CN110708592B - Video content complementing and cutting method and system based on user quality evaluation - Google Patents
Video content complementing and cutting method and system based on user quality evaluation Download PDFInfo
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- 238000013441 quality evaluation Methods 0.000 title claims abstract description 23
- 238000000034 method Methods 0.000 title claims abstract description 18
- 238000012545 processing Methods 0.000 claims abstract description 12
- 238000001303 quality assessment method Methods 0.000 claims description 3
- 238000013473 artificial intelligence Methods 0.000 abstract description 11
- 230000005540 biological transmission Effects 0.000 description 4
- 238000013135 deep learning Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/44—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
- H04N21/462—Content or additional data management, e.g. creating a master electronic program guide from data received from the Internet and a Head-end, controlling the complexity of a video stream by scaling the resolution or bit-rate based on the client capabilities
- H04N21/4621—Controlling the complexity of the content stream or additional data, e.g. lowering the resolution or bit-rate of the video stream for a mobile client with a small screen
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/60—Network structure or processes for video distribution between server and client or between remote clients; Control signalling between clients, server and network components; Transmission of management data between server and client, e.g. sending from server to client commands for recording incoming content stream; Communication details between server and client
- H04N21/63—Control signaling related to video distribution between client, server and network components; Network processes for video distribution between server and clients or between remote clients, e.g. transmitting basic layer and enhancement layers over different transmission paths, setting up a peer-to-peer communication via Internet between remote STB's; Communication protocols; Addressing
- H04N21/647—Control signaling between network components and server or clients; Network processes for video distribution between server and clients, e.g. controlling the quality of the video stream, by dropping packets, protecting content from unauthorised alteration within the network, monitoring of network load, bridging between two different networks, e.g. between IP and wireless
- H04N21/64784—Data processing by the network
- H04N21/64792—Controlling the complexity of the content stream, e.g. by dropping packets
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- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Databases & Information Systems (AREA)
- Computer Security & Cryptography (AREA)
- Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
Abstract
The invention provides a video content completion and cutting method and a system based on user quality evaluation, which comprises the following steps: the network processing node performs initial completion on the video transmitted from the source end; establishing a QoE model database; calling a corresponding QoE model according to the user feedback information, and cutting the initially completed video to a proportion required by the user; and updating the corresponding QoE model in the QoE model database in real time according to the user feedback information. The invention supports better presentation of video sources with different proportions with the playing equipment, and establishes an individualized QoE model database for a user by properly cutting the complete video generated by artificial intelligence so as to achieve the purpose of sending the video with the best QoE for a specific user.
Description
Technical Field
The invention relates to the field of video data processing, in particular to a video content completing and cutting method and system based on user quality evaluation.
Background
With the development of intelligent devices in the era of mobile internet, the screen size ratio of video capture devices to video playing devices in the market is different. For example, there are many 4:3 old fashioned television programming content that needs to be played on the 16:9 screen of the current mainstream; many contents shot by using a vertical screen of the smart phone need to be played in a transverse screen; higher screen widths and high proportions such as "18: 9 full screen" smartphones have also come to the market.
The existing network transmission playing means mainly include methods of enlarging and cutting, reducing and adding black edges, direct stretching and the like. In the technical means, part of content in the picture is cut off by enlarging and cutting, and part of screen area capable of displaying the content is wasted by reducing and adding the black edge, and although the screen can be fully utilized by stretching, the picture content cannot be cut off, but the deformation of the graph is brought. Both of these techniques bring about a reduction in the quality of user experience (QoE).
With the development of Deep Learning (DL) and Artificial Intelligence (AI) technologies in recent years, the content of a corresponding scene can be automatically generated by an artificial intelligence method on the basis of reducing and adding black edges on a network intermediate node, the part which is originally the black edge is supplemented and then transmitted to a user client, so that the original image content cannot be lost, the screen of a playing device is fully utilized, and deformation caused by image stretching is avoided. However, the information other than the generated edge realized by artificial intelligence utilizes the content of the image itself, and the more the region to be generated is far away from the region of the original existing content, the lower the reality and effect used in the generation. In the case of extreme conditions (for example, the content collected in the vertical screen of the 9:18 ratio needs to be played on the horizontal screen of the 18:9 ratio), too much non-original information is generated, and better QoE cannot be obtained. Therefore, when the artificial intelligence is used for completing the content, the content cannot be completely completed according to the original scaling, and partial clipping is still required. How to balance the relationship between completion and cropping is a technical problem to be solved.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a video content completing and cutting method and system based on user quality evaluation.
