CN109302587A - Image transfer method and image transmission - Google Patents
Image transfer method and image transmission Download PDFInfo
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- CN109302587A CN109302587A CN201811278747.8A CN201811278747A CN109302587A CN 109302587 A CN109302587 A CN 109302587A CN 201811278747 A CN201811278747 A CN 201811278747A CN 109302587 A CN109302587 A CN 109302587A
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- video
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Classifications
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/222—Studio circuitry; Studio devices; Studio equipment
- H04N5/262—Studio circuits, e.g. for mixing, switching-over, change of character of image, other special effects ; Cameras specially adapted for the electronic generation of special effects
Abstract
The application provides image transfer method and image transmission, and method includes: that will be reduced to the video same or similar with the parameter of key frame by parameter video adjusted by deep learning algorithm training neural network model；It shoots and is setup parameter by the parameter regulation of taken shooting video；Key frame is set by the setting frame in the shooting video in setting time；Transmit the shooting video and key frame of setup parameter；After the shooting video and key frame that receive setup parameter, the shooting video of setup parameter is adjusted to the video same or similar with the parameter of key frame according to the parameter of the key frame of shooting video by neural network model.When the application is able to solve video or bigger image data because of transmission, so that transmission speed is slow and some images for needing to handle in real time and video you can't get real-time processing problems, and it when the mode for transmitting image and video is 2G/3G/4G, can also avoid the problem that generating a large amount of campus network.
Description
Technical field
This application involves field of image processing more particularly to image transfer methods and image transmission.
Background technique
In the prior art, filming apparatus can all shoot a large amount of video and image, some filming apparatus can will take
Video and image are uploaded to other terminals such as cloud server etc..If transmission video and image data amount it is bigger when
It waits, then may occupy the bandwidth of transmission, so that transmission speed is slow, especially some images for needing to handle in real time and video be just
It is unable to get real-time processing.There are also be exactly that, if user is to transmit image and video using 2G/3G/4G, can generate
A large amount of campus network.
Summary of the invention
The application provides a kind of image transfer method and image transmission, can remove automatic Pilot system in the prior art from
System perception training need to buy great amount of images data and manually to original picture carry out information labeling so as to cause inefficiency,
The problem of at high cost and mark is easy error.
According to a first aspect of the present application, the application provides a kind of image transfer method, and method includes: to pass through deep learning
Algorithm trains neural network model, wherein neural network model can will be adjusted according to the parameter of the key frame of video by parameter
Video afterwards is reduced to the video same or similar with the parameter of key frame；Shoot and adjust the ginseng of the shooting video taken
Number is the processing video of setup parameter, wherein parameter includes vedio color and/or image resolution ratio；By the bat in setting time
The setting frame taken the photograph in video is set as key frame；Transmission process video and key frame；Receive processing video and key frame
Afterwards, neural network model according to shooting video key frame parameter will handle video be adjusted to it is identical as the parameter of key frame or
The similar video of person.
Preferably, in the step of training neural network model by deep learning algorithm, comprising: by the parameter tune of video
Section is setting vedio color and/or the video of image resolution ratio；Set crucial for the setting frame in the video in setting time
Frame；Video, key frame and setting vedio color and/or the video of image resolution ratio is intensive by the training of deep learning algorithm
Through network model.
Preferably, after the step of shooting and adjusting the shooting video taken, further includes: in identification shooting video
Information of vehicles, vehicle location information in the picture and the corresponding license board information of vehicle；In transmission process video and key
In the step of frame, further includes: the location information and vehicle of transmission process video, key frame, information of vehicles, vehicle in the picture
Corresponding license board information.
Preferably, it after the step of being adjusted to the video same or similar with the parameter of key frame, further comprises the steps of:
According to information of vehicles, vehicle location information in the picture and the corresponding license board information of vehicle, license board information is corresponded into vehicle
Location information in the picture is shown.
Preferably, setup parameter is black-white colors.
According to the second aspect of application, the application provides a kind of image transmission, and device includes: training module, is used for
Pass through deep learning algorithm training neural network model, wherein neural network model can be according to the parameter of the key frame of video, will
The video same or similar with the parameter of key frame is reduced to by parameter video adjusted；Parameter adjustment module is used for
It shoots and is setup parameter by the parameter regulation of taken shooting video, wherein parameter includes vedio color and/or image
Resolution ratio；Key frame setup module, for setting key frame for the setting frame in the shooting video in setting time；Transmit mould
Block is used for transmission the shooting video and key frame of setup parameter；Video recovery module, for receiving the shooting of setup parameter
And after key frame, the shooting of setup parameter is adjusted to and is closed according to the parameter of the key frame of shooting video by neural network model
The same or similar video of the parameter of key frame.
