of the number of pixel points to be blurred in the compressed image; the method can well solve the technical problems that in the prior art, the mainstream fuzzy algorithm processing images involves large calculation amount, consumes huge calculation resources and easily causes the blockage of the list in the sliding process.
Description
Technical Field
The present application relates to the field of internet technologies, and in particular, to an image processing method for a sliding list, an image processing apparatus for a sliding list, a terminal device, and a computer-readable storage medium.
Background
With the rapid development of computer network computing, especially the development of mobile internet, mobile internet is gradually permeating into various fields of people's life and work. The user can complete various transactions only by installing various application programs on the electronic equipment according to the requirements of the user.
For example, a user may install an instant messaging client or software on a terminal device, and the instant messaging client may be used to identify more friends, so as to facilitate instant messaging with others. The user may slide through the user list to view the user's online status, the session messages left, etc.
When the images of the user head portraits are displayed in the user list, some users have the requirement of fuzzifying the user head portraits of the users, or some clients or software have the requirement of fuzzifying and displaying the user head portraits which meet the fuzzification.
In the list sliding process, each row of data can be assigned again, and the smoothness of the list sliding is directly influenced by the speed of the assignment process, so that the use experience of a user is influenced. In the prior art, the mainstream fuzzy algorithm processing images involves a large amount of calculation, consumes huge calculation resources, and easily causes the list to be stuck in the sliding process. If the user's head portrait is simply placed as a fixed image or covered with a frosted glass-like appearance (view) on the corresponding image, it will cause other users to be unable to obtain a true blurred image of the user's head portrait.
Disclosure of Invention
The embodiment of the application provides an image processing method for a sliding list, an image processing device for the sliding list, a terminal device and a computer readable storage medium, which can well solve the technical problems that in the prior art, the mainstream fuzzy algorithm processing image involves a large amount of calculation, consumes huge calculation resources and easily causes the list to be stuck in the sliding process.
In a first aspect, an embodiment of the present application provides an image processing method for a sliding list, where the method includes:
the terminal equipment acquires a target image of the sliding list;
the terminal equipment carries out image blurring processing on a plurality of images to be subjected to blurring processing in the target images of the sliding list by adopting an asynchronous thread;
the image blurring processing of the image to be blurred comprises the steps of carrying out small imaging processing on the image to be blurred, and reducing the size of the image to be blurred to a target size; after pixel point compression is carried out on the small-imaged image, fuzzy calculation processing is carried out through a Gaussian fuzzy algorithm to obtain a fuzzy processed image;
and the pixel value radius of the Gaussian blur algorithm is reduced along with the reduction of the number of pixels to be blurred in the compressed image.
In a possible implementation manner, the acquiring, by the terminal device, a target image of the sliding list includes:
receiving a first data stream and a second data stream of a sliding list image;
decoding the first data stream based on a first decoding rule to obtain a first image; decoding the second data stream based on a second decoding rule to obtain a second image;
and superposing the first image to the second image to obtain a target image of the sliding list.
In one possible implementation, the first image includes a target image-text that matches the data sample identified for the original image;
the first data stream is data obtained by encoding the image-text information corresponding to the target image-text through a first encoding rule;
the image-text information comprises the target image-text content, the size of the target image-text, the position information of the target image-text in the original image and the color of the target image-text.
In one possible implementation, the graphics information includes character information; the first data stream is data obtained by coding the character information through a first coding rule after the character information is identified from an original image;
the character information comprises character content, font size, position information of the character in the original image and character color.
In a possible implementation manner, the second image includes an image obtained after pixels corresponding to the target image-text are filled as adjacent related pixels in the original image.
In a possible implementation manner, a value radius σ of a pixel point of the gaussian fuzzy algorithm is determined by the following formula:
wherein, m is the number of pixel points to be subjected to fuzzy processing; said r1And r2Is a preset parameter.
In a possible implementation manner, the performing a blur calculation process by using a gaussian blur algorithm to obtain a blurred image includes:
and carrying out fuzzy calculation processing on each pixel point after the pixel point compression by using the GPU of the terminal equipment through a Gaussian fuzzy algorithm to obtain a fuzzy processed image.
In a second aspect, an embodiment of the present application provides an image processing apparatus for a sliding list, including:
an acquisition unit configured to acquire a target image of a slide list;
the fuzzy processing unit is used for carrying out image fuzzy processing on a plurality of images to be fuzzy processed in the target images of the sliding list by adopting asynchronous threads;
the image blurring processing unit is used for performing image blurring processing on an image to be subjected to blurring processing, and the image to be subjected to blurring processing is subjected to small imaging processing, so that the size of the image to be subjected to blurring processing is reduced to a target size; after pixel point compression is carried out on the small-imaged image, fuzzy calculation processing is carried out through a Gaussian fuzzy algorithm to obtain a fuzzy processed image;
and the pixel value radius of the Gaussian blur algorithm is reduced along with the reduction of the number of pixels to be blurred in the compressed image.
