CN109583477A - A kind of pixel classifications processing method, device and the electronic equipment of image - Google Patents
A kind of pixel classifications processing method, device and the electronic equipment of image Download PDFInfo
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Abstract
The embodiment of the invention provides pixel classifications processing method, device and the electronic equipment of a kind of image, this method and device are specifically used for obtaining image and its multiple coding parameters to be sorted；To all pixels of the image classified by trained Intelligence Classifier in advance and according to multiple coding parameters of image, obtains multiple pixel classifications.Since this programme is not limited to multiple encoded informations such as consider Gradient Features, but consider prediction mode, Prediction Parameters used in cataloged procedure, motion vector, quantization parameter, residual information, pixel value, to improve nicety of grading.
Description
Technical field
This disclosure relates to technical field of image processing more particularly to a kind of pixel classifications processing method of image, device and
Electronic equipment.
Background technique
ALF (Adaptive Loop Filter) auto-adaptive loop filter is the coding work proposed in VVC standard
Tool, belongs to one kind of loop filter, its last ring is in coded image treatment process.Its principle is filtered based on wiener
Wave algorithm repairs the distortion introduced in cataloged procedure with this, after making filtering according to the Image estimation original input picture after coding
Image closer to original image.
Prior art is the gradient direction and size according to image local area to be filtered, by comparing and quantization
Pixel is divided into 25 classifications by mode, is fitted one group of filter system respectively.This mode only considered the gradient of image pixel
Feature causes the accuracy of classification poor.
Summary of the invention
To overcome the problems in correlation technique, the disclosure provides a kind of pixel classifications processing method of image, device
And electronic equipment.
In a first aspect, providing a kind of pixel classifications processing method of image, it is applied to image processing system, comprising:
Obtain image and its multiple coding parameters to be sorted；
According to multiple coding parameters of described image, by Intelligence Classifier trained in advance to all pictures of described image
Element is classified, and multiple pixel classifications are obtained.
Optionally, the multiple coding parameter includes prediction mode, Prediction Parameters, motion vector, quantization parameter, residual error letter
Some or all of in breath, current pixel value and adjacent pixel values.
Optionally, the Intelligence Classifier is Logic Regression Models, decision tree or support vector machines.
Optionally, in multiple coding parameters according to described image, by the Intelligence Classifier trained in advance to institute
The all pixels for stating image are classified, before obtaining multiple pixel classifications, further includes:
Extract multiple characteristic values of described image；
The all pixels progress according to multiple coding parameters, by Intelligence Classifier trained in advance to described image
Classification, obtains multiple pixel classifications, comprising:
According to the multiple coding parameter and the multiple characteristic value, by the Intelligence Classifier to the institute of described image
There is pixel to classify, obtains the multiple pixel classifications.
Optionally, further includes:
Training sample set is obtained, the training sample set includes the pre- of multiple images sample and each described image sample
Some or all of survey mode, quantization parameter, residual information, current pixel value and adjacent pixel values；
Model training is carried out using the training sample set, obtains the classifier.
Second aspect provides a kind of pixel classifications processing unit of image, is applied to image processing system, comprising:
Image input module is configured as obtaining image and its multiple coding parameters to be sorted；
Classification execution module, is configured as multiple coding parameters according to described image, passes through intelligence trained in advance point
Class device classifies to all pixels of described image, obtains multiple pixel classifications.
Optionally, the multiple coding parameter includes prediction mode, Prediction Parameters, motion vector, quantization parameter, residual error letter
Some or all of in breath, current pixel value and adjacent pixel values.
Optionally, the Intelligence Classifier is Logic Regression Models, decision tree or support vector machines.
Optionally, further includes:
Gradient extraction module is configured as carrying out classifying it by the Intelligence Classifier in the classification execution module
Before, extract multiple characteristic values of described image；
The classification execution module is then configured as passing through institute according to the multiple coding parameter and the multiple characteristic value
It states Intelligence Classifier to classify to all pixels of described image, obtains the multiple pixel classifications.
Optionally, further includes:
Sample acquisition module, is configured as obtaining training sample set, the training sample set include multiple images sample with
And the prediction mode of each described image sample, quantization parameter, residual information, current pixel value and adjacent pixel values part or
All；
Model training module is configured as carrying out model training using the training sample set, obtains the classifier.
