CN109769143A - Video image processing method, video image processing device, video system, video equipment and storage medium - Google Patents
Video image processing method, video image processing device, video system, video equipment and storage medium Download PDFInfo
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Abstract
The invention relates to a video image processing method, a video image processing device, a remote video system, a computer device and a storage medium, wherein a second terminal acquires a video image generated by a first terminal, the second terminal optimizes the image quality of the video image from a first image quality to a second image quality by using an image quality optimization model, the image quality optimization model is a model obtained by machine learning of the image quality corresponding relation of a standard scene image, the image quality corresponding relation is the corresponding relation between the first image quality and the second image quality, the standard scene image is an image matched with the video scene of the video image, the second terminal can display the video image with the image quality optimized to the second image quality, so that the video image displayed by the second terminal is not easily influenced by a network state, even if the first terminal transmits the video image with the first image quality to the second terminal in a state of low network bandwidth, the second terminal can still display the video image with the image quality better than the first image quality.
Description
Technical field
The present invention relates to technical field of image processing, processing method, video image more particularly to a kind of video image
Processing unit, remote video system, computer equipment and computer readable storage medium.
Background technique
With the rapid development of image processing techniques, the efficiency that computer equipment handles image is also higher and higher,
Allow users to the sharing video frequency image in the application scenarios of multiplicity.By taking the application scenarios of teleconference as an example, Yong Huke
To initiate Remote Video Conference, pass through transmitting terminal by the video image real-time Transmission at meeting scene to the receiving end of remote user into
Row display.
However, the transmitting terminal needs of video image are dynamically adjusted according to real-time detection network state in traditional technology
The transmission quality of whole video image, so video image is sent to receiving end by transmitting terminal when being shown, what receiving end was shown
The image quality of video image will receive the influence of network state, such as when network bandwidth is in high quality, transmitting terminal can be transmitted
For the video image of high quality to receiving end, and when network bandwidth is in low quality, transmitting terminal then can be by low-quality video figure
It is shown as being transferred to receiving end, the image quality for the video image for causing receiving end to show is relatively low.
Summary of the invention
Based on this, it is necessary to which, in traditional technology, the image quality for the video image that receiving end is shown is easy by network-like
The technical issues of influence of state, provides a kind of processing method of video image, the processing unit of video image, long-distance video system
System, computer equipment and computer readable storage medium.
A kind of processing method of video image, comprising steps of
Obtain the video image that first terminal generates；
Using image quality optimization model by the image quality of the video image from the first image quality optimization be the second image quality；Wherein, institute
Stating image quality optimization model is to carry out the model that machine learning obtains to the image quality corresponding relationship of standard scene image；The image quality pair
It should be related to for the corresponding relationship of first image quality and the second image quality；The standard scene image is the view with the video image
The image that frequency scene matches；
Display image quality is the video image of second image quality.
A kind of processing unit of video image, comprising:
Module is obtained, for obtaining the video image of first terminal generation；
Optimization module, for using image quality optimization model by the image quality of the video image from the first image quality optimization be second
Image quality；Wherein, the image quality optimization model is to carry out the mould that machine learning obtains to the image quality corresponding relationship of standard scene image
Type；The image quality corresponding relationship is the corresponding relationship of first image quality and the second image quality；The standard scene image for institute
State the image that the video scene of video image matches；
Display module is the video image of second image quality for display image quality.
A kind of remote video system, comprising: transmitting terminal, server and receiving end；
The transmitting terminal, for video image to be sent to the server；
The server, for the video image to be forwarded to the receiving end；
The receiving end, the video image sent for receiving the server, using image quality optimization model by institute
It is the second image quality that the image quality of video image, which is stated, from the first image quality optimization, and the video image of second image quality is shown；Its
In, the image quality optimization model is to carry out the model that machine learning obtains to the image quality corresponding relationship of standard scene image；It is described
Image quality corresponding relationship is the corresponding relationship of first image quality and the second image quality；The standard scene image be and the video figure
The image that the video scene of picture matches.
A kind of computer equipment, including processor and memory, the memory are stored with computer program, the processing
Device realizes following steps when executing the computer program:
Obtain the video image that first terminal generates；Using image quality optimization model by the image quality of the video image from first
Image quality optimization is the second image quality；Wherein, the image quality optimization model is to carry out machine to the image quality corresponding relationship of standard scene image
The model that device learns；The image quality corresponding relationship is the corresponding relationship of first image quality and the second image quality；The standard
Scene image is the image to match with the video scene of the video image；Display image quality is the video figure of second image quality
Picture.
A kind of computer readable storage medium, is stored thereon with computer program, and the computer program is held by processor
Following steps are realized when row:
Obtain the video image that first terminal generates；Using image quality optimization model by the image quality of the video image from first
Image quality optimization is the second image quality；Wherein, the image quality optimization model is to carry out machine to the image quality corresponding relationship of standard scene image
The model that device learns；The image quality corresponding relationship is the corresponding relationship of first image quality and the second image quality；The standard
Scene image is the image to match with the video scene of the video image；Display image quality is the video figure of second image quality
Picture.
Processing method, device, remote video system, computer equipment and the storage medium of above-mentioned video image, second eventually
End obtains the video image that first terminal generates, and second terminal is using image quality optimization model by the image quality of the video image from first
Image quality optimization is the second image quality, which is to carry out machine learning to the image quality corresponding relationship of standard scene image to obtain
The model arrived, and image quality corresponding relationship is the corresponding relationship between the first image quality and the second image quality, standard scene image is and this
The image that the video scene of video image matches, the image quality optimization model can by video image from the first image quality optimization be the
Two image quality, then second terminal can show the video image that image quality optimization is the second image quality, so that second terminal is aobvious
The video image shown is not readily susceptible to the influence of network state, even if first terminal is drawn first in the state of low network band width
The video image of matter is sent to second terminal, and second terminal still is able to the video image that display image quality is better than the first image quality.