In one possible implementation manner, the obtaining unit includes:
a receiving unit configured to receive a first data stream and a second data stream of a sliding list image;
a decoding unit, configured to decode the first data stream based on a first decoding rule to obtain a first image; decoding the second data stream based on a second decoding rule to obtain a second image;
and the superposition unit is used for superposing the first image on the second image to obtain a target image of the sliding list.
In one possible implementation, the first image includes a target image-text that matches the data sample identified for the original image;
the first data stream is data obtained by encoding the image-text information corresponding to the target image-text through a first encoding rule;
the image-text information comprises the target image-text content, the size of the target image-text, the position information of the target image-text in the original image and the color of the target image-text.
In one possible implementation, the graphics information includes character information; the first data stream is data obtained by coding the character information through a first coding rule after the character information is identified from an original image;
the character information comprises character content, font size, position information of the character in the original image and character color.
In a possible implementation manner, the second image includes an image obtained after pixels corresponding to the target image-text are filled as adjacent related pixels in the original image.
In a possible implementation manner, a value radius σ of a pixel point of the gaussian fuzzy algorithm is determined by the following formula:
wherein, m is the number of pixel points to be subjected to fuzzy processing; said r1And r2Is a preset parameter.
In a possible implementation manner, the performing a blur calculation process by using a gaussian blur algorithm to obtain a blurred image includes:
and carrying out fuzzy calculation processing on each pixel point after the pixel point compression by using the GPU of the terminal equipment through a Gaussian fuzzy algorithm to obtain a fuzzy processed image.
In a third aspect, an embodiment of the present application provides a terminal device, where the terminal device includes a processor and a memory, where the memory is used for executing a program, and the processor is used for executing the program stored in the memory, and when the program stored in the memory is executed, the processor is used for executing the image processing method for a sliding list as provided in any one implementation manner of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, where a computer program is stored, where the computer program includes program instructions, and the program instructions, when executed by a processor, cause the processor to execute an image processing method for a sliding list provided in any one of the implementations of the first aspect.
In a fifth aspect, the present application further provides a computer program, where the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the image processing method for the sliding list provided in any one of the implementations of the first aspect.
The method comprises the steps that image blurring processing is conducted on a plurality of images to be subjected to blurring processing in target images of a sliding list through an asynchronous thread; the image blurring processing of the image to be blurred comprises the steps of carrying out small imaging processing on the image to be blurred, and reducing the size of the image to be blurred to a target size; after pixel point compression is carried out on the small-imaged image, fuzzy calculation processing is carried out through a Gaussian fuzzy algorithm to obtain a fuzzy processed image; and the pixel value radius of the Gaussian blur algorithm is reduced along with the reduction of the number of pixels to be blurred in the compressed image. The method greatly reduces the computing resources of equipment, can well solve the technical problems that the mainstream fuzzy algorithm processing images in the prior art involves large computing amount, consumes huge computing resources and easily causes the list to be stuck in the sliding process, and avoids the problem that the user head portrait is simply placed into a fixed image or a layer of frosted appearance (view) similar to frosted glass is covered on the corresponding image in the prior art, and other users cannot obtain the real fuzzy image of the user head portrait.
Further, by receiving a first data stream and a second data stream of a sliding list image; decoding the first data stream based on a first decoding rule to obtain a first image; decoding the second data stream based on a second decoding rule to obtain a second image; and superposing the first image to the second image to obtain a target image of the sliding list. The method can not only save the data amount of transmission and save the storage resource, but also save the computing resource for decoding and improve the working performance of the decoder because the first data stream and the second data stream are decoded by two different decoding rules.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below.
FIG. 1 is a system architecture diagram of an image processing method for a sliding list provided in the present application;
FIG. 2 is a flowchart illustrating an image processing method for a sliding list according to an embodiment of the present disclosure;
FIG. 3 is a schematic interface diagram of a sliding list provided by an embodiment of the present application;
fig. 4 is a schematic structural diagram of an image processing apparatus for a sliding list provided in an embodiment of the present application;
fig. 5 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
Technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
For a better understanding of the present application, a network architecture to which the present application is applicable is described below. Referring to fig. 1, fig. 1 is a schematic diagram of a system architecture of an image processing method for a sliding list according to the present application. As shown in fig. 1, the system architecture may include:
the server 101 may be a backend server for an application. Such as an instant messaging software or a management server of the platform. Instant messaging software mainly refers to a series of functions related to instant messaging services, such as online chat, online live broadcast and the like, provided by software or platform operators. The application program in the embodiment of the application includes but is not limited to instant messaging software.
The terminal device 103 may refer to a terminal device on the user side. The terminal device 103 may be installed and operated with a client, such as an instant messaging client. The user can register an account on the instant communication platform through the client and carry out instant communication with other users.
The client in the embodiment of the present invention refers to a program corresponding to the server and providing a local service to the client. Here, the local service may include, but is not limited to: human-computer interaction services, local data collection and maintenance services, communication services between local and server, etc.