The third aspect provides a kind of electronic equipment, comprising:
Processor；
Memory for storage processor executable instruction；
Wherein, the processor is configured to executing pixel classifications processing method as described in relation to the first aspect.
Fourth aspect provides a kind of computer program, for executing the pixel classifications processing side as described in terms of proposing
Method.
5th aspect, provides a kind of non-transitorycomputer readable storage medium, the instruction in the storage medium
When being executed by the processor of mobile terminal, so that mobile terminal is able to carry out pixel classifications processing side as described in relation to the first aspect
Method.
The technical scheme provided by this disclosed embodiment can include the following benefits: since this programme is not limited to consider
Gradient Features, but it is multiple to consider prediction mode, quantization parameter used in cataloged procedure, residual information, pixel value etc.
Encoded information, to improve nicety of grading
It should be understood that above general description and following detailed description be only it is exemplary and explanatory, not
The disclosure can be limited.
Detailed description of the invention
The drawings herein are incorporated into the specification and forms part of this specification, and shows and meets implementation of the invention
Example, and be used to explain the principle of the present invention together with specification.
Fig. 1 is a kind of flow chart of the pixel classifications processing method of image shown according to an exemplary embodiment；
Fig. 2 is the flow chart of the pixel classifications processing method of another image shown according to an exemplary embodiment；
Fig. 3 is the flow chart of the pixel classifications processing method of another image shown according to an exemplary embodiment；
Fig. 4 is a kind of block diagram of the pixel classifications processing unit of image shown according to an exemplary embodiment；
Fig. 5 is the block diagram of the pixel classifications processing unit of another image shown according to an exemplary embodiment；
Fig. 6 is the block diagram of the pixel classifications processing unit of another image shown according to an exemplary embodiment；
Fig. 7 is the block diagram of a kind of electronic equipment shown according to an exemplary embodiment；
Fig. 8 is the block diagram of another electronic equipment shown according to an exemplary embodiment.
Specific embodiment
Example embodiments are described in detail here, and the example is illustrated in the accompanying drawings.Following description is related to
When attached drawing, unless otherwise indicated, the same numbers in different drawings indicate the same or similar elements.Following exemplary embodiment
Described in embodiment do not represent all embodiments consistented with the present invention.On the contrary, they be only with it is such as appended
The example of device and method being described in detail in claims, some aspects of the invention are consistent.
Fig. 1 is a kind of flow chart of the pixel classifications processing method of image shown according to an exemplary embodiment.
As shown in Figure 1, pixel classifications processing method provided in this embodiment is applied to server, computer or mobile terminal
Etc. in equipment, specifically include following steps.
S1, image and its multiple coding parameters to be sorted are obtained.
The image to be sorted has multiple corresponding parameters in an encoding process, here as the benchmark of classification,
Multiple coding parameters respectively include prediction mode, Prediction Parameters, motion vector, quantization parameter, residual information, current pixel value and
Adjacent pixel values.
View of the above, it will be seen that coding parameter here be it is multiple, can therefrom selector in specific classification
Point, it also can choose whole, in general, the parameter therefrom selected is more, and gained classification is more accurate.
For example, wherein prediction mode refers in carrying out image processing process, the Image estimation after coding is originally inputted
The mode of image；Current pixel value refers to that the pixel value of the current pixel for carrying out classification processing, pixel value refer to a number
A vector in other words is organized, including colouring information and luminance information；Adjacent pixel values refer to the picture adjacent with current pixel
The pixel value of element.
S2, classified by pixel of the Intelligence Classifier to image.
Classification when, by the part or all coding parameters of all pixels of image to be sorted and each of which pixel,
As prediction mode, quantization parameter, residual information, current pixel value or adjacent pixel values are input to intelligent classification trained in advance
In device, the classification results to all pixels value are realized to get multiple pixel classifications are arrived.
Here Intelligence Classifier can be Logic Regression Models, decision tree or support vector machines, using corresponding
Sample training obtains.