Detailed description of the invention
Fig. 1 is the application scenario diagram of the processing method of video image in one embodiment；
Fig. 2 is the flow diagram of the processing method of video image in one embodiment；
Fig. 3 is the signaling diagram of the processing method of video image in one embodiment；
Fig. 4 is the structural block diagram of the processing unit of video image in one embodiment；
Fig. 5 is the structural block diagram of one embodiment medium-long range video system；
Fig. 6 is the internal structure chart of computer equipment in one embodiment.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that specific embodiment described herein is only used to explain the present invention, not
For limiting the present invention.
It should be noted that term involved in the embodiment of the present invention " first second " be only be similar pair of difference
As not representing the particular sorted for object, it is possible to understand that ground, " first second " can be interchanged specific in the case where permission
Sequence or precedence.It should be understood that the object that " first second " is distinguished is interchangeable under appropriate circumstances, so as to retouch here
The embodiment of the present invention stated can be performed in other sequences than those illustrated or described herein.
The processing method of video image provided by the invention, can be applied in application scenarios as shown in Figure 1, and Fig. 1 is
The application scenario diagram of the processing method of video image in one embodiment, wherein first terminal 100 can pass through network and second
Terminal 200 is communicatively coupled.Wherein, first terminal 100 and second terminal 200 can be, but not limited to be various individual calculus
Machine, laptop, smart phone and tablet computer.
When sending video image, first terminal 100 can correspond to transmitting terminal, and second terminal 200 can correspond to connect
Video image can be sent to second terminal 200 by network by receiving end, first terminal 100, and second terminal 200 can receive
It is shown after to video image.In this way, user can pass through first terminal in the application scenarios such as such as teleconference
The video image at 100 real-time recording meeting scenes, while the video image at meeting scene is passed through into network using first terminal 100
It is transferred to second terminal 200 to be shown, the user of second terminal 200 is carried out remotely by the second terminal 200
Meeting.However, certain fluctuation may occur for network state, such as by original in the process of transmission of video images
High network quality bandwidth status (network flow high speed, stable or packet loss are few) become low quality network bandwidth state (flow low speed,
Stability difference or packet loss are big) when, first terminal 100 would generally first reduce original image quality of video image, then by the video figure
As being transferred to second terminal 200, to adapt to current low quality network bandwidth state, and second terminal 200 is receiving reduction
It after the video image of image quality, if the video image shown, is easy to cause the image quality of display relatively low, influences long-range
The quality of meeting also influences the perception of user.
The processing method for the video image that various embodiments of the present invention provide, the available first terminal 100 of second terminal 200
The image quality of the video image of generation, the video image is the first image quality, and then second terminal 200 can use image quality optimization model
By the image quality of the video image from the first image quality optimization be the second image quality, wherein the image quality optimization model be to standard scene figure
The image quality corresponding relationship of picture carries out the model that machine learning obtains, and image quality corresponding relationship is the correspondence of the first image quality and the second image quality
Relationship, standard scene image are the image to match with the video scene of video image, and then second terminal 200 can be by image quality
Video image for the second image quality is shown, so that the video image that second terminal 200 is shown is not readily susceptible to current network
The influence of state, even if it is the first image quality that video image, which is the image quality that first terminal 100 is sent in the state of low network band width,
Video image, second terminal 200 still be able to display image quality be better than first image quality video image so that such as remotely meeting
In the application scenarios such as view, the user of second terminal 200 can watch image quality preferably video image.
In one embodiment, a kind of processing method of video image is provided, is in one embodiment with reference to Fig. 2, Fig. 2
The flow diagram of the processing method of video image is applied to be illustrated for the second terminal 200 in Fig. 1 in this way,
The processing method of the video image may comprise steps of:
Step S101 obtains the video image that first terminal generates.
In this step, the video image of the available first terminal 100 of second terminal 200 generation.Wherein, first terminal
100 video images that can be used for needing to share by user, and the transmitting terminal as video image, whole by network and second
After communication connection is established at end 200, which is sent to second terminal 200.Video image can be captured in real-time or record
The video image of system, by taking the application scenarios of teleconference as an example, meeting presider can obtain meeting by first terminal 100 in real time
The video image at scene is discussed, for first terminal 100 by the transmission of video images to second terminal 200, the quantity of second terminal 200 can
Be it is multiple, then the available video image of each second terminal 200 carries out respective handling.
Step S102, using image quality optimization model by the image quality of video image from the first image quality optimization be the second image quality.
This step is mainly that second terminal 200 can use preconfigured image quality optimization model and produce first terminal 100
The image quality of raw video image is the second image quality from the first image quality optimization.Wherein, image quality optimization model refers to standard scene figure
The image quality corresponding relationship of picture carries out the model that machine learning obtains, and image quality corresponding relationship refers to pair of the first image quality and the second image quality
It should be related to, and standard scene image is the image to match with the video scene of video image.
Image quality optimization model, can be to the image quality pair of standard scene image before the image quality to video image optimizes
It should be related to carry out deep learning, i.e., image quality optimization model is to the standard under the standard scene image and the second image quality under the first image quality
The corresponding relationship of scene image carries out deep learning, enables the image quality optimization model after training by the pattern field of the first image quality
Scape image optimization is the standard scene image of the second image quality, and the video image that standard scene image and first terminal 100 are sent
Video scene match, the image quality of the video image is the first image quality, it is trained in this way after image quality optimization model also can be right
The image quality of video image that first terminal 100 is sent optimizes, by the image quality of the video image from the first image quality optimization be the
Two image quality.