Specifically, the client may include: an application running locally, a function running on a Web browser (also called Web App), an applet embedded in an email, an applet embedded in client software for instant messaging (e.g. WeChat), and a function embedded in another application (e.g. WeChat) (e.g. WeChat public number), etc. For the client, a corresponding server-side program needs to be run on the server to provide corresponding services, such as database services, data calculation, decision execution, and the like.
The terminal device referred to in the embodiments of the present application may also be referred to as a User Equipment (UE), a Mobile Station (MS), a mobile terminal device (MT), and the like, and is a device that provides voice/data connectivity to a user, for example, a handheld device, a vehicle-mounted device, and the like having a wireless connection function. Examples of some terminal devices are: a mobile phone (mobile phone), a tablet computer, a notebook computer, a palm computer, a Mobile Internet Device (MID), a wearable device, a Virtual Reality (VR) device, an Augmented Reality (AR) device, a wireless terminal device in industrial control (industrial control), a wireless terminal device in self driving (self driving), a wireless terminal device in remote surgery (remote medical supply), a wireless terminal device in smart grid (smart grid), a wireless terminal device in transportation safety (transportation safety), a wireless terminal device in smart city (smart city), a wireless terminal device in smart home (smart home), a vehicle with a cockpit controller, and the like.
The embodiment of the application takes instant messaging software as an example for explanation, the instant messaging software is installed on the terminal equipment, and a user starts the instant messaging software by using the terminal equipment and performs character interaction with other users according to requirements. The following flowchart of the image processing method for a sliding list provided in the embodiment of the present application shown in fig. 2 may include the following steps:
step S200: acquiring a target image of a sliding list;
specifically, when the terminal device needs to present a slidable list, and each line of the list corresponds to a respective image, for example, a user list, each line corresponds to a head portrait of a user; then the terminal equipment can interact with the server to obtain the target image of the sliding list
Step S202: carrying out image blurring processing on a plurality of images to be subjected to blurring processing in the target images of the sliding list by adopting an asynchronous thread;
specifically, there are multiple images to be blurred in the target image, such as the interface schematic diagram of the sliding list provided in the embodiment of the present application shown in fig. 3, and 30 marked in the sliding list of fig. 3 is the blurred target image. The terminal equipment carries out image blurring processing on a plurality of images to be subjected to blurring processing in the target images of the sliding list in an asynchronous thread mode, so that the efficiency of image blurring calculation can be improved.
The image blurring processing of the image to be blurred comprises the steps of carrying out small imaging processing on the image to be blurred, and reducing the size of the image to be blurred to a target size; after pixel point compression is carried out on the small-imaged image, fuzzy calculation processing is carried out through a Gaussian fuzzy algorithm to obtain a fuzzy processed image; the pixel point dereferencing radius of the Gaussian blur algorithm is reduced along with the reduction of the number of pixel points to be blurred in the compressed image.
Moreover, the terminal device can use the GPU thereof to perform fuzzy calculation processing on each pixel point after pixel point compression through a gaussian fuzzy algorithm, so as to obtain a fuzzy processed image.
The method comprises the steps that image blurring processing is conducted on a plurality of images to be subjected to blurring processing in target images of a sliding list through an asynchronous thread; the image blurring processing of the image to be blurred comprises the steps of carrying out small imaging processing on the image to be blurred, and reducing the size of the image to be blurred to a target size; after pixel point compression is carried out on the small-imaged image, fuzzy calculation processing is carried out through a Gaussian fuzzy algorithm to obtain a fuzzy processed image; the pixel dereferencing radius of the Gaussian blur algorithm is reduced along with the reduction of the number of pixels to be blurred in the compressed image, and the reduction of the pixel dereferencing radius of the Gaussian blur algorithm can reduce the calculation resources required by the blur algorithm. Therefore, the computing resources of equipment are greatly reduced, the technical problems that in the prior art, the image processing by the mainstream fuzzy algorithm involves large computation amount, huge computing resources are consumed, and the list is easy to be jammed in the sliding process are solved, and the problem that in the prior art, the user head portrait is simply placed into a fixed image or a layer of frosted appearance (view) similar to frosted glass is covered on the corresponding image, and the real fuzzy image of the user head portrait cannot be obtained by other users is avoided.
In one implementation, the radius σ of a pixel point of the gaussian fuzzy algorithm is determined by the following formula:
wherein, m is the number of pixel points to be subjected to fuzzy processing; said r1And r2Is a preset parameter.
Preferably, r in the embodiments of the present application1May be 120, r2May be 240. Through the values of the two parameters, a user can better distinguish the information content even if the head portrait (image) is blurred, the problem that other users cannot obtain the real blurred image of the head portrait of the user due to the fact that the head portrait of the user is simply placed into a fixed image or a layer of frosted glass-like appearance (view) is covered on the corresponding image in the prior art is solved, meanwhile, the calculation resources required by the blurring algorithm calculation are reduced, and the technical problems that the mainstream blurring algorithm in the prior art can involve large calculation amount in image processing, huge calculation resources are consumed, and the list is prone to being jammed in the sliding process are solved. Namely, the problems of the two are well combined.