The invention provides a video content completing and cutting method based on user quality evaluation, which comprises the following steps:
an initial completion step: the network processing node performs initial completion on the video transmitted from the source end;
a database establishing step: establishing a QoE model database;
cutting: calling a corresponding QoE model from a QoE model database according to user return information, and cutting the initially completed video to a proportion required by a user;
and (3) updating the model: and updating the corresponding QoE model in the QoE model database in real time according to the user feedback information.
Preferably, the user feedback information includes:
the first information: user identification information;
and second information: description information of a screen resolution of a user;
and third information: description information of a picture cutting ratio of a user;
fourth information: quality evaluation information of the user.
Preferably, the video subjected to initial completion is cut according to the second information and the third information, and a corresponding QoE model is called according to the first information for cutting guidance.
Preferably, when the user backhaul information in the clipping step is a first backhaul of a new user, the called QoE model is a general QoE model, and after receiving first quality evaluation information of the new user, the general QoE model is updated to be a QoE model corresponding to the new user.
Preferably, the video is artificially and intelligently complemented to the maximum scale size in the initial complementing step.
The invention provides a video content completion and cutting system based on user quality evaluation, which comprises:
an initial completion module: the network processing node performs initial completion on the video transmitted from the source end;
a database establishment module: establishing a QoE model database;
a cutting module: calling a corresponding QoE model from a QoE model database according to user return information, and cutting the initially completed video to a proportion required by a user;
a model updating module: and updating the corresponding QoE model in the QoE model database in real time according to the user feedback information.
Preferably, the user feedback information includes:
the first information: user identification information;
and second information: description information of a screen resolution of a user;
and third information: description information of a picture cutting ratio of a user;
fourth information: quality evaluation information of the user.
Preferably, the video subjected to initial completion is cut according to the second information and the third information, and a corresponding QoE model is called according to the first information for cutting guidance.
Preferably, when the user backhaul information in the clipping module is a first backhaul of a new user, the called QoE model is a general QoE model, and after receiving first quality evaluation information of the new user, the general QoE model is updated to be a QoE model corresponding to the new user.
Preferably, the video is artificially and intelligently complemented to the maximum scale size in the initial completion module.
Compared with the prior art, the invention has the following beneficial effects:
the invention supports better presentation of video sources with different proportions with the playing equipment, and establishes an individualized QoE model database for a user by properly cutting the complete video generated by artificial intelligence so as to achieve the purpose of sending the video with the best QoE for a specific user.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic diagram of the system of the present invention;
FIG. 3 is a schematic diagram of an embodiment of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
As shown in fig. 1 and fig. 2, the video content completing and cutting method based on user quality evaluation provided by the present invention includes:
an initial completion step: the network processing node performs initial completion on the video transmitted from the source end;
a database establishing step: establishing a QoE model database;
cutting: calling a corresponding QoE model from a QoE model database according to user return information, and cutting the initially completed video to a proportion required by a user; in this embodiment, the video is supplemented to the maximum scale size by artificial intelligence, which is currently 18:9 or 9:18, but the invention is not limited thereto;
and (3) updating the model: and updating the corresponding QoE model in the QoE model database in real time according to the user feedback information.
Wherein, the user feedback information comprises:
the first information: user identification information;
and second information: description information of a screen resolution of a user;
and third information: description information of a picture cutting ratio of a user;
fourth information: quality evaluation information of the user.
The network processing node needs to clip the initially completed video to the proportion required by the user according to the playing proportion required by different users, and the required information two and the user screen resolution and proportion information in the information three are clipped. After the scale of the clipping is determined, the size of the clipping is determined by clipping the output result with respect to the QoE of the user as a maximization index, so that the user needs to be identified through the user identification tag information in the information one, and the QoE model related parameters of the user are called from the user QoE database for the clipping guidance processing. Since the subjective evaluation of a single user does not always remain unchanged, the QoE model parameters of the user in the QoE database of the specific user need to be updated in time according to the user quality score information in the information four. The information four generally requires the user to feed back when one session service is finished, and the user can feed back at any time in the watching process. And when the new user passes back for the first time, the called QoE model is a general QoE model, and after the first quality evaluation information of the new user is received, the general QoE model is updated to be the QoE model corresponding to the new user.