Wherein, video image has image content, and image content can reflect out the video scene of the video image, with remote
It is illustrated for Cheng Huiyi, in the application scenarios of teleconference, the video image that first terminal 100 generates is meeting scene
Video image, and image content is exactly the picture material shown in the video image, and such as live participant is in picture
Position, the article ornaments at meeting scene and the environment at meeting scene etc., which, which sees, can reflect out the video image
It is to shoot or record under video scene, for teleconference, it will usually it is carried out in a relatively-stationary conference scenario,
It is relatively fixed to refer to that in video image shooting or recording process too big variation, the view of teleconference occur for the conference scenario
The video scene of frequency image is usually some meeting room, and the picture at meeting room scene is usually opposing stationary, therefore, can be with the meeting
The image of room is discussed as standard scene image, to the image quality of video image of the first terminal 100 during teleconference carries out
Before optimizing, the meeting room can be shot or recorded in the image of a variety of image quality as standard scene image, including first
The standard scene image of image quality and the standard scene image of the second image quality, then can be excellent as image quality using neural network model
Change model to instruct come the corresponding relationship between the standard scene image of standard scene image and the second image quality to the first image quality
Practice, and video scene i.e. certain meeting room for the video image that standard scene image and first terminal 100 are sent matches, after training
Image quality optimization model the video image that image quality is the first image quality can be optimized for the video image of the second image quality.
It is understood that if when being in an opposing stationary picture of the video image that first terminal 100 is sent,
The video scene of the video image is also relatively fixed, in this way, second terminal 200 can be in the item for not increasing network bandwidth requirement
It is high-quality video figure by the low quality video image restoring that image quality optimization model sends over first terminal 100 under part
Picture, even if so that first terminal 100 is influenced the video image of original high quality carrying out a degree of compression by network
The second terminal 200 is transmitted again, which can be also reduced to the video image of high quality by second terminal 200.
Step S103, display image quality are the video image of the second image quality.
This step is mainly that the image quality of video image is by second terminal 200 from the first image quality optimization in image quality optimization model
After second image quality, the video image of available second image quality is simultaneously shown, so that the user of second terminal 200 can be with
The content of the video image is viewed by the second terminal 200, and the image quality of the video image passes through optimization, so that second
The video image that terminal 200 is shown is not readily susceptible to the influence of current network state, shows that the video image of high quality is conducive to
User accurately grasps the information of video image, can also improve user to the perception of video image.
The processing method of above-mentioned video image, second terminal obtain the video image that first terminal generates, second terminal benefit
With image quality optimization model by the image quality of the video image from the first image quality optimization be the second image quality, the image quality optimization model be to mark
The image quality corresponding relationship of quasi- scene image carries out the model that machine learning obtains, and image quality corresponding relationship is the first image quality and second
Corresponding relationship between image quality, standard scene image are the images to match with the video scene of the video image, and the image quality is excellent
Changing model can be the second image quality from the first image quality optimization by video image, and then image quality optimization can be second by second terminal
The video image of image quality is shown, so that the video image that second terminal is shown is not readily susceptible to the influence of network state, i.e.,
Make in the state of low network band width first terminal that the video image of the first image quality is sent to second terminal, second terminal is still
Can display image quality be better than the first image quality video image.
In one embodiment, video image can be first terminal for the image quality of video image from the second image quality boil down to
The video image that first image quality obtains.
In the present embodiment, for first terminal 100 before by transmission of video images to second terminal 200, first terminal 100 can
First to compress the image quality of the video image, there are many kinds of the modes for compressing the image quality of the video image, such as reducing should
The resolution ratio of video image reduces frame per second etc., and the image quality of the video image is compressed to the first image quality from the second image quality, will
Image quality is the transmission of video images of the first image quality to second terminal 200.The present embodiment first compresses the image quality of video image
It transmits again, since the image quality of the video image can be the second original image quality by the first image quality optimization by second terminal 200, because
This this mode can ensure 200 display image quality of second terminal preferably the video image of the second image quality while, also reduce
The network bandwidth that the video image occupies in transmission process.
In one embodiment, which can be the video figure that first terminal transmits under low network band width state
Picture.
In the present embodiment, first terminal 100 can be given the transmission of video images of generation in the state of low network band width
Second terminal 200.Wherein, low network band width state refers to that current network bandwidth is in low-quality state, such as network flow
The states such as low speed, stability are poor, packet loss is big, under the low network band width state, first terminal 100 is difficult to such as fine definition
Etc. high quality transmission of video images to second terminal 200, so first terminal 100 can be by the lower video figure of image quality
As being transferred to second terminal 200 in the state of low network band width, second terminal 200 restores the lower video image of the image quality
It is shown for image quality preferably video image.Specifically, first terminal 100 first can will in the state of low network band width
The image quality of video image is transferred to from preferably second the first image quality of image quality boil down to, then under current low network band width state
Second terminal 200, so that second terminal 200 still is able to view more excellent image quality under the interactive scene of low network band width
Long-distance video image.
In one embodiment, the step of video image of acquisition first terminal generation may include:
Obtain the video image that server is sent；The server is used to receive the video image of first terminal transmission, and will
The video image is sent to corresponding receiving end.
In the present embodiment, second terminal 200 can obtain the video image that first terminal 100 generates by server.Ginseng
Fig. 3 is examined, Fig. 3 is the signaling diagram of the processing method of video image in one embodiment, the view that first terminal 100 can be generated
Frequency image is sent to server 300, and server 300 can receive the video image of the first terminal 100 transmission, then server
300 can be sent to the video image corresponding receiving end, and receiving end refers to the terminal for receiving the video image, and connects
Receive the terminal of the video image quantity may include it is multiple, which can be sent to specified by first terminal 100
Video image is such as sent to second terminal 200 by receiving end, and first terminal 100 can be by the video image and second terminal
200 terminal iidentification is sent to server 200, and video image can be transmitted to second according to the terminal iidentification by server 200
Terminal 200, so that second terminal 200 can receive the video image and optimize to the image quality of the video image, by image quality
Video image after optimization is shown.The scheme of the present embodiment can apply the application in multiple transmitting terminals to multiple receiving ends
In scene, server 300 can be realized the scheduling to each end stream medium data, the video image that transmitting terminal is sent
It is forwarded to corresponding receiving end by server 300, image quality optimization processing is carried out to video image on the receive side, reduces transmission
The network bandwidth that video image occupies.