In one implementation manner, when the server side sends the target image of the sliding list in the embodiment of the present application, the method may specifically include:
when the server codes the target image, firstly identifying the target image; for example, the server may train the recognition model in advance through deep learning, input the training sample image into the recognition model, so that the recognition model can recognize the matched training sample image-text, and store the training sample image-text in the database as the data sample to be recognized in the subsequent application. After the recognition model is trained, the server can input the target image, recognize the target image through the recognition model, and see whether the target image has a target image-text matched with the data sample in the database.
If the target image is identified to have the target image-text matched with the data sample in the database, the image-text information corresponding to the target image-text in the target image can be extracted. The image-text information may comprise the target image-text content, the size of the target image-text, the position information of the original image in which the target image-text is located, and the color of the target image-text. The target image-text content can be the content of the data sample matched with the target image-text content in the database; the position information of the target image-text in the original image is the position information of the target image-text in the original image as a whole.
Then, the server encodes the image-text information corresponding to the target image-text based on a first encoding rule to obtain a first data stream. Because the image-text information only comprises the content of the data sample matched with the target image-text in the database, the size of the target image-text, the position information of the target image-text in the original image and the color of the target image-text, the encoding quantity for encoding the image-text information is much smaller than the encoding quantity for encoding the original target image-text in the target image. Because the original target image-text in the target image must include the corresponding numerical value and the coordinate value of each pixel, the corresponding code amount is much larger.
For example, the image-text information corresponding to the target image-text in the embodiment of the present application may include character information; the character information may include character content, font size, position information of where the character is located in the original image, and character color.
The server fills pixel points corresponding to the target image-text in the target image into adjacent related pixels to obtain a second image; and coding the second image based on a second coding rule to obtain a second data stream. Specifically, the server may predict, based on an adjacent pixel prediction algorithm, adjacent related pixels through adjacent pixels of the pixel point corresponding to the target image-text, and fill the adjacent related pixels into the pixel point corresponding to the target image-text.
And finally, the server sends the encoded first data stream and the encoded second data stream to the terminal equipment.
The step of the terminal device acquiring the target image of the sliding list in step S200 may specifically include:
receiving a first data stream and a second data stream of a sliding list image; decoding the first data stream based on a first decoding rule to obtain a first image; decoding the second data stream based on a second decoding rule to obtain a second image; and superposing the first image to the second image to obtain a target image of the sliding list.
Specifically, the first decoding rule and the second decoding rule are decoding rules corresponding to the first encoding rule and the second encoding rule. The server may carry information for sending the first encoding rule and the second encoding rule in the process of sending the first data stream and the second data stream, so that the terminal device knows the corresponding first decoding rule and the second decoding rule. And then, superposing the first image on the second image according to the position of the first image to obtain a target image of the sliding list.
By the embodiment, the data stream corresponding to the target image received by the terminal equipment is reduced, the transmitted data volume is reduced, and the transmission efficiency is improved. In addition, in the decoding process, the calculation resource for decoding can be saved, and the working performance of the decoder is improved, so that the target image can be more efficiently decoded.
In one implementation manner, when the image-text information corresponding to the target image-text in the embodiment of the present application includes Character information, when the server encodes the target image, the server first identifies the target image, and may specifically and directly identify the original image through Optical Character Recognition (OCR) to see whether a Character is identified. The character information includes character content, font size, position information of the character in the original image, and character color.
In order to better implement the method of the embodiment of the present application, the embodiment of the present application also describes a schematic structural diagram of an image processing apparatus and a terminal device for a sliding list, which belong to the same application concept as the embodiment of the method. The following detailed description is made with reference to the accompanying drawings:
as shown in fig. 4, which is a schematic structural diagram of an image processing apparatus for a sliding list provided in an embodiment of the present application, the image processing apparatus 40 for a sliding list is equivalent to the terminal device in the embodiment of fig. 2, and the image processing apparatus 40 for a sliding list may include an acquisition unit 400 and a blurring processing unit 402, where:
the acquisition unit 400 is used for acquiring a target image of the sliding list;
the blurring processing unit 402 is configured to perform image blurring processing on a plurality of images to be blurred in the target images of the sliding list by using an asynchronous thread;
the blurring processing unit 402 performs image blurring processing on the image to be blurred, including performing small imaging processing on the image to be blurred, and reducing the size of the image to be blurred to a target size; after pixel point compression is carried out on the small-imaged image, fuzzy calculation processing is carried out through a Gaussian fuzzy algorithm to obtain a fuzzy processed image;
and the pixel value radius of the Gaussian blur algorithm is reduced along with the reduction of the number of pixels to be blurred in the compressed image.