It can be seen from the above technical proposal that present embodiments providing a kind of pixel classifications processing method of image, the party
Method, which specifically includes, obtains image and its multiple coding parameters to be sorted；By trained Intelligence Classifier in advance and according to image
Multiple coding parameters classify to all pixels of the image, obtain multiple pixel classifications.Since this programme is not limited to examine
Consider Gradient Features, but considers prediction mode, Prediction Parameters used in cataloged procedure, motion vector, quantization parameter, residual
Multiple encoded informations such as poor information, pixel value, to improve nicety of grading.
Fig. 2 is the flow chart of the pixel classifications processing method of another image shown according to an exemplary embodiment.
As shown in Figure 1, pixel classifications processing method provided in this embodiment is applied to server, computer or mobile terminal
Etc. in equipment, specifically include following steps.
S1, image and its multiple coding parameters to be sorted are obtained.
The image to be sorted has multiple corresponding parameters in an encoding process, here as the benchmark of classification,
Multiple coding parameters respectively include prediction mode, Prediction Parameters, motion vector, quantization parameter, residual information, current pixel value and
Adjacent pixel values.
View of the above, it will be seen that coding parameter here be it is multiple, can therefrom selector in specific classification
Point, it also can choose whole, in general, the parameter therefrom selected is more, and gained classification is more accurate.
For example, wherein prediction mode refers in carrying out image processing process, the Image estimation after coding is originally inputted
The mode of image；Current pixel value refers to that the pixel value of the current pixel for carrying out classification processing, pixel value refer to a number
A vector in other words is organized, including colouring information and luminance information；Adjacent pixel values refer to the picture adjacent with current pixel
The pixel value of element.
S11, the multiple characteristic values for extracting image to be sorted.
After obtaining image to be sorted, the multiple characteristic values of extractor.Here the gradient value of characteristic value preferred image, point
Not Wei respective image horizontal direction, vertical direction and two diagonally adjacent four gradient values.S2, pass through intelligence point
Class device classifies to the pixel of image.
Classification when, by the part or all coding parameters of all pixels of image to be sorted and each of which pixel,
As prediction mode, quantization parameter, residual information, current pixel value or adjacent pixel values are input to intelligent classification trained in advance
In device, the classification results to all pixels value are realized to get multiple pixel classifications are arrived.
Here Intelligence Classifier can be Logic Regression Models, decision tree or support vector machines, using corresponding
Sample training obtains.
When using decision tree or support vector machines as the Intelligence Classifier, the processing of pixel classifications disclosed in the present embodiment
Method further includes following steps, as shown in Fig. 2, further including as follows before being classified using Intelligence Classifier to all pixels
Step:
It can be seen from the above technical proposal that present embodiments providing a kind of pixel classifications processing method of image, the party
Method, which specifically includes, obtains image and its multiple coding parameters to be sorted；Extract multiple characteristic values of image to be sorted；Pass through
Intelligence Classifier trained in advance simultaneously classifies to all pixels of the image according to multiple coding parameters of image, obtains more
A pixel classifications.Due to this programme be not limited to consider Gradient Features, but consider prediction mode used in cataloged procedure,
Multiple encoded informations such as Prediction Parameters, motion vector, quantization parameter, residual information, pixel value, to improve nicety of grading.
Fig. 3 is the flow chart of the pixel classifications processing method of another image shown according to an exemplary embodiment.
As shown in figure 3, pixel classifications processing method provided in this embodiment is applied to server, computer or mobile terminal
Etc. in equipment, specifically include following steps.
S01, training sample set is obtained.
It includes multiple images sample that the training sample, which is concentrated, further includes the prediction in an encoding process of each image pattern
Part or complete in mode, Prediction Parameters, motion vector, quantization parameter, residual information, current pixel value and adjacent pixel values
Portion.
S02, model training is carried out according to training sample set.
Corresponding function is input to using the corresponding data that above-mentioned training sample is concentrated, is carried out in such as deep neural network
Model training, so that corresponding classifier is obtained, such as Logic Regression Models, decision tree, support vector machines or depth nerve net
Network model.
S1, image and its multiple coding parameters to be sorted are obtained.
The image to be sorted has multiple corresponding parameters in an encoding process, here as the benchmark of classification,
Multiple coding parameters respectively include prediction mode, Prediction Parameters, motion vector, quantization parameter, residual information, current pixel value and
Adjacent pixel values.