Preferably, training module includes: adjusting unit, for by the parameter regulation of video be setting vedio color and/or
The video of image resolution ratio；Setup unit, for setting key frame for the setting frame in the video in setting time；Training is single
Member, for video, key frame and setting vedio color and/or the video of image resolution ratio to be passed through the training of deep learning algorithm
Neural network model out.
Preferably, image transmission further include: identification module shoots information of vehicles, vehicle in video for identification
The corresponding license board information of location information and vehicle in the picture；Transmission module is also used to transmit the shooting view of setup parameter
Frequently, key frame, information of vehicles, vehicle location information in the picture and the corresponding license board information of vehicle.
Preferably, image transmission further include: information display module is used for according to information of vehicles, vehicle in the picture
Location information and the corresponding license board information of vehicle, license board information is corresponded into the location information of vehicle in the picture and is shown
Show.
Preferably, setup parameter is black-white colors.
The beneficial effects of the present application are as follows:.
Detailed description of the invention
Fig. 1 is the flow chart of the image transfer method of one embodiment of the application；And
Fig. 2 is the schematic diagram of the image transmission of another embodiment of the application.
Specific embodiment
The present invention is further described with exemplary embodiment with reference to the accompanying drawing, the examples of the embodiments are attached
It is shown in figure, in which the same or similar labels are throughly indicated same or similar element or there is same or like function
Element.The embodiments described below with reference to the accompanying drawings are exemplary, for explaining only the invention, and cannot be construed to pair
Limitation of the invention.In addition, if the detailed description of known technology is for showing the invention is characterized in that unnecessary, then by it
It omits.
Those skilled in the art of the present technique are appreciated that unless expressly stated, singular " one " used herein, " one
It is a ", " described " and "the" may also comprise plural form.It is to be further understood that being arranged used in specification of the invention
Taking leave " comprising " is
Refer to there are the feature, integer, step, operation, element and/or component, but it is not excluded that in the presence of or addition one
Other a or multiple features, integer, step, operation, element, component and/or their group.It should be understood that when we claim element
It is " connected " or when " coupled " to another element, it can be directly connected or coupled to other elements, or there may also be in
Between element.In addition, " connection " used herein or " coupling " may include being wirelessly connected or wirelessly coupling.Wording used herein
"and/or" includes one or more associated wholes for listing item or any cell and all combinations.
Those skilled in the art of the present technique are appreciated that unless otherwise defined, all terms used herein (including technology art
Language and scientific term), there is meaning identical with the general understanding of those of ordinary skill in fields of the present invention.Should also
Understand, those terms such as defined in the general dictionary, it should be understood that have in the context of the prior art
The consistent meaning of meaning, and unless idealization or meaning too formal otherwise will not be used by specific definitions as here
To explain.
Referring to Fig. 1, a kind of image transfer method of the application, method include:
Step S101: pass through deep learning algorithm training neural network model, wherein neural network model can be according to video
Key frame parameter, the video same or similar with the parameter of key frame will be reduced to by parameter video adjusted.
Training neural network model the step of include:
Step S1011: being the video for setting vedio color and/or image resolution ratio by the parameter regulation of video.This implementation
In example, video is adjusted to the video of black-white colors, alternatively, video is adjusted to 480P from image resolution ratio 1080P, alternatively,
The resolution ratio and vedio color of video will be adjusted simultaneously, be adjusted to 480P from image resolution ratio 1080P and adjusts video face
Color is black-white colors.
Step S1012: key frame is set by the setting frame in the video in setting time.In the present embodiment, when setting
Between can be set in 30 seconds, using the frame in video the 15th second as the key frame of video.
Step S1013: video, key frame and setting vedio color and/or the video of image resolution ratio are passed through into depth
Learning algorithm trains neural network model.
It is the processing video of setup parameter that step S102:, which shooting, and adjusts the parameter of the shooting video taken, wherein ginseng
Number includes vedio color and/or image resolution ratio.In the present embodiment, video is adjusted to the video of black-white colors, alternatively, will view
Frequency is adjusted to 480P from image resolution ratio 1080P, alternatively, the resolution ratio and vedio color that video will be adjusted simultaneously, from image
Resolution ratio 1080P is adjusted to 480P and adjusts vedio color be black-white colors.
Step S103: key frame is set by the setting frame in the shooting video in setting time.In the present embodiment, it will clap
It takes the photograph video and is divided into multiple video clips, setting time is 30 seconds, then shooting video is divided into the video clip of multiple 30 seconds durations.