In view of the above problems, new indication information needs to be added to the user backhaul signaling, and the information can be implemented variously, and is preferably implemented by taking the set of information in table 1 below as an example.
TABLE 1
message _ id, the serial number of the information, is unique in a session;
length, indicating the length of this piece of information;
usr _ id { }: for indicating user information, comprising:
the user _ id _ length is the length of the character string of the user id;
the usr _ id _ byte stores one character in the user id;
usr _ screen _ descriptor { }: and the user side screen expression symbol comprises the pixel size, the proportion type and the playing direction of the user screen resolution. Which comprises the following steps:
resolution _ width of user Screen resolution width
resolution _ height of user Screen
scale _ ratio _ type: screen scale specifications for the four main streams are specified. The four proportions are specified to have the share in the market of more than 90 percent and the share in the internet playing equipment of more than 98 percent, and the four specifications are specified to meet the requirements of most playing equipment. And the devices with other proportions can calculate approximate proportions according to the width and height pixel values and display the approximate proportions in a cutting mode. Specifically, these four ratio types are shown in table 2:
TABLE 2
| Numerical value | Description of the invention |
| 000 | 16:9 |
| 001 | 16:10 |
| 010 | 4:3 |
| 011 | 18:9 |
| 100～111 | Retention |
scale _ direction _ flag: the direction of play for the target device is either landscape (width > height) or portrait (width < height). If the horizontal screen playing is performed, the value is 0 and is also a default value; if the display is vertical screen display, the value is 1.
score _ flag: for indicating whether this piece of signaling contains quality assessment information for user feedback. When the value is "1", the information is information containing information carrying user evaluation information, and the default is "0".
usr _ score: the user carries the user's quality score for a single service. The scores may be divided into five grades, see the following table:
TABLE 3
| Numerical value | Description of the invention |
| 0x01 | 1 minute (1) |
| 0x02 | 2 is divided into |
| 0x03 | 3 points of |
| 0x04 | 4 is divided into |
| 0x05 | 5 points (best) |
| 0x06～0xff | Retention |
For convenience of description, the following embodiments refer to the above description of a set of indication information, but in other embodiments, other information may be possible or possible.
It should be noted that the present invention is only described with the above fields as an example, and is not limited to the above fields and the size thereof. For a better understanding of the meaning of the above fields, reference is made to the example of application shown in fig. 3.
On the basis of the video content completing and cutting method based on user quality evaluation, the invention also provides a video content completing and cutting system based on user quality evaluation, which comprises the following steps:
an initial completion module: the network processing node performs initial completion on the video transmitted from the source end;
a database establishment module: establishing a QoE model database;
a cutting module: calling a corresponding QoE model from a QoE model database according to user return information, and cutting the initially completed video to a proportion required by a user; in this embodiment, the video is supplemented to the maximum scale size by artificial intelligence, which is currently 18:9 or 9:18, but the invention is not limited thereto;
a model updating module: and updating the corresponding QoE model in the QoE model database in real time according to the user feedback information.
The user return information comprises:
the first information: user identification information;
and second information: description information of a screen resolution of a user;
and third information: description information of a picture cutting ratio of a user;
fourth information: quality evaluation information of the user.
And the cutting module cuts the video subjected to initial completion according to the second information and the third information, and calls a corresponding QoE model according to the first information to guide cutting. And when the user feedback information in the cutting module is the first feedback of a new user, the called QoE model is a universal QoE model, and the universal QoE model is updated to be the QoE model corresponding to the new user after the first quality evaluation information of the new user is received.
As shown in fig. 3, in the network node, the artificial intelligence completion image is performed in the longitudinal direction and the transverse direction respectively by a deep learning artificial intelligence method, and the size of the completion is 18:9 or 9:18 (i.e. 2:1 or 1:2) at maximum to meet different requirements of different users. At this time, after the full-size video is generated, the percentage of the original content size to the full-size video size p0_ hor (representing the horizontal direction) and p0_ ver (representing the vertical direction) are recorded. It is worth noting that the processing performance of the process for the network node is high, so that a high-performance distributed node can be selected for operation, and the generated completion part is used as a common part which is possibly used by all users, and can be transmitted to an edge node close to the users in a broadcasting and multicasting mode, and for a program on demand, the content of the version is stored in the edge node in advance and is directly extracted for further processing when the user requests. The transmission for generating the complete part of the content can be realized by respectively coding or designing a new mapping scheme for coding transmission and the like.