View of the above, it will be seen that coding parameter here be it is multiple, can therefrom selector in specific classification
Point, it also can choose whole, in general, the parameter therefrom selected is more, and gained classification is more accurate.
For example, wherein prediction mode refers in carrying out image processing process, the Image estimation after coding is originally inputted
The mode of image；Current pixel value refers to that the pixel value of the current pixel for carrying out classification processing, pixel value refer to a number
A vector in other words is organized, including colouring information and luminance information；Adjacent pixel values refer to the picture adjacent with current pixel
The pixel value of element.
S2, classified by pixel of the Intelligence Classifier to image.
Classification when, by the part or all coding parameters of all pixels of image to be sorted and each of which pixel,
As prediction mode, quantization parameter, residual information, current pixel value or adjacent pixel values are input to intelligent classification trained in advance
In device, the classification results to all pixels value are realized to get multiple pixel classifications are arrived.
Here Intelligence Classifier can be Logic Regression Models, decision tree, support vector machines or deep neural network
Model is obtained using corresponding sample training.
It can be seen from the above technical proposal that present embodiments providing a kind of pixel classifications processing method of image, the party
Method specifically includes acquisition training sample；Model training is carried out according to training sample set；Obtain image to be sorted and its multiple volumes
Code parameter；Classify by the Intelligence Classifier and according to multiple coding parameters of image to all pixels of the image, obtains
To multiple pixel classifications.Since this programme is not limited to consider Gradient Features, but consider prediction used in cataloged procedure
Multiple encoded informations such as mode, Prediction Parameters, motion vector, quantization parameter, residual information, pixel value, to improve classification
Precision.
Fig. 4 is a kind of block diagram of the pixel classifications processing unit of image shown according to an exemplary embodiment.
As shown in figure 4, pixel classifications processing unit provided in this embodiment is applied to server, computer or mobile terminal
Etc. in equipment, specifically include image input module 10 and classification execution module 20.
Image input module 10 is for obtaining image and its multiple coding parameters to be sorted.
The image to be sorted has multiple corresponding parameters in an encoding process, here as the benchmark of classification,
Multiple coding parameters respectively include prediction mode, Prediction Parameters, motion vector, quantization parameter, residual information, current pixel value and
Adjacent pixel values.
View of the above, it will be seen that coding parameter here be it is multiple, can therefrom selector in specific classification
Point, it also can choose whole, in general, the parameter therefrom selected is more, and gained classification is more accurate.
For example, wherein prediction mode refers in carrying out image processing process, the Image estimation after coding is originally inputted
The mode of image；Current pixel value refers to that the pixel value of the current pixel for carrying out classification processing, pixel value refer to a number
A vector in other words is organized, including colouring information and luminance information；Adjacent pixel values refer to the picture adjacent with current pixel
The pixel value of element.
Classification execution module 20 using pixel of the Intelligence Classifier to image for being classified.
Classification when, by the part or all coding parameters of all pixels of image to be sorted and each of which pixel,
As prediction mode, quantization parameter, residual information, current pixel value or adjacent pixel values are input to intelligent classification trained in advance
In device, the classification results to all pixels value are realized to get multiple pixel classifications are arrived.
Here Intelligence Classifier can be Logic Regression Models, decision tree, support vector machines or deep neural network
Model is obtained using corresponding sample training.
It can be seen from the above technical proposal that present embodiments providing a kind of pixel classifications processing unit of image, the dress
It sets to specifically include and obtains image and its multiple coding parameters to be sorted；By trained Intelligence Classifier in advance and according to image
Multiple coding parameters classify to all pixels of the image, obtain multiple pixel classifications.Since this programme is not limited to examine
Consider Gradient Features, but considers prediction mode, Prediction Parameters used in cataloged procedure, motion vector, quantization parameter, residual
Multiple encoded informations such as poor information, pixel value, to improve nicety of grading.
Fig. 5 is the block diagram of the pixel classifications processing unit of another image shown according to an exemplary embodiment.
As shown in figure 5, pixel classifications processing unit provided in this embodiment is for a upper embodiment, it is to be additionally arranged
Gradient extraction module 30.