Using the frame in the video clip of 30 seconds durations the 15th second as the key frame of video.
Step S104: transmission process video and key frame.In the present embodiment, video that filming apparatus can will take
Cloud server is transmitted to by wifi/2G/3G/4G.Because video have passed through the processing of adjustment parameter, video is allowed in this way
Data volume in transmission process is smaller, can soon transmit over, and can save flow and space.
Step S105: after receiving processing video and key frame, key frame of the neural network model according to shooting video
Parameter by handle video be adjusted to the video same or similar with the parameter of key frame.
Further, after step S105, the information of vehicles in identification shooting video is further comprised the steps of:, vehicle is being schemed
Location information and the corresponding license board information of vehicle as in.In the present embodiment, shooting can be identified by image recognition technology
Information of vehicles, vehicle in video location information in the picture and the corresponding license board information of vehicle.
Therefore, in the transmission process video and key frame the step of, further includes: transmission process video, key frame, vehicle
Information, vehicle location information in the picture and the corresponding license board information of vehicle.
Further, after step S105, the position letter in the picture according to information of vehicles, vehicle is further comprised the steps of:
Breath and the corresponding license board information of vehicle, correspond to the location information of vehicle in the picture for license board information and show.Also it is both
It says, the position of vehicle in the picture can be determined by image recognition technology, then the license board information recognized is added to
The variation of vehicle is followed in the picture and is changed.
Correspondingly, the function modoularization thinking according to computer software, referring to Fig. 2, the application proposes that a kind of image passes
Defeated device, device include:
Training module 201, for training neural network model by deep learning algorithm, according to the ginseng of the key frame of video
Number will be reduced to the video same or similar with the parameter of key frame by parameter video adjusted.
Training module 201 includes:
Unit is adjusted, for being the video for setting vedio color and/or image resolution ratio by the parameter regulation of video.This reality
It applies in example, video is adjusted to the video of black-white colors, alternatively, shooting video is adjusted to 480P from image resolution ratio 1080P,
Alternatively, the resolution ratio and vedio color that will adjust simultaneously video, are adjusted to 480P from image resolution ratio 1080P and adjust view
Frequency color is black-white colors.
Setup unit, for setting key frame for the setting frame in the video in setting time.In the present embodiment, setting
Time can be set in 30 seconds, using the frame in video the 15th second as the key frame of video.
Training unit, for video, key frame and setting vedio color and/or the video of image resolution ratio to be passed through depth
Degree learning algorithm trains neural network model.
Parameter adjustment module 202, for shooting and being setup parameter by the parameter regulation of taken shooting video,
In, parameter includes vedio color and/or image resolution ratio.
In the present embodiment, video is adjusted to the video of black-white colors, alternatively, by video from image resolution ratio 1080P tune
Section is 480P, alternatively, the resolution ratio and vedio color that will adjust simultaneously video, are adjusted to 480P from image resolution ratio 1080P
And adjusting vedio color is black-white colors.
Key frame setup module 203, for setting key frame for the setting frame in the shooting video in setting time.
In the present embodiment, shooting video is divided into multiple video clips, setting time is 30 seconds, then is divided into shooting video
The video clip of multiple 30 seconds durations.Using the frame in the video clip of 30 seconds durations the 15th second as the key of video
Frame.
Transmission module 204 is used for transmission the shooting video and key frame of setup parameter.
In the present embodiment, the video taken can be transmitted to cloud service by wifi/2G/3G/4G by filming apparatus
Device.Because video have passed through the processing of adjustment parameter, it is smaller to allow for data volume of the video in transmission process in this way, can be with
The soon transmission past, and flow and space can be saved.
Video recovery module 205, after the shooting and the key frame that receive setup parameter, neural network model is according to shooting
The shooting of setup parameter is adjusted to the video same or similar with the parameter of key frame by the parameter of the key frame of video.
Further, image transmission further includes identification module, shoots information of vehicles, vehicle in video for identification
Location information and the corresponding license board information of vehicle in the picture.In the present embodiment, it can be known by image recognition technology
It Pai She not information of vehicles in video, vehicle location information in the picture and the corresponding license board information of vehicle.
Transmission module is also used to: transmitting the shooting video of setup parameter, key frame, information of vehicles, vehicle in the picture
Location information and the corresponding license board information of vehicle.
Further, image transmission further includes information display module, is used for according to information of vehicles, vehicle in image
In the corresponding license board information of location information and vehicle, license board information is corresponded into the location information of vehicle in the picture and is shown
Show.It also is both that the position of vehicle in the picture can be determined by image recognition technology, then believe the license plate recognized
Breath, which is added to, to be followed the variation of vehicle in the picture and changes.