In one embodiment, after the step of obtaining the video image that first terminal generates, can also include:
Obtain the video scene of video image；The determining standard scene image to match with video scene；It is more from what is prestored
Image quality optimization model corresponding with standard scene image is extracted in a image quality optimization model.
In the present embodiment, second terminal 200 can be after the video image for receiving the generation of first terminal 100, from the view
Video scene is extracted in the video pictures of frequency image, after determining the video scene of the video image, the available and video
The standard scene image that scene matches.By taking the application scenarios of teleconference as an example, if the video image is in the field of meeting room A
The video image of real-time recording under scape, then after receiving the video image, the available video scene is second terminal 200
Then the scene of meeting room A can obtain the standard scene image to match with the scene of meeting room A from database, should
Standard scene image can be the image of the meeting room A shot in advance, due to video scene may include it is a variety of, such as in difference
The video scene for the video image recorded in meeting room is different, so standard scene image also may include a variety of, various standards
Scene image matches from different video scenes respectively, as standard scene image A corresponds to meeting room A, standard scene image B
Correspond to meeting room C etc. corresponding to meeting room B and standard scene image C.
Second terminal 200 can be by multiple image quality optimization models respectively to the corresponding pass of the image quality of different standard scene images
System is trained, and enables multiple image quality optimization models after training that the video image progress image quality of different video scene is excellent
Change.Then, video image, and the pattern field that the video scene for being determined at the video image matches are received in second terminal 200
After scape image, image quality corresponding with the standard scene image can be extracted from preparatory trained multiple image quality optimization models
Optimized model to carry out image quality optimization to the video image, thus realize to the image quality of the video image under different video scene into
Row optimization processing.
In one embodiment, can with comprising steps of
Obtain history video image；The history video image is the figure that first terminal is sent under high network bandwidth state
Picture；The image quality of the history video image is not less than the second image quality；The video scene of history video image and the video of video image
Scene is identical；Standard scene image is obtained according to history video image.
The present embodiment is mainly that second terminal 200 is gone through according to what first terminal 100 was sent in the state of high network bandwidth
History video image obtains standard scene image.Wherein, first terminal 100 can be under the network state of high network bandwidth, will be high
The video image of quality is sent to second terminal 200, and second terminal 200 can such as be stored every 10 frames with certain frequency and be received
The video image of the high quality arrived, then second terminal 200 obtains standard scene image according to the video image of the high quality, the
Two terminals 200 can be using the video image of all high quality received as standard video image, and by these normal videos
Image as image quality optimization model training data training the image quality optimization model, enable training after image quality optimization model
Low-quality video image is reduced to the video image of high quality.
Specifically, first terminal 100 can be by the transmission of video images of high quality under the network state of high network bandwidth
It is shown to second terminal 200, however when network state becomes low network band width from high network bandwidth, first terminal 100
It can be that low-quality video image is transmitted with adapting to current network state by the video image compression of original high quality, this
When, the video image that second terminal 200 can send first terminal 100 under the network state of high network bandwidth is as history
Video image, the image quality of the history video image are not less than the second image quality, and the video scene of the history video image and the
The video scene for the video image that one terminal 100 is sent is identical, that is to say, that the video for the video image that first terminal 100 is sent
Scene is identical before and after network state becomes low network band width from high network bandwidth, and second terminal 200 can be by history video
Image is as standard scene image.In this manner it is possible to first first terminal 100 is sent under the network state of high network bandwidth
High-quality video image is stored as history video image, and using the history video image as standard scene image to image quality optimization
Model is trained, so that when network state becomes low network band width, using the image quality optimization model by the view of the first image quality
Frequency image restoring is the second image quality, finally renders by the picture of the video image of the second image quality after reduction again, to guarantee
The image quality for the video image that user watches in second terminal 200 is not readily susceptible to the influence of current network state fluctuation.
In one embodiment, using image quality optimization model by the image quality of video image from the step of being optimized for the second image quality
May include:
For image quality optimization model, optimization threshold value corresponding with the second image quality is set；The image quality optimization model can be using volume
Product neural network model；Video image is inputted into image quality optimization model, triggers the image quality optimization model according to optimization threshold value for the
One image quality optimization is the second image quality.
Mainly optimization threshold value corresponding with the second image quality can be arranged for image quality optimization model in the present embodiment, so that drawing
Matter Optimized model can according to the optimization threshold value by the image quality of video image from the first image quality optimization be the second image quality.Wherein, excellent
Change threshold value and refer to the parameter for the target image quality optimization model to the degree of optimization of image quality, for different optimization threshold values, draws
Matter Optimized model is different to the degree of optimization of the image quality of video image, and in general, optimization threshold value is higher, to the picture of video image
The degree of optimization of matter is higher, which can correspond to the standard scene figure of the video image after image quality optimization and high quality
Similarity between picture, optimization threshold value it is higher, the standard scene image of video image and high quality after image quality optimization it
Between similarity it is also higher.
In the present embodiment, optimization threshold corresponding with the second image quality can be arranged for image quality optimization model in second terminal 200
Value, then the video image that image quality is the first image quality is input to image quality optimization model by second terminal 200, so that the image quality optimization
Model exports the video image of the second image quality according to the optimization threshold value.It is understood that when second terminal 200 needs to export not
When the image quality of ad eundem, different optimization threshold values can be correspondingly set, allow image quality optimization model according to the excellent of setting
Change the video image of threshold value output respective level image quality.