Gradient extraction module 30 is used to carry out classifying it to all pixels using Intelligence Classifier in classification execution module 20
Before, extract multiple characteristic values of image to be sorted.
Here the gradient value of characteristic value preferred image, the respectively horizontal direction of respective image, vertical direction and two
A four diagonally adjacent gradient values.
After obtaining multiple gradient values, using above-mentioned decision tree or support vector machines and according to multiple coding parameters and more
A gradient value carries out classification processing to all pixels of image to be sorted.
Fig. 6 is the block diagram of the pixel classifications processing unit of another image shown according to an exemplary embodiment.
As shown in fig. 6, pixel classifications processing unit provided in this embodiment is additionally arranged sample for a upper embodiment
This acquisition module 40 and model training module 50.
Sample acquisition module 40 is for obtaining training sample set.
It includes multiple images sample that the training sample, which is concentrated, further includes the prediction in an encoding process of each image pattern
Part or complete in mode, Prediction Parameters, motion vector, quantization parameter, residual information, current pixel value and adjacent pixel values
Portion.
Model training module 50 is used to carry out model training using training sample set, to obtain above-mentioned intelligent classification
Device.
Corresponding function is input to using the corresponding data that above-mentioned training sample is concentrated, is carried out in such as deep neural network
Model training, so that corresponding classifier is obtained, such as Logic Regression Models, decision tree, support vector machines or depth nerve net
Network model.
The application also provides a kind of computer program, and the computer program is for executing pixel classifications as shown in Figures 1 to 3
Processing method.
Fig. 7 is the block diagram of a kind of electronic equipment shown according to an exemplary embodiment.For example, the electronic equipment 700 can
To be provided as a server.Referring to shown in Fig. 7, processing component 722 is specifically included, further comprises one or more processing
Device and memory resource represented by a memory 1932, can be by the instruction of the execution of processing component 722, example for storing
Such as application program.The application program stored in memory 732 may include it is one or more each correspond to one group
The module of instruction.In addition, processing component 722 is configured as executing instruction, to execute at pixel classifications shown in above-mentioned Fig. 1~3
Reason method.
The electronic equipment 700 can also include that a power supply module 726 is configured as executing the power supply pipe of electronic equipment 700
Reason, a wired or wireless network interface 750 are configured as electronic equipment 700 being connected to network and an input and output (I/
O) interface 758.Electronic equipment 700 can be operated based on the operating system for being stored in memory 1932, such as Windows
ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or similar.
Fig. 8 is the block diagram of another electronic equipment shown according to an exemplary embodiment.For example, electronic equipment 800 can
To be mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, Medical Devices are good for
Body equipment, personal digital assistant etc..
Referring to Fig. 8, electronic equipment 800 may include following one or more components: processing component 802, memory 804,
Electric power assembly 806, multimedia component 808, audio component 810, the interface 812 of input/output (I/O), sensor module 814,
And communication component 816.
The integrated operation of the usual controlling electronic devices 800 of processing component 802, such as with display, call, data are logical
Letter, camera operation and record operate associated operation.Processing component 802 may include one or more processors 820 to hold
Row instruction, to perform all or part of the steps of the methods described above.In addition, processing component 802 may include one or more moulds
Block, convenient for the interaction between processing component 802 and other assemblies.For example, processing component 802 may include multi-media module, with
Facilitate the interaction between multimedia component 808 and processing component 802.
Memory 804 is configured as storing various types of data to support the operation in equipment 800.These data are shown
Example includes the instruction of any application or method for operating on electronic equipment 800, contact data, telephone directory number
According to, message, picture, video etc..Memory 804 can by any kind of volatibility or non-volatile memory device or they
Combination realize, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM) is erasable
Programmable read only memory (EPROM), programmable read only memory (PROM), read-only memory (ROM), magnetic memory, quick flashing
Memory, disk or CD.
Power supply module 806 provides electric power for the various assemblies of electronic equipment 800.Power supply module 806 may include power supply pipe
Reason system, one or more power supplys and other with for electronic equipment 800 generate, manage, and distribute the associated component of electric power.