In the edge network node, the video after being supplemented with the full artificial intelligence needs to call a QoE model QoE _ usr _ x (QoE _ orig _ dire, type, p) specific to each user according to user identification information usr _ id { } fed back by each user. The parameters differ between QoE models of different users. Wherein QoE _ orig _ dire is the QoE of the full-size video without personalized user information calculated by using the general QoE model. Specifically, dire may be replaced with ver or hor, representing that the user indicates that a portrait play or a landscape play is required according to the scale _ direction _ flag field. Type indicates a device play ratio currently required by a user, and is provided by a scale _ ratio _ Type field. The key variable in the QoE model is p, that is, the percentage of the original video in the finally generated target proportion video in the size of the finally transmitted video. Therefore, it is necessary to calculate:
max qoe_usr_x(qoe_orig_dire,type,p)
s.t.p0_dire<p<100
wherein p0_ dire can make p0_ ver or p0_ hor determined according to the user playing requirement.
And according to the p value calculated by the formula and the scale _ ratio _ type in the user return information, calculating the cut edge information, cutting to generate a video with personalized aspect ratio and size of the user, and performing personalized transmission through protocols such as MMT (multimedia messaging technology) or DASH (data over the Internet protocol) and the like.
When a session is in progress, a user can feed back the current video quality at any time (the user is forced to feed back after the session is ended), and after receiving the feedback of the user, the network node can optimize and update the QoE model parameters of the current user according to the currently used clipping coefficient p and the quality score usr _ score of the current user.
Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, units provided by the present invention as pure computer readable program code, the system and its various devices, modules, units provided by the present invention can be fully implemented by logically programming method steps in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units included in the system for realizing various functions can also be regarded as structures in the hardware component; means, modules, units for performing the various functions may also be regarded as structures within both software modules and hardware components for performing the method.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (4)
1. A video content completion and cutting method based on user quality evaluation is characterized by comprising the following steps:
an initial completion step: the network processing node respectively carries out initial completion on the video transmitted from the source end to the longitudinal direction and the transverse direction;
a database establishing step: establishing a QoE model database;
cutting: calling a corresponding QoE model from a QoE model database according to user return information, and cutting the initially completed video to a proportion required by a user;
and (3) updating the model: updating the corresponding QoE model in the QoE model database in real time according to the user feedback information;
the user feedback information comprises:
the first information: user identification information;
and second information: description information of a screen resolution of a user;
and third information: description information of a picture cutting ratio of a user;
fourth information: quality evaluation information of the user;
cutting the initially completed video according to the second information and the third information, and calling a corresponding QoE model according to the first information to guide cutting;
and when the user feedback information in the cutting step is the first feedback of a new user, the called QoE model is a universal QoE model, and the universal QoE model is updated to be the QoE model corresponding to the new user after the first quality evaluation information of the new user is received.
2. The user quality assessment based video content completion and cropping method of claim 1, wherein in said initial completion step the video is artificially intelligently completed to a maximum scale size.
3. A video content completion and cropping system based on user quality assessment, comprising:
an initial completion module: the network processing node respectively carries out initial completion on the video transmitted from the source end to the longitudinal direction and the transverse direction;
a database establishment module: establishing a QoE model database;
a cutting module: calling a corresponding QoE model from a QoE model database according to user return information, and cutting the initially completed video to a proportion required by a user;
a model updating module: updating the corresponding QoE model in the QoE model database in real time according to the user feedback information;
the user feedback information comprises:
the first information: user identification information;
and second information: description information of a screen resolution of a user;
and third information: description information of a picture cutting ratio of a user;
fourth information: quality evaluation information of the user;
cutting the initially completed video according to the second information and the third information, and calling a corresponding QoE model according to the first information to guide cutting;
and when the user feedback information in the cutting module is the first feedback of a new user, the called QoE model is a universal QoE model, and the universal QoE model is updated to be the QoE model corresponding to the new user after the first quality evaluation information of the new user is received.
4. The user quality rating based video content completion and cropping system of claim 3, wherein the video is artificially intelligently completed to a maximum scale size in the initial completion module.
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