In one possible implementation, the obtaining unit 400 may include:
a receiving unit configured to receive a first data stream and a second data stream of a sliding list image;
a decoding unit, configured to decode the first data stream based on a first decoding rule to obtain a first image; decoding the second data stream based on a second decoding rule to obtain a second image;
and the superposition unit is used for superposing the first image on the second image to obtain a target image of the sliding list.
In one possible implementation, the first image includes a target image-text that matches the data sample identified for the original image;
the first data stream is data obtained by encoding the image-text information corresponding to the target image-text through a first encoding rule;
the image-text information comprises the target image-text content, the size of the target image-text, the position information of the target image-text in the original image and the color of the target image-text.
In one possible implementation, the graphics information includes character information; the first data stream is data obtained by coding the character information through a first coding rule after the character information is identified from an original image;
the character information comprises character content, font size, position information of the character in the original image and character color.
In a possible implementation manner, the second image includes an image obtained after pixels corresponding to the target image-text are filled as adjacent related pixels in the original image.
In a possible implementation manner, a value radius σ of a pixel point of the gaussian fuzzy algorithm is determined by the following formula:
wherein, m is the number of pixel points to be subjected to fuzzy processing; said r1And r2Is a preset parameter.
In a possible implementation manner, the performing a blur calculation process by using a gaussian blur algorithm to obtain a blurred image includes:
and carrying out fuzzy calculation processing on each pixel point after the pixel point compression by using the GPU of the terminal equipment through a Gaussian fuzzy algorithm to obtain a fuzzy processed image.
It is to be understood that the description of each unit in the image processing apparatus 40 for the sliding list may also refer to the description related to the terminal device in the foregoing fig. 2 embodiment implementing the image processing method for the sliding list, and the detailed description is not repeated here.
As shown in fig. 5, which is a schematic structural diagram of a terminal device provided in this embodiment of the present application, the terminal device 50 is the terminal device in the foregoing method embodiment, and may include a processor 500, a memory 502, and a communication module 504.
The processor 500 may be a general purpose Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits for controlling the execution of programs according to the above schemes.
The Memory 502 may be a Read-Only Memory (ROM) or other types of static storage devices that can store static information and instructions, a Random Access Memory (RAM) or other types of dynamic storage devices that can store information and instructions, an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Compact Disc Read-Only Memory (CD-ROM) or other optical Disc storage, optical Disc storage (including Compact Disc, laser Disc, optical Disc, digital versatile Disc, blu-ray Disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code or computer programs in the form of instructions or data structures and that can be accessed by a computer, but is not limited to such. The memory 502 may be separate and coupled to the processor 500 via a bus. The memory 502 may also be integrated with the processor 500.
The communication module 504 is used for receiving and sending information;
the memory 502 is used for storing computer programs for executing the above schemes, and is controlled by the processor 500 to execute. The processor 500 is configured to execute the computer program stored in the memory 502.
When the program stored in the memory 502 is executed, the processor 500 is configured to perform the steps related to the terminal device in the image processing method for the sliding list provided in fig. 2 above:
acquiring a target image of a sliding list;
carrying out image blurring processing on a plurality of images to be subjected to blurring processing in the target images of the sliding list by adopting an asynchronous thread;
the image blurring processing of the image to be blurred comprises the steps of carrying out small imaging processing on the image to be blurred, and reducing the size of the image to be blurred to a target size; after pixel point compression is carried out on the small-imaged image, fuzzy calculation processing is carried out through a Gaussian fuzzy algorithm to obtain a fuzzy processed image;
and the pixel value radius of the Gaussian blur algorithm is reduced along with the reduction of the number of pixels to be blurred in the compressed image.
In one possible implementation, the acquiring, by the processor 500, a target image of a sliding list may include:
receiving, by the communication module 504, a first data stream and a second data stream of the sliding list image;
decoding the first data stream based on a first decoding rule to obtain a first image; decoding the second data stream based on a second decoding rule to obtain a second image;
and superposing the first image to the second image to obtain a target image of the sliding list.
In one possible implementation, the graphics information includes character information; the first data stream is data obtained by coding the character information through a first coding rule after the character information is identified from an original image;
the character information comprises character content, font size, position information of the character in the original image and character color.
In a possible implementation manner, the second image includes an image obtained after pixels corresponding to the target image-text are filled as adjacent related pixels in the original image.
In a possible implementation manner, a value radius σ of a pixel point of the gaussian fuzzy algorithm is determined by the following formula:
wherein, m is the number of pixel points to be subjected to fuzzy processing; said r1And r2Is a preset parameter.
In a possible implementation manner, the processor 500 is a GPU of the terminal device 50, and the performing, by the terminal device 50, a blur calculation process through a gaussian blur algorithm to obtain a blurred image may include:
and carrying out fuzzy calculation processing on each pixel point after the pixel point compression by using a GPU of the terminal equipment through a Gaussian fuzzy algorithm to obtain a fuzzy processed image.