Multimedia component 808 includes the screen of one output interface of offer between the electronic equipment 800 and user.
In some embodiments, screen may include liquid crystal display (LCD) and touch panel (TP).If screen includes touch surface
Plate, screen may be implemented as touch screen, to receive input signal from the user.Touch panel includes one or more touches
Sensor is to sense the gesture on touch, slide, and touch panel.The touch sensor can not only sense touch or sliding
The boundary of movement, but also detect duration and pressure associated with the touch or slide operation.In some embodiments,
Multimedia component 808 includes a front camera and/or rear camera.When equipment 800 is in operation mode, as shot mould
When formula or video mode, front camera and/or rear camera can receive external multi-medium data.Each preposition camera shooting
Head and rear camera can be a fixed optical lens system or have focusing and optical zoom capabilities.
Audio component 810 is configured as output and/or input audio signal.For example, audio component 810 includes a Mike
Wind (MIC), when electronic equipment 800 is in operation mode, when such as call mode, recording mode, and voice recognition mode, microphone
It is configured as receiving external audio signal.The received audio signal can be further stored in memory 804 or via logical
Believe that component 816 is sent.In some embodiments, audio component 810 further includes a loudspeaker, is used for output audio signal.
I/O interface 812 provides interface between processing component 802 and peripheral interface module, and above-mentioned peripheral interface module can
To be keyboard, click wheel, button etc..These buttons may include, but are not limited to: home button, volume button, start button and lock
Determine button.
Sensor module 814 includes one or more sensors, for providing the state of various aspects for electronic equipment 800
Assessment.For example, sensor module 814 can detecte the state that opens/closes of equipment 800, the relative positioning of component, such as institute
The display and keypad that component is electronic equipment 800 are stated, sensor module 814 can also detect electronic equipment 800 or electronics
The position change of 800 1 components of equipment, the existence or non-existence that user contacts with electronic equipment 800,800 orientation of electronic equipment
Or the temperature change of acceleration/deceleration and electronic equipment 800.Sensor module 814 may include proximity sensor, be configured to
It detects the presence of nearby objects without any physical contact.Sensor module 814 can also include optical sensor, such as
CMOS or ccd image sensor, for being used in imaging applications.In some embodiments, which can be with
Including acceleration transducer, gyro sensor, Magnetic Sensor, pressure sensor or temperature sensor.
Communication component 816 is configured to facilitate the communication of wired or wireless way between electronic equipment 800 and other equipment.
Electronic equipment 800 can access the wireless network based on communication standard, such as WiFi, carrier network (such as 2G, 3G, 4G or 5G),
Or their combination.In one exemplary embodiment, communication component 816 receives via broadcast channel and comes from external broadcasting management
The broadcast singal or broadcast related information of system.In one exemplary embodiment, the communication component 816 further includes that near field is logical
(NFC) module is believed, to promote short range communication.For example, radio frequency identification (RFID) technology, infrared data association can be based in NFC module
Meeting (IrDA) technology, ultra wide band (UWB) technology, bluetooth (BT) technology and other technologies are realized.
In the exemplary embodiment, electronic equipment 800 can be by one or more application specific integrated circuit (ASIC), number
Word signal processor (DSP), digital signal processing appts (DSPD), programmable logic device (PLD), field programmable gate array
(FPGA), controller, microcontroller, microprocessor or other electronic components are realized, for executing pixel as shown in Figures 1 to 3
Classification processing method.
In the exemplary embodiment, a kind of non-transitorycomputer readable storage medium including instruction, example are additionally provided
It such as include the memory 804 of instruction, above-metioned instruction can be executed by the processor 820 of electronic equipment 800 to complete the above method.Example
Such as, the non-transitorycomputer readable storage medium can be ROM, random access memory (RAM), CD-ROM, tape, soft
Disk and optical data storage devices etc..
Those skilled in the art after considering the specification and implementing the invention disclosed here, will readily occur to of the invention its
Its embodiment.This application is intended to cover any variations, uses, or adaptations of the invention, these modifications, purposes or
Person's adaptive change follows general principle of the invention and including the undocumented common knowledge in the art of the disclosure
Or conventional techniques.The description and examples are only to be considered as illustrative, and true scope and spirit of the invention are by following
Claim is pointed out.