The working principle of the application is described in detail below with reference to Fig. 1 and Fig. 2.
By training deep learning algorithm training neural network model, wherein neural network model can be according to video
The parameter of key frame will be reduced to the video same or similar with the parameter of key frame by parameter video adjusted.
It shoots and is setup parameter by the parameter regulation of taken shooting video, wherein parameter includes video face
Color and/or image resolution ratio.For example, be the video of black-white colors by the color adaptation for shooting video, and/or, by video image
Resolution ratio is adjusted to 480P from 1080P.
Set key frame for the setting frame in the shooting video in setting time, for example, shooting video when a length of 30
Second, select a 15th second frame as key frame.Transmit shooting and the key frame of setup parameter.
After the shooting video and key frame that receive setup parameter, key frame of the neural network model according to shooting video
Parameter the shooting video of setup parameter is adjusted to the video same or similar with the parameter of key frame.
The disclosure proposes a kind of computer readable storage medium, and computer-readable recording medium storage has computer program,
The step of image transfer method as described above is realized when computer program is executed by processor.
If module/unit of the equipment be realized in the form of SFU software functional unit and as independent product sale or
In use, can store in a computer readable storage medium.Based on this understanding, the present invention realizes above-mentioned implementation
All or part of the process in example method, can also instruct relevant hardware to complete, the meter by computer program
Calculation machine program can be stored in a computer readable storage medium, the computer program when being executed by processor, it can be achieved that on
The step of stating each embodiment of the method.Wherein, the computer program includes computer program code, the computer program generation
Code can be source code form, object identification code form, executable file or certain intermediate forms etc..The computer-readable medium
It may include: any entity or device, recording medium, USB flash disk, mobile hard disk, magnetic that can carry the computer program code
Dish, CD, computer storage, read-only memory (ROM, Read-Only Memory), random access memory (RAM,
Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium etc..It should be noted that described
The content that computer-readable medium includes can carry out increasing appropriate according to the requirement made laws in jurisdiction with patent practice
Subtract, such as does not include electric carrier signal and electricity according to legislation and patent practice, computer-readable medium in certain jurisdictions
Believe signal.
The beneficial effects of the present application are as follows: by being setup parameter by the parameter regulation of taken video, will set
The setting frame in shooting video in time is set as key frame, then transmits the shooting video and key frame of setup parameter,
After the shooting and key frame for receiving setup parameter, neural network model will be set according to the parameter of the key frame of shooting video
The shooting video for determining parameter is adjusted to the view same or similar with the parameter of key frame, and this makes it possible to the views solved because of transmission
When frequency or image data are bigger, so that transmission speed is slow and some images for needing to handle in real time and video just can not
Real-time processing problem is obtained, is also exactly to avoid the problem that generating a large amount of campus network.
It will be understood by those skilled in the art that all or part of the steps of various methods can pass through in above embodiment
Program instructs related hardware to complete, which can be stored in a computer readable storage medium, storage medium can wrap
It includes: read-only memory, random access memory, disk or CD etc..
The foregoing is a further detailed description of the present application in conjunction with specific implementation manners, and it cannot be said that this Shen
Specific implementation please is only limited to these instructions.For those of ordinary skill in the art to which this application belongs, it is not taking off
Under the premise of from the present application design, a number of simple deductions or replacements can also be made.
Claims (10)
1. a kind of image transfer method, which is characterized in that the described method includes:
Pass through deep learning algorithm training neural network model, wherein the neural network model can be according to the key frame of video
Parameter, the video same or similar with the parameter of the key frame will be reduced to by parameter video adjusted；
It shoots and the parameter for adjusting the shooting video taken is the processing video of setup parameter, wherein the parameter includes view
Frequency color and/or image resolution ratio；
Key frame is set by the setting frame in the shooting video in setting time；
Transmit the processing video and key frame；
After receiving the processing video and key frame, the neural network model is according to the key frame for shooting video
The processing video is adjusted to the video same or similar with the parameter of the key frame by parameter.
2. image transfer method as described in claim 1, which is characterized in that pass through deep learning algorithm training nerve described
In the step of network model, comprising:
It is the video for setting vedio color and/or image resolution ratio by the parameter regulation of the video；
Key frame is set by the setting frame in the video in setting time；
The video, key frame and setting vedio color and/or the video of image resolution ratio are passed through into the depth
It practises algorithm and trains the neural network model.