The above specific implementation may refer to the implementation of the above method embodiment, and details are not described here.
The present embodiments also provide a computer storage medium having instructions stored therein, which when executed on a computer or a processor, cause the computer or the processor to perform one or more steps of the method according to any one of the above embodiments. Based on the understanding that the constituent modules of the above-mentioned apparatus, if implemented in the form of software functional units and sold or used as independent products, may be stored in the computer-readable storage medium, and based on this understanding, the technical solutions of the present application, in essence, or a part contributing to the prior art, or all or part of the technical solutions, may be embodied in the form of software products, and the computer products are stored in the computer-readable storage medium.
The computer readable storage medium may be an internal storage unit of the device according to the foregoing embodiment, such as a hard disk or a memory. The computer readable storage medium may be an external storage device of the above-described apparatus, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the computer-readable storage medium may include both an internal storage unit and an external storage device of the device. The computer-readable storage medium is used for storing the computer program and other programs and data required by the apparatus. The above-described computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.
It will be understood by those skilled in the art that all or part of the processes of the methods of the above embodiments may be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the above embodiments of the methods when the computer program is executed. And the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.
The steps in the method of the embodiment of the application can be sequentially adjusted, combined and deleted according to actual needs.
The modules in the device can be merged, divided and deleted according to actual needs.
It is to be understood that one of ordinary skill in the art would recognize that the elements and algorithm steps of the various examples described in connection with the embodiments disclosed in the various embodiments disclosed herein can be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Those of skill would appreciate that the functions described in connection with the various illustrative logical blocks, modules, and algorithm steps disclosed in the various embodiments disclosed herein may be implemented as hardware, software, firmware, or any combination thereof. If implemented in software, the functions described in the various illustrative logical blocks, modules, and steps may be stored on or transmitted over as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. The computer-readable medium may include a computer-readable storage medium, which corresponds to a tangible medium, such as a data storage medium, or any communication medium including a medium that facilitates transfer of a computer program from one place to another (e.g., according to a communication protocol). In this manner, a computer-readable medium may generally correspond to (1) a non-transitory tangible computer-readable storage medium, or (2) a communication medium, such as a signal or carrier wave. A data storage medium may be any available medium that can be accessed by one or more computers or one or more processors to retrieve instructions, code and/or data structures for implementing the techniques described herein. The computer program product may include a computer-readable medium.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. An image processing method for a sliding list, comprising:
the terminal equipment acquires a target image of the sliding list;
the terminal equipment carries out image blurring processing on a plurality of images to be subjected to blurring processing in the target images of the sliding list by adopting an asynchronous thread;
the image blurring processing of the image to be blurred comprises the steps of carrying out small imaging processing on the image to be blurred, and reducing the size of the image to be blurred to a target size; after pixel point compression is carried out on the small-imaged image, fuzzy calculation processing is carried out through a Gaussian fuzzy algorithm to obtain a fuzzy processed image;
and the pixel value radius of the Gaussian blur algorithm is reduced along with the reduction of the number of pixels to be blurred in the compressed image.
2. The method of claim 1, wherein the terminal device acquiring the target image of the sliding list comprises:
receiving a first data stream and a second data stream of a sliding list image;
decoding the first data stream based on a first decoding rule to obtain a first image; decoding the second data stream based on a second decoding rule to obtain a second image;
and superposing the first image to the second image to obtain a target image of the sliding list.
3. The method of claim 2, wherein the first image comprises a target image identified for the original image that matches the data sample;
the first data stream is data obtained by encoding the image-text information corresponding to the target image-text through a first encoding rule;
the image-text information comprises the target image-text content, the size of the target image-text, the position information of the target image-text in the original image and the color of the target image-text.
4. A method as claimed in claim 3, wherein the teletext information comprises character information; the first data stream is data obtained by coding the character information through a first coding rule after the character information is identified from an original image;
the character information comprises character content, font size, position information of the character in the original image and character color.
5. A method as claimed in claim 3, wherein the second image comprises an image obtained by filling pixels corresponding to the object-text in the original image as neighboring related pixels.
6. The method according to any one of claims 1 to 5, wherein a pixel point dereferencing radius σ pixels of the Gaussian blur algorithm is determined by the following formula:
wherein, m is the number of pixel points to be subjected to fuzzy processing; said r1And r2Is a preset parameter.
7. The method of claim 6, wherein the blur calculation processing by the gaussian blur algorithm to obtain the blurred image comprises:
and carrying out fuzzy calculation processing on each pixel point after the pixel point compression by using the GPU of the terminal equipment through a Gaussian fuzzy algorithm to obtain a fuzzy processed image.
8. An image processing apparatus for a sliding list, comprising means to perform the method according to any of claims 1-7.