It should be understood that the present invention is not limited to the precise structure already described above and shown in the accompanying drawings, and
And various modifications and changes may be made without departing from the scope thereof.The scope of the present invention is limited only by the attached claims.
Claims (10)
1. a kind of pixel classifications processing method of image is applied to image processing system characterized by comprising
Obtain image and its multiple coding parameters to be sorted；
According to multiple coding parameters of described image, by Intelligence Classifier trained in advance to all pixels of described image into
Row classification, obtains multiple pixel classifications.
2. pixel classifications processing method as described in claim 1, which is characterized in that the multiple coding parameter includes prediction mould
Some or all of in formula, Prediction Parameters, motion vector, quantization parameter, residual information, current pixel value and adjacent pixel values.
3. pixel classifications processing method as described in claim 1, which is characterized in that the Intelligence Classifier is logistic regression mould
Type, decision tree or support vector machines.
4. pixel classifications processing method as claimed in claim 3, which is characterized in that join according to multiple codings of described image
Number classifies to all pixels of described image by Intelligence Classifier trained in advance, before obtaining multiple pixel classifications,
Further include:
Extract multiple characteristic values of described image；
Multiple coding parameters according to described image are carried out by all pixels of the Intelligence Classifier to described image
Classification, obtains multiple pixel classifications, comprising:
Own according to the multiple coding parameter and the multiple characteristic value, and by the Intelligence Classifier to described image
Pixel is classified, and the multiple pixel classifications are obtained.
5. pixel classifications method as described in claim 1, which is characterized in that further include:
Training sample set is obtained, the training sample set includes the prediction mould of multiple images sample and each described image sample
Some or all of formula, Prediction Parameters, motion vector, quantization parameter, residual information, current pixel value and adjacent pixel values；
Model training is carried out according to the training sample set, obtains the classifier.
6. a kind of pixel classifications processing unit of image is applied to image processing system characterized by comprising
Image input module is configured as obtaining image and its multiple coding parameters to be sorted；
Classification execution module, is configured as multiple coding parameters according to described image, passes through Intelligence Classifier trained in advance
Classify to all pixels of described image, obtains multiple pixel classifications.
7. pixel classifications processing unit as claimed in claim 6, which is characterized in that the multiple coding parameter includes prediction mould
Some or all of in formula, quantization parameter, residual information, current pixel value and adjacent pixel values.
8. pixel classifications processing unit as claimed in claim 6, which is characterized in that the Intelligence Classifier is logistic regression mould
Type, decision tree or support vector machines.
9. a kind of electronic equipment characterized by comprising
Processor；
Memory for storage processor executable instruction；
Wherein, the processor is configured to executing pixel classifications processing method as claimed in any one of claims 1 to 5.
10. a kind of non-transitorycomputer readable storage medium, when the instruction in the storage medium is by the processing of mobile terminal
When device executes, so that mobile terminal is able to carry out pixel classifications processing method as claimed in any one of claims 1 to 5.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104519361A (en) * | 2014-12-12 | 2015-04-15 | 天津大学 | Video steganography analysis method based on space-time domain local binary pattern |
| CN107046639A (en) * | 2016-10-31 | 2017-08-15 | 上海大学 | HEVC code stream quality prediction models based on content |
| CN108134937A (en) * | 2017-12-21 | 2018-06-08 | 西北工业大学 | A kind of compression domain conspicuousness detection method based on HEVC |
-
2018
- 2018-11-05 CN CN201811309278.1A patent/CN109583477B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104519361A (en) * | 2014-12-12 | 2015-04-15 | 天津大学 | Video steganography analysis method based on space-time domain local binary pattern |
| CN107046639A (en) * | 2016-10-31 | 2017-08-15 | 上海大学 | HEVC code stream quality prediction models based on content |
| CN108134937A (en) * | 2017-12-21 | 2018-06-08 | 西北工业大学 | A kind of compression domain conspicuousness detection method based on HEVC |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112514364A (en) * | 2019-11-29 | 2021-03-16 | 深圳市大疆创新科技有限公司 | Image signal processing apparatus, image signal processing method, camera, and movable platform |
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