Convolutional neural networks model can passed through to video using convolutional neural networks model as image quality optimization model
It can be multiple picture units by video image cutting, such as by video image cutting be more when the image quality of image optimizes
Each picture unit is input in convolutional neural networks model and carries out image quality optimization, so by the picture unit of a 16 × 9 pixel
The picture unit after each image quality optimization is stitched together again afterwards, forms the video image of high quality.By by video image into
Row cutting can be improved the efficiency of image quality reduction to the mode that each picture unit carries out image quality optimization.
In one embodiment, a kind of processing unit of video image is provided, is in one embodiment with reference to Fig. 4, Fig. 4
The processing unit of the structural block diagram of the processing unit of video image, the video image may include:
Module 101 is obtained, for obtaining the video image of first terminal generation；
Optimization module 102, for using image quality optimization model by the image quality of video image from the first image quality optimization be second
Image quality；Wherein, image quality optimization model is to carry out the model that machine learning obtains to the image quality corresponding relationship of standard scene image；It draws
Matter corresponding relationship is the corresponding relationship of the first image quality and the second image quality；Standard scene image is the video scene phase with video image
Matched image；
Display module 103 is the video image of the second image quality for display image quality.
In one embodiment, video image can be first terminal for the image quality of video image from the second image quality boil down to
The video image that first image quality obtains.
In one embodiment, video image can be the video figure that first terminal transmits under low network band width state
Picture.
In one embodiment, module 101 is obtained to be further used for:
Obtain the video image that server is sent；The server is used to receive the video image of first terminal transmission, and will
The video image is sent to corresponding receiving end.
In one embodiment, can also include:
Scene acquiring unit, for obtaining the video scene of video image；
Scene determination unit, for the determining standard scene image to match with video scene；
Model extraction unit, it is corresponding with standard scene image for being extracted from the multiple image quality optimization models prestored
Image quality optimization model.
In one embodiment, can also include:
First image acquisition unit, for obtaining history video image；The history video image is first terminal in high net
The image sent under network bandwidth status；The image quality of the history video image is not less than the second image quality；The video of history video image
Scene is identical as the video scene of video image；
Second image acquisition unit, for obtaining standard scene image according to history video image.
In one embodiment, optimization module 102 is further used for:
For image quality optimization model, optimization threshold value corresponding with the second image quality is set；The image quality optimization model can be using volume
Product neural network model；Video image is inputted into image quality optimization model, triggers the image quality optimization model according to optimization threshold value for the
One image quality optimization is the second image quality.
The processing unit of video image of the invention and the processing method of video image of the invention correspond, about view
The specific of the processing unit of frequency image limits the restriction that may refer to the processing method above for video image, in above-mentioned view
Technical characteristic and its advantages that the embodiment of the processing method of frequency image illustrates are suitable for the processing unit of video image
Embodiment in, details are not described herein.Modules in the processing unit of above-mentioned video image can be fully or partially through soft
Part, hardware and combinations thereof are realized.Above-mentioned each module can be embedded in the form of hardware or independently of the processing in computer equipment
It in device, can also be stored in a software form in the memory in computer equipment, in order to which processor calls execution above each
The corresponding operation of a module.
In one embodiment, a kind of remote video system is provided, is one embodiment medium-long range with reference to Fig. 5, Fig. 5
The structural block diagram of video system, the remote video system may include: transmitting terminal 400, server 300 and receiving end 500；
Transmitting terminal 400, for video image to be sent to server 300；Server 300, for forwarding video image
To receiving end 500；Receiving end 500, for receiving the video image of the transmission of server 300, using image quality optimization model by video
The image quality of image is the second image quality from the first image quality optimization, and the video image of the second image quality is shown；Wherein, image quality optimization
Model is to carry out the model that machine learning obtains to the image quality corresponding relationship of standard scene image；Image quality corresponding relationship is the first picture
The corresponding relationship of matter and the second image quality；Standard scene image is the image to match with the video scene of video image.
In the present embodiment, video image can be sent to server 300, server 300 by the transmitting terminal 400 of video image
After receiving the video image, which is forwarded to corresponding receiving end 500, receiving end 500 can use image quality
Optimized model by the image quality of received video image from the first image quality optimization be the second image quality, and by the video figure of second image quality
As being shown.Wherein, the quantity of transmitting terminal 400 and receiving end 500 can be multiple, and server 300 can be used to implement not
Receiving end 2, receiving end are forwarded to the stream media scheduling between transmitting terminal and receiving end, such as by the video image of transmitting terminal 1
M, the video image of transmitting terminal 2 is forwarded to receiving end 1 etc., the program can be applied in multiple transmitting terminals to multiple receiving ends
Application scenarios in, server 300 can be realized the scheduling to each end stream medium data so that transmitting terminal send video figure
As corresponding receiving end can be forwarded to by server 300, image quality optimization processing is carried out to video image on the receive side, is subtracted
The network bandwidth that few transmitting video image occupies.
In one embodiment, transmitting terminal is further used for drawing the image quality of video image from the second image quality boil down to first
The video image that image quality is the first image quality is sent to receiving end by server under low network band width state by matter.
In the present embodiment, when network state is in low network band width state, transmitting terminal 400 can be by the picture of video image
Then the video image of first image quality is sent to server 300, server 300 from second the first image quality of image quality boil down to by matter
The video image of first image quality is transmitted to receiving end 500 again, so that receiving end 500 optimizes the video image of the first image quality
Video image for the second image quality is shown, so that receiving end 500 still is able under the interactive scene of low network band width
View the long-distance video image of more excellent image quality.
In one embodiment, transmitting terminal 400 is also used to that history video image is passed through clothes under high network bandwidth state
Business device 300 is sent to receiving end 500；The image quality of history video image is not less than the second image quality；The video field of history video image
Scape is identical as the video scene of video image；Receiving end 500 is also used to receive history video image, is obtained according to history video image
Take standard scene image.