9. A terminal device, comprising: a memory for a program and a processor for executing the program stored by the memory, the processor being configured to perform the method of any one of claims 1-7 when the program stored by the memory is executed.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program comprising program instructions that, when executed by a processor, cause the processor to carry out the method according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011506218.6A CN112488964B (en) | 2020-12-18 | 2020-12-18 | Image processing method, related device, equipment and medium for sliding list |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011506218.6A CN112488964B (en) | 2020-12-18 | 2020-12-18 | Image processing method, related device, equipment and medium for sliding list |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112488964A true CN112488964A (en) | 2021-03-12 |
| CN112488964B CN112488964B (en) | 2024-04-16 |
Family
ID=74914148
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011506218.6A Active CN112488964B (en) | 2020-12-18 | 2020-12-18 | Image processing method, related device, equipment and medium for sliding list |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112488964B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113486271A (en) * | 2021-07-06 | 2021-10-08 | 网易（杭州）网络有限公司 | Method and device for determining dominant hue of image and electronic terminal |
| CN115526809A (en) * | 2022-11-04 | 2022-12-27 | 山东捷瑞数字科技股份有限公司 | Image processing method and device, electronic equipment and storage medium |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040240737A1 (en) * | 2003-03-15 | 2004-12-02 | Chae-Whan Lim | Preprocessing device and method for recognizing image characters |
| US20050286794A1 (en) * | 2004-06-24 | 2005-12-29 | Apple Computer, Inc. | Gaussian blur approximation suitable for GPU |
| WO2016050126A1 (en) * | 2014-09-30 | 2016-04-07 | Beijing Zhigu Tech Co., Ltd. | Super-resolution image acquisition methods and apparatus |
| CN105843516A (en) * | 2015-01-13 | 2016-08-10 | 阿里巴巴集团控股有限公司 | Method and device for displaying information during scrolling of list page |
| CN107240071A (en) * | 2016-03-29 | 2017-10-10 | 掌赢信息科技（上海）有限公司 | A kind of image blurring processing method and electronic equipment |
| CN108182668A (en) * | 2017-12-29 | 2018-06-19 | 努比亚技术有限公司 | A kind of enlarged drawing processing method, terminal and computer readable storage medium |
| CN108564541A (en) * | 2018-03-28 | 2018-09-21 | 麒麟合盛网络技术股份有限公司 | A kind of image processing method and device |
| CN108763350A (en) * | 2018-05-15 | 2018-11-06 | Oppo广东移动通信有限公司 | Text data processing method, device, storage medium and terminal |
| CN109035158A (en) * | 2018-06-25 | 2018-12-18 | 东软集团股份有限公司 | Image fuzzy processing method, device, storage medium and electronic equipment |
| CN109314776A (en) * | 2017-05-17 | 2019-02-05 | 深圳配天智能技术研究院有限公司 | Image processing method, image processing equipment and storage medium |
| CN109618173A (en) * | 2018-12-17 | 2019-04-12 | 深圳Tcl新技术有限公司 | Video-frequency compression method, device and computer readable storage medium |
| CN110798693A (en) * | 2019-09-29 | 2020-02-14 | 深圳市镜玩科技有限公司 | User management method, server and computer readable storage medium |
| CN111045576A (en) * | 2018-10-11 | 2020-04-21 | 阿里巴巴集团控股有限公司 | Display control method, display control device, terminal device and electronic device |
| CN111611573A (en) * | 2020-05-20 | 2020-09-01 | 深圳市镜玩科技有限公司 | Data processing method for realizing terminal equipment switching, related equipment and medium |
-
2020
- 2020-12-18 CN CN202011506218.6A patent/CN112488964B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040240737A1 (en) * | 2003-03-15 | 2004-12-02 | Chae-Whan Lim | Preprocessing device and method for recognizing image characters |
| US20050286794A1 (en) * | 2004-06-24 | 2005-12-29 | Apple Computer, Inc. | Gaussian blur approximation suitable for GPU |
| WO2016050126A1 (en) * | 2014-09-30 | 2016-04-07 | Beijing Zhigu Tech Co., Ltd. | Super-resolution image acquisition methods and apparatus |
| CN105843516A (en) * | 2015-01-13 | 2016-08-10 | 阿里巴巴集团控股有限公司 | Method and device for displaying information during scrolling of list page |
| CN107240071A (en) * | 2016-03-29 | 2017-10-10 | 掌赢信息科技（上海）有限公司 | A kind of image blurring processing method and electronic equipment |
| CN109314776A (en) * | 2017-05-17 | 2019-02-05 | 深圳配天智能技术研究院有限公司 | Image processing method, image processing equipment and storage medium |
| CN108182668A (en) * | 2017-12-29 | 2018-06-19 | 努比亚技术有限公司 | A kind of enlarged drawing processing method, terminal and computer readable storage medium |
| CN108564541A (en) * | 2018-03-28 | 2018-09-21 | 麒麟合盛网络技术股份有限公司 | A kind of image processing method and device |