3. image transfer method as described in claim 1, which is characterized in that in the shooting and adjust the shooting view taken
After the step of frequency, further includes: information of vehicles, vehicle location information in the picture and institute in the identification shooting video
State the corresponding license board information of vehicle；
In the transmission process video and key frame the step of, further includes: the transmission processing video, key frame, vehicle
Information, the vehicle location information in the picture and the corresponding license board information of the vehicle.
4. image transfer method as claimed in claim 3, which is characterized in that be adjusted to identical as the parameter of key frame described
Or it after the step of similar video, further comprises the steps of:
It, will according to the information of vehicles, the vehicle location information in the picture and the corresponding license board information of the vehicle
The license board information corresponds to the location information of the vehicle in the picture and is shown.
5. image transfer method as described in claim 1, which is characterized in that the setup parameter is black-white colors.
6. a kind of image transmission, which is characterized in that described device includes:
Training module, for passing through deep learning algorithm training neural network model, wherein the neural network model can basis
The parameter of the key frame of video will be reduced to same or similar with the parameter of the key frame by parameter video adjusted
Video；
Parameter adjustment module, for shooting and being setup parameter by the parameter regulation of taken shooting video, wherein described
Parameter includes vedio color and/or image resolution ratio；
Key frame setup module, for setting key frame for the setting frame in the shooting video in setting time；
Transmission module is used for transmission the shooting video and key frame of the setup parameter；
Video recovery module, after shooting and the key frame for receiving the setup parameter, the neural network model according to
The shooting of the setup parameter is adjusted to according to the parameter of the key frame of the shooting video identical as the parameter of the key frame
Or similar video.
7. image transmission as claimed in claim 6, which is characterized in that the training module includes:
Unit is adjusted, for being the video for setting vedio color and/or image resolution ratio by the parameter regulation of the video；
Setup unit, for setting key frame for the setting frame in the video in setting time；
Training unit, for leading to the video, key frame and setting vedio color and/or the video of image resolution ratio
It crosses the deep learning algorithm and trains the neural network model.
8. image transmission as claimed in claim 6, which is characterized in that described image transmitting device further include:
Identification module, for identification information of vehicles in the shooting video, vehicle location information in the picture and described
The corresponding license board information of vehicle；
Transmission module is also used to transmit the shooting video of the setup parameter, key frame, information of vehicles, the vehicle in image
In location information and the corresponding license board information of the vehicle.
9. image transmission as claimed in claim 8, which is characterized in that described image transmitting device further include: information is aobvious
Show module, for the location information and the corresponding license plate of the vehicle according to the information of vehicles, the vehicle in the picture
The license board information is corresponded to the location information of the vehicle in the picture and shown by information.
10. image transmission as claimed in claim 6, which is characterized in that the setup parameter is black-white colors.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811278747.8A CN109302587A (en) | 2018-10-30 | 2018-10-30 | Image transfer method and image transmission |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811278747.8A CN109302587A (en) | 2018-10-30 | 2018-10-30 | Image transfer method and image transmission |
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| CN109302587A true CN109302587A (en) | 2019-02-01 |
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| US20140093185A1 (en) * | 2012-09-28 | 2014-04-03 | Luhong Liang | Apparatus, system, and method for multi-patch based super-resolution from an image |
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| CN106373402A (en) * | 2015-07-21 | 2017-02-01 | 南京海印通信设备有限公司 | Wireless-communication-transmission-based license plate monitoring management system and method |
| CN108022212A (en) * | 2017-11-24 | 2018-05-11 | 腾讯科技（深圳）有限公司 | High-resolution pictures generation method, generating means and storage medium |
| CN108376386A (en) * | 2018-03-23 | 2018-08-07 | 深圳天琴医疗科技有限公司 | A kind of construction method and device of the super-resolution model of image |
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2018
- 2018-10-30 CN CN201811278747.8A patent/CN109302587A/en not_active Withdrawn
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5153925A (en) * | 1989-04-27 | 1992-10-06 | Canon Kabushiki Kaisha | Image processing apparatus |
| US20140093185A1 (en) * | 2012-09-28 | 2014-04-03 | Luhong Liang | Apparatus, system, and method for multi-patch based super-resolution from an image |
| CN103863213A (en) * | 2012-12-13 | 2014-06-18 | 上海汽车集团股份有限公司 | Automobile information management device and automobile information display method thereof |
| CN106373402A (en) * | 2015-07-21 | 2017-02-01 | 南京海印通信设备有限公司 | Wireless-communication-transmission-based license plate monitoring management system and method |
| CN108022212A (en) * | 2017-11-24 | 2018-05-11 | 腾讯科技（深圳）有限公司 | High-resolution pictures generation method, generating means and storage medium |
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Application publication date: 20190201 |