In the present embodiment, when network state is in high network bandwidth state, transmitting terminal 400 can be by the video of high quality
Image is sent to server 300, and the video image of the high quality is transmitted to receiving end 500 by server 300, and receiving end 500 connects
It receives the video image of the high quality and is stored as history video image, the image quality of the history video image is not less than second
Image quality, and the video scene of the history video image is identical as the video scene of video image, in this way, receiving end 500 can be with
It is trained using the history video image as the training data of image quality optimization model, when network state is from high network bandwidth state
When becoming low network band width state, receiving end 500 can use that trained image quality optimization model sends transmitting terminal 400
The video image of one image quality is optimized to the video image of the second image quality, and the video image of second image quality is shown, with
Guarantee that the image quality for the video image that user watches on receiving end 500 is not readily susceptible to the influence of current network state fluctuation.
In one embodiment, a kind of computer equipment is provided, which can be terminal, internal structure
Figure can using as shown in fig. 6, Fig. 6 as the internal structure chart of computer equipment in one embodiment.The computer equipment includes passing through
Processor, memory, network interface, display screen and the input unit of system bus connection.Wherein, the processing of the computer equipment
Device is for providing calculating and control ability.The memory of the computer equipment includes non-volatile memory medium, built-in storage.It should
Non-volatile memory medium is stored with operating system and computer program.The built-in storage is the behaviour in non-volatile memory medium
The operation for making system and computer program provides environment.The network interface of the computer equipment is used to pass through net with external terminal
Network connection communication.A kind of processing method of video image is realized when the computer program is executed by processor.The computer is set
Standby display screen can be liquid crystal display or electric ink display screen, and the input unit of the computer equipment can be display
The touch layer covered on screen is also possible to the key being arranged on computer equipment shell, trace ball or Trackpad, can also be outer
Keyboard, Trackpad or mouse for connecing etc..
It will be understood by those skilled in the art that structure shown in Fig. 6, only part relevant to the present invention program is tied
The block diagram of structure does not constitute the restriction for the computer equipment being applied thereon to the present invention program, specific computer equipment
It may include perhaps combining certain components or with different component layouts than more or fewer components as shown in the figure.
In one embodiment, a kind of computer equipment, including processor and memory, the memory storage are provided
There is computer program, the processor performs the steps of when executing the computer program
Obtain the video image that first terminal generates；Using image quality optimization model by the image quality of video image from the first image quality
It is optimized for the second image quality；Wherein, which is to carry out machine learning to the image quality corresponding relationship of standard scene image
Obtained model；Image quality corresponding relationship is the corresponding relationship of the first image quality and the second image quality；Standard scene image be and video figure
The image that the video scene of picture matches；Display image quality is the video image of the second image quality.
In one embodiment, video image can be first terminal for the image quality of video image from the second image quality boil down to
The video image that first image quality obtains.
In one embodiment, video image can be the video figure that first terminal transmits under low network band width state
Picture.
In one embodiment, it is also performed the steps of when processor executes computer program and obtains what server was sent
Video image；The server is used to receive the video image of first terminal transmission, and the video image is sent to and is connect accordingly
Receiving end.
In one embodiment, the view for obtaining video image is also performed the steps of when processor executes computer program
Frequency scene；The determining standard scene image to match with video scene；It extracts and marks from the multiple image quality optimization models prestored
The corresponding image quality optimization model of quasi- scene image.
In one embodiment, it is also performed the steps of when processor executes computer program and obtains history video image；
The history video image is the image that first terminal is sent under high network bandwidth state；The image quality of the history video image is not low
In the second image quality；The video scene of history video image and the video scene of video image are identical；It is obtained according to history video image
Take standard scene image.
In one embodiment, it also performs the steps of when processor executes computer program and is set for image quality optimization model
Set optimization threshold value corresponding with the second image quality；The image quality optimization model can use convolutional neural networks model；By video figure
As input image quality optimization model, triggering the image quality optimization model according to optimization threshold value is the second image quality by the first image quality optimization.
Above-mentioned computer equipment, by the computer program run on the processor, so that the view that second terminal is shown
Frequency image is not readily susceptible to the influence of network state, even if first terminal is by the view of the first image quality in the state of low network band width
Frequency image is sent to second terminal, and second terminal still is able to the video image that display image quality is better than the first image quality.
Those of ordinary skill in the art will appreciate that realizing the processing side of video image described in as above any one embodiment
All or part of the process in method is relevant hardware can be instructed to complete by computer program, the computer
Program can be stored in a non-volatile computer read/write memory medium, and the computer program is when being executed, it may include as above
State the process of the embodiment of each method.Wherein, to memory, storage, number used in each embodiment provided by the present invention
According to any reference of library or other media, non-volatile and/or volatile memory may each comprise.Nonvolatile memory can wrap
Include read-only memory (ROM), programming ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM
(EEPROM) or flash memory.Volatile memory may include random access memory (RAM) or external cache.Make
To illustrate rather than limit to, RAM is available in many forms, such as static state RAM (SRAM), dynamic ram (DRAM), synchronous dram
(SDRAM), double data rate sdram (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronization link (Synchlink) DRAM
(SLDRAM), memory bus (Rambus) directly RAM (RDRAM), direct memory bus dynamic ram (DRDRAM) and
Memory bus dynamic ram (RDRAM) etc..
Accordingly, a kind of computer readable storage medium is provided in one embodiment, is stored thereon with computer program,
It is performed the steps of when computer program is executed by processor
Obtain the video image that first terminal generates；Using image quality optimization model by the image quality of video image from the first image quality
It is optimized for the second image quality；Wherein, which is to carry out machine learning to the image quality corresponding relationship of standard scene image
Obtained model；Image quality corresponding relationship is the corresponding relationship of the first image quality and the second image quality；Standard scene image be and video figure
The image that the video scene of picture matches；Display image quality is the video image of the second image quality.
In one embodiment, video image can be first terminal for the image quality of video image from the second image quality boil down to
The video image that first image quality obtains.
In one embodiment, video image can be the video figure that first terminal transmits under low network band width state
Picture.
In one embodiment, it is also performed the steps of when computer program is executed by processor and obtains server transmission
Video image；The server is used to receive the video image of first terminal transmission, and the video image is sent to accordingly
Receiving end.
In one embodiment, it is also performed the steps of when computer program is executed by processor and obtains video image
Video scene；The determining standard scene image to match with video scene；From the multiple image quality optimization models prestored extract with
The corresponding image quality optimization model of standard scene image.
In one embodiment, it is also performed the steps of when computer program is executed by processor and obtains history video figure
Picture；The history video image is the image that first terminal is sent under high network bandwidth state；The image quality of the history video image
Not less than the second image quality；The video scene of history video image and the video scene of video image are identical；According to history video figure
As obtaining standard scene image.
In one embodiment, it is also performed the steps of when computer program is executed by processor as image quality optimization model
Optimization threshold value corresponding with the second image quality is set；The image quality optimization model can use convolutional neural networks model；By video
Image inputs image quality optimization model, and triggering the image quality optimization model according to optimization threshold value is the second image quality by the first image quality optimization.
Above-mentioned computer readable storage medium, by the computer program that it is stored, so that the video that second terminal is shown
Image is not readily susceptible to the influence of network state, even if first terminal is by the video of the first image quality in the state of low network band width
Image is sent to second terminal, and second terminal still is able to the video image that display image quality is better than the first image quality.
Each technical characteristic of above embodiments can be combined arbitrarily, for simplicity of description, not to above-described embodiment
In each technical characteristic it is all possible combination be all described, as long as however, the combination of these technical characteristics be not present lance
Shield all should be considered as described in this specification.
The embodiments described above only express several embodiments of the present invention, and the description thereof is more specific and detailed, but simultaneously
It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to protection of the invention
Range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.
Claims (13)
1. a kind of processing method of video image, which is characterized in that comprising steps of
Obtain the video image that first terminal generates；
Using image quality optimization model by the image quality of the video image from the first image quality optimization be the second image quality；Wherein, the picture
Matter Optimized model is to carry out the model that machine learning obtains to the image quality corresponding relationship of standard scene image；The image quality is corresponding to close
System is the corresponding relationship of first image quality and the second image quality；The standard scene image is the video field with the video image
The image that scape matches；
Display image quality is the video image of second image quality.
2. the processing method of video image according to claim 1, which is characterized in that the video image is described first
The video image that terminal obtains the image quality of the video image from the first image quality of the second image quality boil down to.
3. the processing method of video image according to claim 2, which is characterized in that the video image is described first
The video image that terminal is transmitted under low network band width state.
4. the processing method of video image according to claim 1, which is characterized in that described to obtain what first terminal generated
The step of video image includes:
Obtain the video image that server is sent；The server is used to receive the video that the first terminal is sent
Image, and the video image is sent to corresponding receiving end.
5. the processing method of video image according to claim 1, which is characterized in that generated in the acquisition first terminal
Video image the step of after, further includes:
Obtain the video scene of the video image；
The determining standard scene image to match with the video scene；
Image quality optimization model corresponding with the standard scene image is extracted from the multiple image quality optimization models prestored.
6. the processing method of video image according to claim 1, which is characterized in that further comprise the steps of:
Obtain history video image；The history video image is the figure that the first terminal is sent under high network bandwidth state
Picture；The image quality of the history video image is not less than second image quality；The video scene of the history video image with it is described
The video scene of video image is identical；
The standard scene image is obtained according to the history video image.
7. the processing method of video image according to claim 1, which is characterized in that described to utilize image quality optimization model
The image quality of the video image from including: the step of being optimized for the second image quality
For the image quality optimization model, optimization threshold value corresponding with second image quality is set；The image quality optimization model uses
Convolutional neural networks model；
The video image is inputted into the image quality optimization model, triggering the image quality optimization model will according to the optimization threshold value
First image quality optimization is second image quality.
8. a kind of processing unit of video image characterized by comprising
Module is obtained, for obtaining the video image of first terminal generation；
Optimization module, for being drawn the image quality of the video image for second from the first image quality optimization using image quality optimization model
Matter；Wherein, the image quality optimization model is to carry out the model that machine learning obtains to the image quality corresponding relationship of standard scene image；
The image quality corresponding relationship is the corresponding relationship of first image quality and the second image quality；The standard scene image be and the view
The image that the video scene of frequency image matches；
Display module is the video image of second image quality for display image quality.
9. a kind of remote video system characterized by comprising transmitting terminal, server and receiving end；
The transmitting terminal, for video image to be sent to the server；
The server, for the video image to be forwarded to the receiving end；
The receiving end, the video image sent for receiving the server, using image quality optimization model by the view
The image quality of frequency image is the second image quality from the first image quality optimization, and the video image of second image quality is shown；Wherein, institute
Stating image quality optimization model is to carry out the model that machine learning obtains to the image quality corresponding relationship of standard scene image；The image quality pair
It should be related to for the corresponding relationship of first image quality and the second image quality；The standard scene image is the view with the video image
The image that frequency scene matches.
10. remote video system according to claim 9, which is characterized in that
The transmitting terminal is further used for the image quality of the video image from the first image quality of the second image quality boil down to, will
Image quality is that the video image of first image quality is sent to the receiving end by the server under low network band width state.
11. remote video system according to claim 9, which is characterized in that
The transmitting terminal is also used under high network bandwidth state for history video image being sent to by the server described
Receiving end；The image quality of the history video image is not less than second image quality；The video scene of the history video image with
The video scene of the video image is identical；
The receiving end is also used to receive the history video image, obtains the pattern field according to the history video image
Scape image.
12. a kind of computer equipment, including processor and memory, the memory are stored with computer program, feature exists
In the processor realizes the processing side of the described in any item video images of claim 1 to 7 when executing the computer program
The step of method.
13. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the computer program
The step of processing method of the described in any item video images of claim 1 to 7 is realized when being executed by processor.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111405234A (en) * | 2020-04-17 | 2020-07-10 | 杭州大轶科技有限公司 | Video conference information system and method with integration of cloud computing and edge computing |
| CN112399128A (en) * | 2020-11-16 | 2021-02-23 | 明磊 | Video communication system and method based on big data |
| CN112702558A (en) * | 2020-12-23 | 2021-04-23 | 联想(北京)有限公司 | Data processing method and device |
| CN112929703A (en) * | 2019-12-06 | 2021-06-08 | 上海海思技术有限公司 | Method and device for processing code stream data |
| CN113592733A (en) * | 2021-07-22 | 2021-11-02 | 北京小米移动软件有限公司 | Image processing method, image processing device, storage medium and electronic equipment |
| CN115202551A (en) * | 2021-04-13 | 2022-10-18 | 广州视源电子科技股份有限公司 | Picture display method, device, storage medium and equipment |
| CN116647635A (en) * | 2023-07-27 | 2023-08-25 | 深圳市乗名科技有限公司 | Remote desktop conference system and method based on deep learning |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140136686A1 (en) * | 2012-11-09 | 2014-05-15 | Institute For Information Industry | Dynamic resolution regulating system and dynamic resolution regulating method |
| CN104322065A (en) * | 2012-06-28 | 2015-01-28 | 宇龙计算机通信科技(深圳)有限公司 | Terminal and video image compression method |
| CN105812711A (en) * | 2016-05-05 | 2016-07-27 | 广东小天才科技有限公司 | Method and system for optimizing image quality in video call process |
| CN106534942A (en) * | 2016-11-04 | 2017-03-22 | 微鲸科技有限公司 | Video image quality adjusting method and apparatus |
| CN106792263A (en) * | 2016-12-09 | 2017-05-31 | 东方网力科技股份有限公司 | A kind of method of transmitting video data, apparatus and system |
| CN106791927A (en) * | 2016-12-23 | 2017-05-31 | 福建帝视信息科技有限公司 | A kind of video source modeling and transmission method based on deep learning |
| CN107659828A (en) * | 2017-10-30 | 2018-02-02 | 广东欧珀移动通信有限公司 | Video image quality adjustment method, device, terminal device and storage medium |
| CN108681436A (en) * | 2018-03-13 | 2018-10-19 | 广东欧珀移动通信有限公司 | Image quality parameter adjusting method, device, terminal and storage medium |
| CN108810649A (en) * | 2018-07-12 | 2018-11-13 | 深圳创维－Rgb电子有限公司 | Picture quality regulation method, intelligent TV set and storage medium |
-
2019
- 2019-02-03 CN CN201910108693.9A patent/CN109769143A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104322065A (en) * | 2012-06-28 | 2015-01-28 | 宇龙计算机通信科技(深圳)有限公司 | Terminal and video image compression method |
| US20140136686A1 (en) * | 2012-11-09 | 2014-05-15 | Institute For Information Industry | Dynamic resolution regulating system and dynamic resolution regulating method |
| CN105812711A (en) * | 2016-05-05 | 2016-07-27 | 广东小天才科技有限公司 | Method and system for optimizing image quality in video call process |
| CN106534942A (en) * | 2016-11-04 | 2017-03-22 | 微鲸科技有限公司 | Video image quality adjusting method and apparatus |
| CN106792263A (en) * | 2016-12-09 | 2017-05-31 | 东方网力科技股份有限公司 | A kind of method of transmitting video data, apparatus and system |
| CN106791927A (en) * | 2016-12-23 | 2017-05-31 | 福建帝视信息科技有限公司 | A kind of video source modeling and transmission method based on deep learning |
| CN107659828A (en) * | 2017-10-30 | 2018-02-02 | 广东欧珀移动通信有限公司 | Video image quality adjustment method, device, terminal device and storage medium |
| CN108681436A (en) * | 2018-03-13 | 2018-10-19 | 广东欧珀移动通信有限公司 | Image quality parameter adjusting method, device, terminal and storage medium |
| CN108810649A (en) * | 2018-07-12 | 2018-11-13 | 深圳创维－Rgb电子有限公司 | Picture quality regulation method, intelligent TV set and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 柴宏: "视频图像处理和网络视频技术的研究与应用", 《信息通信》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112929703A (en) * | 2019-12-06 | 2021-06-08 | 上海海思技术有限公司 | Method and device for processing code stream data |
| CN111405234A (en) * | 2020-04-17 | 2020-07-10 | 杭州大轶科技有限公司 | Video conference information system and method with integration of cloud computing and edge computing |
| CN112399128A (en) * | 2020-11-16 | 2021-02-23 | 明磊 | Video communication system and method based on big data |
| CN112702558A (en) * | 2020-12-23 | 2021-04-23 | 联想(北京)有限公司 | Data processing method and device |
| CN115202551A (en) * | 2021-04-13 | 2022-10-18 | 广州视源电子科技股份有限公司 | Picture display method, device, storage medium and equipment |
| CN113592733A (en) * | 2021-07-22 | 2021-11-02 | 北京小米移动软件有限公司 | Image processing method, image processing device, storage medium and electronic equipment |
| CN116647635A (en) * | 2023-07-27 | 2023-08-25 | 深圳市乗名科技有限公司 | Remote desktop conference system and method based on deep learning |
| CN116647635B (en) * | 2023-07-27 | 2023-11-28 | 深圳市乗名科技有限公司 | Remote desktop conference system and method based on deep learning |
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