| CN108763350A (en) * | 2018-05-15 | 2018-11-06 | Oppo广东移动通信有限公司 | Text data processing method, device, storage medium and terminal |
| CN109035158A (en) * | 2018-06-25 | 2018-12-18 | 东软集团股份有限公司 | Image fuzzy processing method, device, storage medium and electronic equipment |
| CN111045576A (en) * | 2018-10-11 | 2020-04-21 | 阿里巴巴集团控股有限公司 | Display control method, display control device, terminal device and electronic device |
| CN109618173A (en) * | 2018-12-17 | 2019-04-12 | 深圳Tcl新技术有限公司 | Video-frequency compression method, device and computer readable storage medium |
| CN110798693A (en) * | 2019-09-29 | 2020-02-14 | 深圳市镜玩科技有限公司 | User management method, server and computer readable storage medium |
| CN111611573A (en) * | 2020-05-20 | 2020-09-01 | 深圳市镜玩科技有限公司 | Data processing method for realizing terminal equipment switching, related equipment and medium |
Non-Patent Citations (6)
| Title |
|---|
| JAN FLUSSER 等: "Recognition of Images Degraded by Gaussian Blur", IEEE TRANSACTIONS ON IMAGE PROCESSING（IEEE）, vol. 25, no. 2, 23 December 2015 (2015-12-23), pages 790 - 806, XP011592230, DOI: 10.1109/TIP.2015.2512108 * |
| MUHAMMAD FRAZ 等: "Exploiting colour information for better scene text detection and recognition", INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION (IJDAR) （SPRINGER）, 19 February 2015 (2015-02-19), pages 153 - 167 * |
| 刘海;: "一种图像自适应中值滤波算法", 软件导刊, no. 05, 15 May 2018 (2018-05-15), pages 59 - 61 * |
| 周建华;: "基于维纳滤波压缩感知的车辆运动模糊图像恢复", 河北北方学院学报(自然科学版), no. 06, 28 December 2015 (2015-12-28), pages 6 - 10 * |
| 彭婷;王福龙;: "结合小波变换和改进邻域权值的FCM算法", 计算机系统应用, no. 02, 15 February 2016 (2016-02-15), pages 116 - 123 * |
| 程书睿;陈翰林;胡荣春;: "基于模糊聚类的施工升降梯内部人数统计", 现代计算机(专业版), no. 35, 15 December 2017 (2017-12-15), pages 35 - 40 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113486271A (en) * | 2021-07-06 | 2021-10-08 | 网易（杭州）网络有限公司 | Method and device for determining dominant hue of image and electronic terminal |
| CN115526809A (en) * | 2022-11-04 | 2022-12-27 | 山东捷瑞数字科技股份有限公司 | Image processing method and device, electronic equipment and storage medium |
| CN115526809B (en) * | 2022-11-04 | 2023-03-10 | 山东捷瑞数字科技股份有限公司 | Image processing method and device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112488964B (en) | 2024-04-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109993150B (en) | Method and device for identifying age | |
| CN109309842B (en) | Live broadcast data processing method and device, computer equipment and storage medium | |
| CN112488964A (en) | Image processing method for sliding list, related device, equipment and medium | |
| CN111444826A (en) | Video detection method and device, storage medium and computer equipment | |
| CN112770129A (en) | Live broadcast-based group chat establishing method, related device, equipment and medium | |
| CN114494815A (en) | Neural network training method, target detection method, device, equipment and medium | |
| CN113794899A (en) | Cloud desktop image data transmission method, device, equipment and storage medium | |
| JP2023039892A (en) | Training method for character generation model, character generating method, device, apparatus, and medium | |
| CN114511041A (en) | Model training method, image processing method, device, equipment and storage medium | |
| CN113963359A (en) | Text recognition model training method, text recognition device and electronic equipment | |
| CN106530377B (en) | Method and apparatus for manipulating three-dimensional animated characters | |
| CN108234659A (en) | Data processing method, apparatus and system | |
| CN113963358B (en) | Text recognition model training method, text recognition device and electronic equipment | |
| CN114222181B (en) | Image processing method, device, equipment and medium | |
| CN116129534A (en) | Image living body detection method and device, storage medium and electronic equipment | |
| CN116977247A (en) | Image processing method, device, electronic equipment and storage medium | |
| CN108121942B (en) | Fingerprint identification method and device | |
| CN113157226A (en) | Remote data display method, device, equipment and machine-readable storage medium | |
| CN111667411A (en) | Image transmission method and device, electronic equipment and storage medium | |
| CN115937338B (en) | Image processing method, device, equipment and medium | |
| CN113117341B (en) | Picture processing method and device, computer readable storage medium and electronic equipment | |
| CN113362218B (en) | Data processing method and device, electronic equipment and storage medium | |
| CN116629947B (en) | Method, device, equipment and medium for generating flow site processing information | |
| CN117278765B (en) | Video compression method, device, equipment and storage medium | |
| CN113627399B (en) | Topic processing method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |