CN114095763A - Video list pushing method and playing quality evaluation method - Google Patents
Video list pushing method and playing quality evaluation method Download PDFInfo
- Publication number
- CN114095763A CN114095763A CN202111412110.5A CN202111412110A CN114095763A CN 114095763 A CN114095763 A CN 114095763A CN 202111412110 A CN202111412110 A CN 202111412110A CN 114095763 A CN114095763 A CN 114095763A
- Authority
- CN
- China
- Prior art keywords
- video
- target
- equipment
- mth
- videos
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 63
- 238000013441 quality evaluation Methods 0.000 title claims abstract description 43
- 230000000694 effects Effects 0.000 claims abstract description 117
- 238000001303 quality assessment method Methods 0.000 claims abstract description 24
- 238000012163 sequencing technique Methods 0.000 claims abstract description 19
- 230000004044 response Effects 0.000 claims abstract description 7
- 238000011156 evaluation Methods 0.000 claims description 101
- 238000013507 mapping Methods 0.000 claims description 57
- 230000002452 interceptive effect Effects 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 10
- 238000010586 diagram Methods 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 230000002159 abnormal effect Effects 0.000 description 4
- 230000006978 adaptation Effects 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000003044 adaptive effect Effects 0.000 description 3
- 238000004891 communication Methods 0.000 description 3
- 230000003993 interaction Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000000670 limiting effect Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
- 230000036961 partial effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000000844 transformation Methods 0.000 description 1
Images
Classifications
-
- 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]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/25—Management operations performed by the server for facilitating the content distribution or administrating data related to end-users or client devices, e.g. end-user or client device authentication, learning user preferences for recommending movies
- H04N21/262—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists
- H04N21/26258—Content or additional data distribution scheduling, e.g. sending additional data at off-peak times, updating software modules, calculating the carousel transmission frequency, delaying a video stream transmission, generating play-lists for generating a list of items to be played back in a given order, e.g. playlist, or scheduling item distribution according to such list
-
- 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
- H04N21/432—Content retrieval operation from a local storage medium, e.g. hard-disk
-
- 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
- H04N21/442—Monitoring of processes or resources, e.g. detecting the failure of a recording device, monitoring the downstream bandwidth, the number of times a movie has been viewed, the storage space available from the internal hard disk
- H04N21/44204—Monitoring of content usage, e.g. the number of times a movie has been viewed, copied or the amount which has been watched
-
- 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
- H04N21/47—End-user applications
- H04N21/482—End-user interface for program selection
- H04N21/4826—End-user interface for program selection using recommendation lists, e.g. of programs or channels sorted out according to their score
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Software Systems (AREA)
- Human Computer Interaction (AREA)
- Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
Abstract
The embodiment of the application discloses a video list pushing method, which comprises the following steps: determining a plurality of videos in response to a video list request provided by a target device; determining a play quality evaluation value of each video in the plurality of videos in the target device; sequencing the playing quality evaluation values of the videos from high to low to obtain a sequencing result; and generating a target video list and pushing the target video list to the target equipment based on the sequencing result, wherein all videos in the target video list are sequenced from high to low according to respective play quality assessment values. According to the technical scheme provided by the embodiment of the application, whether the video is pushed or not or the sequencing position of the video in the push list is determined according to the play quality evaluation value, so that the video is determined to be dynamically output in the target equipment (such as large-screen equipment). Based on the above-mentioned play quality assessment value, the play effect of the video which is seen by the user first can be made best, so that the viewing experience of the user is improved.
Description
Technical Field
The present application relates to the field of computers, and in particular, to a method, a system, a computer device, and a computer-readable storage medium for pushing a video list.
Background
With the development of internet technology, different network platforms have developed different content types. For example, PGC (Professional Generated Content) types and OGV (Professional Generated Video) types represented by the art of love and Netflix, and UGC (User Generated Content) types represented by network platforms such as Youtube and bililli. PGC type and OGV type are produced by professional organizations, and video output quality is generally better guaranteed. However, with the popularization of mobile networks (such as 4G) and small-screen devices (such as smart phones), UGC-type videos (hereinafter, UGC videos) are increasing. The core of the UGC is to advocate each user to present their original content through the platform or to provide it to other users. UGC enables people to be content producers, and therefore massive videos can be rapidly produced to enrich the spiritual life of people.
UGC video is usually shot by a large number of laymen through laymen, and the difference of video quality is large. UGC video is created for small-screen equipment, namely video parameters and the like of the UGC video are better adapted to the small-screen equipment. Most UGC videos are played on large-screen equipment (such as televisions and projectors), and the playing effect is poor.
Disclosure of Invention
An object of the embodiments of the present application is to provide a video list pushing method, a video list pushing system, a playing quality evaluation method, a computer device, and a computer-readable storage medium, which are used to solve the above technical problems.
One aspect of the embodiments of the present application provides a method for pushing a video list, where the method includes:
determining a plurality of videos in response to a video list request provided by a target device;
determining a play quality evaluation value of each video in the plurality of videos in the target device;
sequencing the playing quality evaluation values of the videos from high to low to obtain a sequencing result; and
and generating a target video list and pushing the target video list to the target equipment based on the sequencing result, wherein all videos in the target video list are sequenced from high to low according to respective play quality assessment values.
Optionally, the determining the play quality assessment value of each of the plurality of videos in the target device includes:
calculating the target quality assessment value by one or more of the following parameters:
the target matching degree is the matching degree between the video metadata of each video and the target equipment parameters of the target equipment;
a target playing effect evaluation value which is an evaluation value of the playing effect of each video on the target equipment;
and obtaining the target popularity according to the interactive data of each video.
Optionally, the method further includes:
inquiring the mth target matching degree between the target equipment and the mth video from the cache; the cache is used for pre-storing the matching degree between each of one or more videos and different devices, the mth video is any one of the videos, and m is a positive integer greater than 0; and
if the mth target matching degree between the target device and the mth video is not inquired, calculating the mth target matching degree according to the target device parameters and the video meta-information of the mth video.
Optionally, the method further includes:
determining the target equipment grade of the target equipment according to the target equipment parameter;
inquiring an mth target playing effect evaluation value of the mth video from a cache according to the grade of the target equipment and the model of the target equipment; the cache is used for storing playing effect evaluation values of one or more videos on devices with different device grades and different device models in advance, the mth video is any one of the videos, and m is a positive integer greater than 0; and
and if the mth target playing effect evaluation value is not inquired according to the grade of the target equipment and the model of the target equipment, inquiring the mth target playing effect evaluation value from the cache according to the grade of the target equipment.
Optionally, the target device level is one of a plurality of device levels; the method further comprises the following steps of acquiring the play effect evaluation value of each equipment grade in advance:
acquiring a play log of each device, and calculating a play effect evaluation value of each device;
carrying out grade division according to equipment parameters of each equipment, calculating the playing effect evaluation mean value of each equipment grade, and obtaining the mapping relation between each equipment grade and the corresponding playing effect evaluation mean value; the average value of the evaluation of the playing effect of one device level is the average value of the evaluation values of the playing effect of each device under the device level.
Optionally, the cache is further configured to store:
the popularity degree corresponding to each of the plurality of video identifications, and each video identification is associated with one video;
and the popularity degree of each video label corresponds to, and each video label is respectively associated with one or more videos.
Optionally, the method further includes:
according to the mth video identification of the mth video, querying the mth popularity degree associated with the mth video identification from the cache;
and if the mth popularity is not inquired according to the mth video identifier, acquiring the mth popularity from the cache according to one or more video tags associated with the mth video.
An aspect of an embodiment of the present application further provides a video list pushing system, including:
a first determining module, configured to determine a plurality of videos in response to a video list request provided by a target device;
a second determining module, configured to determine a playing quality assessment value of each of the plurality of videos in the target device;
the sequencing module is used for sequencing the playing quality evaluation values of the videos from high to low to obtain a sequencing result; and
and the pushing module is used for generating a target video list and pushing the target video list to the target equipment based on the sorting result, wherein all videos in the target video list are sorted from high to low according to respective play quality evaluation values.
One aspect of the present embodiment further provides a play quality evaluation method, including:
obtaining a plurality of matching degrees according to the video metadata of each video and the equipment parameters of each equipment, and establishing a first mapping relation among each matching degree, the corresponding video and the corresponding equipment; each matching degree is calculated according to the video metadata of one of the videos and the equipment parameters of one of the equipment;
determining the equipment model and the equipment grade of each piece of equipment according to the equipment parameters of each piece of equipment; determining the playing effect evaluation value of each device according to the playing log of each device; establishing a second mapping relation among the equipment models and the equipment grades of the equipment and the corresponding playing effect evaluation values;
calculating the popularity of each video according to the interactive data associated with each video; establishing a third mapping relation between the video identification of each video and the corresponding popularity and/or a fourth mapping relation between the video label of each video and the corresponding popularity, wherein the video label is used for describing the characteristics of the video;
updating the first mapping relation, the second mapping relation, the third mapping relation and the fourth mapping relation into a cache;
the first mapping relation, the second mapping relation, the third mapping relation and the fourth mapping relation are located in the cache and used for being read by a processor and used for evaluating a playing quality evaluation value of the video in the equipment.
Optionally, the method further includes:
establishing a fifth mapping relation between each equipment grade and the corresponding play effect evaluation mean value; the average value of the evaluation of the playing effect of one device level is the average value of the evaluation values of the playing effect of each device under the device level.
An aspect of the embodiments of the present application further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor is configured to implement the steps of the video list pushing method or the playing quality assessment method as described above when executing the computer program.
An aspect of the embodiments of the present application further provides a computer-readable storage medium, in which a computer program is stored, where the computer program is executable by at least one processor to cause the at least one processor to execute the steps of the play quality assessment method according to the above video list push method.
The video list pushing method, the video list pushing system, the playing quality evaluation method, the playing quality evaluation device and the computer-readable storage medium provided by the embodiment of the application have the following advantages:
and determining whether to push the video or the sequencing position of the video in the push list according to the play quality evaluation value, thereby determining to realize the dynamic output of the video on a target device (such as a large-screen device). Based on the above-mentioned play quality assessment value, the play effect of the video which is seen by the user first can be made best, so that the viewing experience of the user is improved.
Drawings
Fig. 1 schematically shows an application environment diagram of a play quality evaluation method according to an embodiment of the present application;
fig. 2 schematically shows a flow chart of a play quality assessment method according to a first embodiment of the present application;
fig. 3 schematically shows an example of a play quality evaluation method according to a first embodiment of the present application;
fig. 4 schematically shows a flow chart of a video list pushing method according to the second embodiment of the present application;
5-8 are flowcharts schematically illustrating additional steps of a video list pushing method according to the second embodiment of the present application;
fig. 9 schematically shows a block diagram of a video list push system according to a third embodiment of the present application;
fig. 10 schematically shows a block diagram of a play quality evaluation system according to a fourth embodiment of the present application;
fig. 11 schematically shows a hardware architecture diagram of a computer device according to a fifth embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the descriptions relating to "first", "second", etc. in the embodiments of the present application are only for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present application.
In the description of the present application, it should be understood that the numerical references before the steps do not identify the order of performing the steps, but merely serve to facilitate the description of the present application and to distinguish each step, and therefore should not be construed as limiting the present application.
Through analysis of the inventor, the current mainstream resource types on a large screen still take OGV as the dominance, such as Aiqiyi, Netflix and the like. Because the video output quality can be better guaranteed due to the production of professional institutions. On the contrary, UGC type production is random many, and with the popularization of intelligent machines and 4G networks, users can shoot videos and upload platforms at any time. The UGC has vastly different video quality, subject to the level of the shooting equipment and the photography itself.
Meanwhile, while large screen consumption is still mainly OGV, the playing times and duration ratio of UGC are gradually increased, especially in the young group in the Z era. Taking station B as an example, the playing times and the duration of UGC video on a large screen both exceed more than 60%. This also illustrates that users have an increasingly stronger desire to consume UGC video on large screens.
Users consume UGC videos on large screens, often without a good viewing experience on mobile devices. On one hand, many UGC videos are authored on their own based on mobile devices; on the other hand, compared with large-screen equipment such as a television and the like, the mobile equipment has the software and hardware parameters more adaptive to the UGC video playing. However, no third party organization or UGC production platform in the industry provides a relatively standard set of large-screen output schemes suitable for UGC video. The existing mode is to directly launch the head UGC with good playing effect on the mobile equipment to a large screen for consumption of a user, so that the problems that the feedback of the user on the large screen side is often received, the picture is too fuzzy, the 4k video playing is stuck, and the like are caused. Therefore, there is also a large difference between the playback quality of the UGC video on a large screen and the viewing expectations of the user.
Therefore, the following problems are solved: (1) the method temporarily does not need a large-screen output scheme and an evaluation method for UGC video. (2) The big screen output mode of the UGC video is usually to refer to the playing effect on the mobile device, and the adaptation degree of the video and the big screen device is not considered. 3) Even considering the differences on large screens, the general practice is not flexible enough, e.g. for low end machines, not 4k high definition, etc.
The application provides a video large-screen playing quality evaluation scheme, and in the scheme, the video playing quality can be evaluated in real time according to multiple dimensions such as video parameters, playing equipment and user experience, dynamic output of videos on a large screen is achieved, and the following effects are achieved: (1) UGC video can not be output to devices with low matching degree, for example, low-quality video is output to high-end devices, so that the blurring effect is increased; or high quality video output to low end devices, resulting in playback anomalies. (2) Except for the fixed matching condition between the video and the equipment, the output value from the video to the equipment is comprehensively measured by combining the on-line user feedback and the actual effect of the equipment, and the dynamic update of the calculation engine is realized.
The following are the term explanations of the present application:
UGC: user Generated Content, User produced Content.
PGC: professional Generated Content, Professional production Content.
OGV: occupationally produced Video, professional organizations produce videos, including dramas, animations, movies, television shows, art shows, etc. purchased or self-made by the platform.
Video resolution: the video area size is in pixels (px), e.g., 1920 x 1080.
Video frame rate: number of frames per second (fps).
Color depth: how many levels N, e.g., 8 bits, a primary (e.g., RGB) can be divided into, can represent 2^8 ^ 256 levels.
Video code rate: the amount of data per second (bps, bit) is equal to the resolution frame rate color depth.
Resolution of the device: w h, how many pixels can be displayed. E.g., 2k, indicating a horizontal display resolution of 2000 pixels.
Device refresh rate: number of refreshes per second in Hz.
Encoding/decoding: the original file of the audio and video is generally too large, and the coding is used for reducing the data size and facilitating the transmission; the decoding is to obtain the original audio-video file.
Definition: the video quality measure index with specified resolution, code rate and frame rate, such as 1080p, 1080p60 frames.
Fig. 1 schematically shows an environment architecture diagram of a video list push method according to an embodiment of the present application. In an exemplary embodiment, as shown in FIG. 1, a computer device 2 may be connected to a plurality of devices 4 via a network.
The computer device 2 may push the video list into the device (large screen device) 4 over the network.
The computer device 2 may be composed of a single or multiple computing devices, such as a rack server, a blade server, a tower server, or a rack server (including a stand-alone server or a server cluster composed of multiple servers). The one or more computer devices may include virtualized compute instances. The computer device may load the virtual machine based on a virtual image and/or other data that defines the particular software (e.g., operating system, dedicated application, server) used for emulation. As the demand for different types of processing services changes, different virtual machines may be loaded and/or terminated on the one or more computer devices.
The network may include various network devices such as routers, switches, multiplexers, hubs, modems, bridges, repeaters, firewalls, proxy devices, and/or the like. The network may include physical links, such as coaxial cable links, twisted pair cable links, fiber optic links, combinations thereof, and/or the like. The network may include wireless links, such as cellular links, satellite links, Wi-Fi links, and/or the like.
Device 4 may be configured to access computer device 2. The device 4 may comprise, among other things, any type of computer device, such as: electronic equipment with large display interfaces, such as smart televisions, projectors, personal computers and the like.
Hereinafter, the present application will provide a video quality assessment method, a video list push method, a system, a computer device, and a computer-readable storage medium.
Example one
Fig. 2 schematically shows a flow chart of a playback quality assessment method according to an embodiment of the present application. The method may be performed in a computer device 2. As shown in fig. 2, the method for evaluating the playing quality may include steps S200 to S206, wherein:
step S200, obtaining a plurality of matching degrees according to the video metadata of each video and the equipment parameters of each equipment, and establishing a first mapping relation among each matching degree, the corresponding video and the corresponding equipment; wherein each matching degree is calculated according to the video metadata of one of the videos and the device parameters of one of the devices.
The video metadata may include the following indicators: video resolution, video frame rate, video bitrate, encoding, and the like.
The equipment parameters may include the following: screen refresh rate, device resolution, screen size, decoding.
For example, the matching degree M can be calculated by the following formula:
wherein i is used to refer to an index.
aiThe weights (0-1) for each index score can be dynamically adjusted according to the effect recall.
αiFor the index scoring parameter, based on experimental evaluation results, e.g. resolution index alphai＝4。
αiThe acquisition process is as follows:
the method comprises the following steps: different videos are played in a large number of devices, and evaluation is carried out from different evaluation dimensions such as screen-blooming times, first frame loading time consumption, pause times, definition, sound and picture asynchrony and the like, so that a large number of evaluation effect scores are obtained.
Step two: and calculating the index score parameters of the indexes according to the evaluation effect scores and the indexes corresponding to the evaluation effects.
xiScore an index, e.g., (1) resolution index, then
RvFor video resolution, RdThe resolution of the device; (2) the frame rate index is a video frame rate/equipment refreshing rate; (3) the code rate index is a code rate value of the video code rate; (4) and the coding index is a first value if the coding and the decoding are adaptive, or is a second value if the coding and the decoding are adaptive. It should be noted that some of the indicators are independent indicators, such as a bitrate indicator, and some of the indicators are non-independent indicators, such as a resolution indicator, which is a ratio of video resolution to device resolution, and the frame rate indicator is similar to the above.
Step S202, determining the equipment model and the equipment grade of each equipment according to the equipment parameters of each equipment; determining the playing effect evaluation value of each device according to the playing log of each device; and establishing a second mapping relation among the equipment models and the equipment grades of the equipment and the corresponding playing effect evaluation values.
Establishing a fifth mapping relation between each equipment grade and the corresponding play effect evaluation mean value; the average value of the evaluation of the playing effect of one device level is the average value of the evaluation values of the playing effect of each device under the device level. The playback effect evaluation average corresponding to the device class can be used directly by a large number of devices in a wider range.
The play log may include the following indicators: the pause rate, the abnormal loading rate, the first frame loading time-consuming level and the like.
The method comprises the following steps: different videos are played in a large number of devices, and a large number of playing logs of the devices are obtained.
Step two: the playback effect evaluation values of the respective devices are calculated S1.
Taking one of the devices a as an example, the play effect evaluation value S1 can be calculated by the following formula:
where j is used to refer to an index, such as the katton rate.
bjThe weights (0-1) for each index score can be dynamically adjusted according to the effect recall.
Beta is a parameter, and the initial value is 1 according to the evaluation effect score of the experiment.
The beta acquisition process is as follows: playing different videos in a large number of devices, acquiring user evaluation values fed back by each device, and calculating playing effect evaluation values of each device based on the formula and the current beta value. And fitting based on the user evaluation value and the playing effect evaluation value, so that the value of the beta is modified according to the fitting result.
yjAnd scoring indexes such as abnormal loading rate, pause rate, first frame loading time consumption level and the like of the equipment A.
Step three: according to partial device parameters (such as operating system version, CPU grade, GPU grade and memory grade) of each device, a large number of devices are divided into different device grades, such as high grade, medium grade and low grade.
Step four: and carrying out average operation on the playing effect evaluation values S1 of all the devices at the same device level to obtain the playing effect evaluation value of the same device level, and storing all the device levels and the corresponding playing effect evaluation values in a database.
Step S204, calculating the popularity of each video according to the interactive data related to each video; and establishing a third mapping relation between the video identification of each video and the corresponding popularity and/or a fourth mapping relation between the video label of each video and the corresponding popularity, wherein the video label is used for describing the characteristics of the video.
The interactive data can comprise play-out rate, praise rate, coin-feed rate and collection rate.
The playing completion rate is the playing completion rate of the video, that is, how many people of all users who see the video completely finish the video by 100%. Of the 100 people, 30 people finish watching the video, and the playing finish rate is 30%. If the playing rate is low, the video is indicated, and the video is not enough to attract the user to see the end.
The praise ratio, the ratio of praise/visitor number in the statistical period, is used as one of the judgment indexes reflecting the popularity of the video in the traffic pool.
The coin-in ratio, the ratio of coin count/visitor count in the statistical period, is used as one of the judgment indexes reflecting the popularity of the video in the flow pool.
The collection ratio, the ratio of collection number/visitor number in the statistical period, is used as one of the judgment indexes reflecting the popularity of the video in the traffic pool.
For example, the popularity S2 can be calculated by the following formula:
where k is used to refer to an indicator, such as the end play rate.
ckThe weights (0-1) for each index score can be dynamically adjusted according to the effect recall.
zkScores are made for indicators such as the end play rate, coin-feed ratio, like-rate, collection rate, etc. of the video.
Step S206, updating the first mapping relationship, the second mapping relationship, the third mapping relationship and the fourth mapping relationship into a cache.
It can be stored in a Key-Value pair (Key-Value) manner.
Wherein the first mapping relation, the second mapping relation, the third mapping relation and the fourth mapping relation located in the cache are used for being read by a processor and used for evaluating a playing quality evaluation value S of the video in the device, as obtained by the following formula:
formula (4) S2 (M × S1)
For example, calculate oneEvaluation value S of playing quality of video A played on certain equipment BAB：
SAB＝MAB*S1AB*S2AB
MABIs the degree of match between the video metadata of video a and the device parameters of device B;
S1ABthe playing effect evaluation value corresponding to the equipment grade of the equipment B;
S2ABis the popularity of video a.
The playback quality evaluation value SABFor determining whether to push video a to device B. If the play quality evaluation value SABBelow the lowest output threshold, video a is not pushed to device B. The lowest output threshold may be customized.
The playback quality evaluation value SABFor determining the ordering of video a among the pushed plurality of videos. For example, the playback quality evaluation value SABThe higher the video a is, the earlier in the ranking, the better it is pushed to the user.
The play quality evaluation method provided by the embodiment of the application has the following advantages:
(1) the playing quality evaluation value of the video on the equipment can be evaluated in real time according to multiple dimensions such as video parameters, equipment parameters, interactive data and the like, whether the video is pushed or not or the sequencing position of the video in a pushing list is determined, and therefore the dynamic output of the video on the equipment (such as large-screen equipment) is determined. Based on the above-mentioned play quality assessment value, the play effect of the video which is seen by the user first can be made best, so that the viewing experience of the user is improved.
(2) In the prior art, the big-screen output mode of the UGC video generally refers to the playing effect on the small-screen device, and does not consider the adaptation degree of the video and the big-screen device.
In other words, the UGC video is not output to devices with too low a match, e.g., low quality video is output to high-end devices, resulting in increased blurring effects; or high quality video output to low end devices, resulting in playback anomalies.
(3) Besides the matching degree between the video and the equipment, the interactive data of each video watching object and the actual effect of the equipment are further combined, and the playing quality evaluation value of the video played on the equipment is comprehensively measured.
In order to make the embodiments of the present application more clearly understood, a specific application example is provided below as shown in fig. 3.
Firstly, referring to a table I, dynamically configuring the weight parameters of each index through a configuration module, namely realizing aiAnd bjAnd ckWithout restarting the relevant application.
Watch 1
Secondly, a monitoring platform acquires a large number of playing logs of the equipment in the video playing process in real time. The play log is data generated when each device plays the video, such as the pause rate, the abnormal loading rate and the first frame loading time-consuming level.
The playback effect evaluation value of each device can be calculated by the Job offline module.
For example, each run is as follows:
1) inquiring a play log of the previous day of the dimension of the equipment, namely a pause rate, a loading abnormal rate and a first frame loading time-consuming level; calculating a playing effect evaluation value s1_ score of each device according to the playing log; the device class and the device model of each device are acquired, and key ═ level } _ { model } _ s1_ score is stored in the database. Wherein, level represents the device level (high, medium, low) of the device, and model is the device model of the device.
The device level can be distinguished according to the memory size, the cpu core number, the operating system version and the like.
2) The average playback effect evaluation value for each device level is calculated and stored in the database as key ═ { level } _ s1_ score.
The correspondence between the device class and the memory, and the CPU … can be referred to as table two.
Watch two
The actual evaluation value is a subjective effect evaluation value of the video played by the user at each equipment level, and each subjective effect evaluation value can be used for being mutually proved with an average playing effect evaluation value of the corresponding equipment level.
It should be noted that the Job offline module may update the data to the cache every other day.
Thirdly, interactive data of each video is obtained according to the monitoring platform, the popularity of each video is calculated through a Job offline module, and a mapping relation between the video identification/video label of each video and the popularity is generated and updated into a cache.
For example, each run is as follows: 1) inquiring a label list and interaction data of the video; 2) calculating the popularity of each video s2_ score, and storing the popularity in a database by key { cid } _ s2_ score, wherein cid is the video identification of each video; 3) the popularity of a video tag is the average of the popularity of all videos with the tag, and is stored in the database as key ═ tag } _ s2_ score, tag being the identity of each video tag. It should be noted that the Job offline module may update the data to the cache every hour.
Example two
Various technical details (such as calculation formulas) in the embodiment can be referred to in embodiment one.
Fig. 4 schematically shows a flowchart of a video push method according to an embodiment of the present application. The method may be performed in a computer device 2. As shown in fig. 4, the video push method may include steps S400 to S406, wherein:
in step S400, a plurality of videos are determined in response to a video list request provided by a target device.
The plurality of videos can be randomly obtained from a video pool;
the plurality of videos may also be obtained from a video pool based on the current scene of the target device (e.g., "hot").
Step S402, determining a playing quality evaluation value of each of the plurality of videos in the target device.
Step S404, rank the playing quality evaluation values of the videos from high to low to obtain a ranking result.
Step S406, based on the sorting result, generating a target video list and pushing the target video list to the target device, where all videos in the target video list are sorted from high to low according to their respective play quality assessment values.
The video pushing method provided by the embodiment of the application has the following advantages:
and determining whether to push the video or the sequencing position of the video in the push list according to the play quality evaluation value, thereby determining to realize the dynamic output of the video on a target device (such as a large-screen device). Based on the above-mentioned play quality assessment value, the play effect of the video which is seen by the user first can be made best, so that the viewing experience of the user is improved.
As an alternative embodiment, the step S402 may include: calculating the target quality assessment value by one or more of the following parameters: the target matching degree is the matching degree between the video metadata of each video and the target equipment parameters of the target equipment; a target playing effect evaluation value which is an evaluation value of the playing effect of each video on the target equipment; and obtaining the target popularity according to the interactive data of each video.
In the prior art, the large-screen output mode of many videos usually refers to the playing effect on the small-screen device, and does not consider the adaptation degree of the video itself and the large-screen device, in this optional embodiment, the adaptation degree (matching degree) of the video itself and the large-screen device is fully considered, the video with the possibly poor playing effect is played in the large-screen device, and the video with the good playing effect is not pushed or pushed backwards, so that the user can watch the video with the good playing effect in the large-screen device as much as possible. In other words, the video is not output to devices with too low a match, e.g., low quality video is output to high-end devices, resulting in increased blurring effects; or high quality video output to low end devices, resulting in playback anomalies.
In addition, besides the matching degree between the video and the equipment, the interactive data of each video watching object and the actual effect of the equipment are further combined, and the playing quality evaluation value of the video played on the equipment is comprehensively measured.
As an alternative embodiment, as shown in fig. 5, the method further includes: step S500, inquiring the mth target matching degree between the target equipment and the mth video from the cache; the cache is used for pre-storing the matching degree between each of one or more videos and different devices, the mth video is any one of the videos, and m is a positive integer greater than 0; and step S502, if the mth target matching degree between the target device and the mth video is not inquired, calculating the mth target matching degree according to the target device parameters and the video meta-information of the mth video. Referring back to fig. 3, the computer device 10000 queries from the cache according to the video identifier of the mth video and the device identifier (buvid) of the target device, so as to quickly obtain the mth target matching degree. And under the condition that the query fails, querying the target device parameters of the target device according to the device identification of the target device, and calculating the mth target matching degree through a formula (1) in the first embodiment. The calculated mth target matching degree can be used for calculating a play quality assessment value and can be updated to the cache so as to facilitate subsequent direct use. In this optional embodiment, the mth target matching degree may be efficiently obtained by caching the query and performing real-time calculation in the case of a failure of the query.
As an alternative embodiment, as shown in fig. 6, the method further includes: step S600, determining the target equipment grade of the target equipment according to the target equipment parameter; step S602, inquiring an mth target playing effect evaluation value of the mth video from a cache according to the grade of the target equipment and the model of the target equipment; the cache is used for storing playing effect evaluation values of one or more videos on devices with different device grades and different device models in advance, the mth video is any one of the videos, and m is a positive integer greater than 0; and step S604, if the mth target playing effect evaluation value is not inquired according to the grade of the target equipment and the model of the target equipment, inquiring the mth target playing effect evaluation value from the cache according to the grade of the target equipment. Referring back to fig. 3, the computer device 10000 queries key ═ { level } _{ model } _ s1_ score, which matches both the target device class and the target device model, from the cache, thereby obtaining an accurate target play effect evaluation value. And if the key which matches the target device grade and the target device model is not { level } _ { model } _ s1_ score, querying the key which matches the target device grade { level } _ s1_ score. In this optional embodiment, the target playing effect evaluation value is accurately queried, and in the case that the accurate query fails, the target playing effect evaluation value is queried in a fuzzy manner, that is, the query is performed solely through the device level, so as to efficiently and effectively obtain the target playing effect evaluation value.
As an alternative embodiment, the target device class is one of a plurality of device classes. As shown in fig. 7, the method further includes a step of acquiring the play effect evaluation values of the respective device ranks in advance: step S700, acquiring a play log of each device, and calculating a play effect evaluation value of each device; step S702, grade division is carried out according to equipment parameters of each equipment, and a playing effect evaluation mean value of each equipment grade is calculated to obtain a mapping relation between each equipment grade and the corresponding playing effect evaluation mean value; the average value of the evaluation of the playing effect of one device level is the average value of the evaluation values of the playing effect of each device under the device level. In this alternative embodiment, the calculation of the rating and the play effect evaluation value may refer to embodiment one.
As an optional embodiment, the cache is further configured to store:
the popularity degree corresponding to each of the plurality of video identifications, and each video identification is associated with one video;
and the popularity degree of each video label corresponds to, and each video label is respectively associated with one or more videos.
In the optional embodiment, the popularity of each video can be accurately inquired through video identification.
In the case of failure of accurate query, popularity of videos with similar dimension or dimensions can be obtained through video tags. The popularity of the video tag can be quickly evaluated through similar videos.
As an alternative embodiment, as shown in fig. 8, the method further includes: step S800, according to the mth video identifier of the mth video, inquiring the mth popularity degree associated with the mth video identifier from the cache; step S802, if the mth popularity is not queried according to the mth video identifier, obtaining the mth popularity from the cache according to one or more video tags associated with the mth video. And when the mth video is provided with a plurality of video tags, and each video tag corresponds to a popularity, taking the largest popularity as the popularity of the mth video. In this alternative embodiment, the popularity of the mth video may be accurately queried, and in case of failure of accurate query, the popularity of the mth video is quickly evaluated based on the video tags of the mth video.
In order to make the embodiments of the present application more clearly understood, a specific application example is provided below, with the parameters of fig. 3 being continued.
1) The M operator firstly inquires whether the calculated matching degree exists in the cache or not according to key (cid) (buvid) (M-score), and if yes, the M operator directly returns; otherwise, inquiring the video meta-information in real time, calculating the matching degree between the video meta-information and the video meta-information according to the formula (1), and writing the matching degree into the cache. cid represents the video identification of the video and buvid represents the device identification of the device.
2) And the S1 operator inquires the playing effect evaluation value according to the equipment grade and the equipment model, and only inquires by using the equipment grade if the inquiry is empty.
3) And (8) inquiring the popularity degree according to the video identification of the video by the S2 operator, and if the inquiry is empty, inquiring the popularity degree by adopting a video tag (tag) of the video. When a video is associated with multiple tags, then maximum popularity is taken.
4) And (4) calculating the playing quality evaluation value of each video at the equipment according to the formula (4).
5) Taking the hot function as an example, when the hot video is processed, the video is sequenced from high to low according to the play quality evaluation value, so that the best video playing effect seen by the user firstly is realized.
EXAMPLE III
Fig. 9 schematically shows a block diagram of a video list push system according to a third embodiment of the present application. The video list push system may be partitioned into one or more program modules, which are stored in a storage medium and executed by one or more processors to implement embodiments of the present application. The program modules referred to in the embodiments of the present application refer to a series of computer program instruction segments that can perform specific functions, and the following description will specifically describe the functions of the program modules in the embodiments.
As shown in fig. 9, the video list push system 900 may include a first determining module 910, a second determining module 920, an ordering module 930, and a push module 940, wherein:
a first determining module 910, configured to determine a plurality of videos in response to a video list request provided by a target device;
a second determining module 920, configured to determine a playing quality assessment value of each of the plurality of videos in the target device;
a sorting module 930, configured to sort the playing quality evaluation values of the videos from high to low to obtain a sorting result; and
a pushing module 940, configured to generate a target video list based on the sorting result, and push the target video list to the target device, where all videos in the target video list are sorted from high to low according to respective play quality assessment values.
As an optional embodiment, the second determining module 920 is further configured to:
calculating the target quality assessment value by one or more of the following parameters:
the target matching degree is the matching degree between the video metadata of each video and the target equipment parameters of the target equipment;
a target playing effect evaluation value which is an evaluation value of the playing effect of each video on the target equipment;
and obtaining the target popularity according to the interactive data of each video.
As an optional embodiment, the system further includes a matching degree obtaining module, configured to:
inquiring the mth target matching degree between the target equipment and the mth video from the cache; the cache is used for pre-storing the matching degree between each of one or more videos and different devices, the mth video is any one of the videos, and m is a positive integer greater than 0; and
if the mth target matching degree between the target device and the mth video is not inquired, calculating the mth target matching degree according to the target device parameters and the video meta-information of the mth video.
As an optional embodiment, the system further includes a playing effect evaluation value obtaining module, configured to:
determining the target equipment grade of the target equipment according to the target equipment parameter;
inquiring an mth target playing effect evaluation value of the mth video from a cache according to the grade of the target equipment and the model of the target equipment; the cache is used for storing playing effect evaluation values of one or more videos on devices with different device grades and different device models in advance, the mth video is any one of the videos, and m is a positive integer greater than 0; and
and if the mth target playing effect evaluation value is not inquired according to the grade of the target equipment and the model of the target equipment, inquiring the mth target playing effect evaluation value from the cache according to the grade of the target equipment.
As an alternative embodiment, the target device class is one of a plurality of device classes;
the playing effect evaluation value obtaining module is further configured to:
acquiring a play log of each device, and calculating a play effect evaluation value of each device;
carrying out grade division according to equipment parameters of each equipment, calculating the playing effect evaluation mean value of each equipment grade, and obtaining the mapping relation between each equipment grade and the corresponding playing effect evaluation mean value; the average value of the evaluation of the playing effect of one device level is the average value of the evaluation values of the playing effect of each device under the device level.
As an optional embodiment, the cache is further configured to store:
the popularity degree corresponding to each of the plurality of video identifications, and each video identification is associated with one video;
and the popularity degree of each video label corresponds to, and each video label is respectively associated with one or more videos.
As an optional embodiment, the system further includes a popularity obtaining module, configured to:
according to the mth video identification of the mth video, querying the mth popularity degree associated with the mth video identification from the cache;
and if the mth popularity is not inquired according to the mth video identifier, acquiring the mth popularity from the cache according to one or more video tags associated with the mth video.
Example four
Fig. 10 schematically shows a block diagram of a play quality evaluation system according to a fourth embodiment of the present application. The video list push system may be partitioned into one or more program modules, which are stored in a storage medium and executed by one or more processors to implement embodiments of the present application. The program modules referred to in the embodiments of the present application refer to a series of computer program instruction segments that can perform specific functions, and the following description will specifically describe the functions of the program modules in the embodiments.
As shown in fig. 10, the playing quality evaluation system 1000 may include a first establishing module 1010, a second establishing module 1020, a third establishing module 1030, and an updating module 1040, wherein:
a first establishing module 1010, configured to obtain multiple matching degrees according to the video metadata of each video and the device parameters of each device, and establish a first mapping relationship among each matching degree, a corresponding video, and a corresponding device; each matching degree is calculated according to the video metadata of one of the videos and the equipment parameters of one of the equipment;
a second establishing module 1020, configured to determine, according to the device parameter of each device, a device model and a device class of each device; determining the playing effect evaluation value of each device according to the playing log of each device; establishing a second mapping relation among the equipment models and the equipment grades of the equipment and the corresponding playing effect evaluation values;
a third establishing module 1030, configured to calculate popularity of each video according to the interaction data associated with each video; establishing a third mapping relation between the video identification of each video and the corresponding popularity and/or a fourth mapping relation between the video label of each video and the corresponding popularity, wherein the video label is used for describing the characteristics of the video;
an updating module 1040, configured to update the first mapping relationship, the second mapping relationship, the third mapping relationship, and the fourth mapping relationship in a cache;
the first mapping relation, the second mapping relation, the third mapping relation and the fourth mapping relation are located in the cache and used for being read by a processor and used for evaluating a playing quality evaluation value of the video in the equipment.
As an optional embodiment, the system may further comprise a fourth establishing module for
Establishing a fifth mapping relation between each equipment grade and the corresponding play effect evaluation mean value; the average value of the evaluation of the playing effect of one device level is the average value of the evaluation values of the playing effect of each device under the device level.
EXAMPLE five
Fig. 11 schematically shows a hardware architecture diagram of a computer device 2 suitable for implementing the video list pushing method according to the fifth embodiment of the present application. In the present embodiment, the computer device 2 is a device capable of automatically performing numerical calculation and/or information processing in accordance with a command set in advance or stored. For example, the server may be a rack server, a blade server, a tower server, or a rack server (including an independent server or a server cluster composed of a plurality of servers). As shown in fig. 11, the computer device 2 includes at least, but is not limited to: the memory 10010, processor 10020, and network interface 10030 may be communicatively linked to each other via a system bus. Wherein:
the memory 10010 includes at least one type of computer-readable storage medium including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the memory 10010 may be an internal storage module of the computer device 2, such as a hard disk or a memory of the computer device 2. In other embodiments, the memory 10010 can also be an external storage device of the computer device 2, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device 2. Of course, the memory 10010 may also include both internal and external memory modules of the computer device 2. In this embodiment, the memory 10010 is generally configured to store an operating system installed in the computer device 2 and various types of application software, such as program codes of the video list pushing method. In addition, the memory 10010 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 10020, in some embodiments, can be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip. The processor 10020 is generally configured to control overall operations of the computer device 2, such as performing control and processing related to data interaction or communication with the computer device 2. In this embodiment, the processor 10020 is configured to execute program codes stored in the memory 10010 or process data.
Network interface 10030 may comprise a wireless network interface or a wired network interface, and network interface 10030 is generally configured to establish a communication link between computer device 2 and another computer device. For example, the network interface 10030 is used to connect the computer device 2 to an external device via a network, establish a data transmission channel and a communication link between the computer device 2 and the external device, and the like. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), or Wi-Fi.
It should be noted that fig. 11 only shows a computer device having the components 10010-10030, but it should be understood that not all of the shown components are required to be implemented, and more or less components may be implemented instead.
In this embodiment, the video list pushing method stored in the memory 10010 can be further divided into one or more program modules and executed by one or more processors (in this embodiment, the processor 10020) to complete the embodiment of the present application.
EXAMPLE six
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the video list push method in embodiments.
In this embodiment, the computer-readable storage medium includes a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the computer readable storage medium may be an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. In other embodiments, the computer readable storage medium may be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the computer device. Of course, the computer-readable storage medium may also include both internal and external storage devices of the computer device. In this embodiment, the computer-readable storage medium is generally used to store an operating system and various types of application software installed in the computer device, for example, the program code of the video list pushing method in the embodiment, and the like. Further, the computer-readable storage medium may also be used to temporarily store various types of data that have been output or are to be output.
It will be apparent to those skilled in the art that the modules or steps of the embodiments of the present application described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different from that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.
It should be noted that the above mentioned embodiments are only preferred embodiments of the present application, and not intended to limit the scope of the present application, and all the equivalent structures or equivalent flow transformations made by the contents of the specification and the drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present application.
Claims (12)
1. A method for pushing a video list, the method comprising:
determining a plurality of videos in response to a video list request provided by a target device;
determining a play quality evaluation value of each video in the plurality of videos in the target device;
sequencing the playing quality evaluation values of the videos from high to low to obtain a sequencing result; and
and generating a target video list and pushing the target video list to the target equipment based on the sequencing result, wherein all videos in the target video list are sequenced from high to low according to respective play quality assessment values.
2. The method according to claim 1, wherein the determining the play quality assessment value of each of the plurality of videos in the target device includes:
calculating the target quality assessment value by one or more of the following parameters:
the target matching degree is the matching degree between the video metadata of each video and the target equipment parameters of the target equipment;
a target playing effect evaluation value which is an evaluation value of the playing effect of each video on the target equipment;
and obtaining the target popularity according to the interactive data of each video.
3. The video list push method according to claim 2, further comprising:
inquiring the mth target matching degree between the target equipment and the mth video from the cache; the cache is used for pre-storing the matching degree between each of one or more videos and different devices, the mth video is any one of the videos, and m is a positive integer greater than 0; and
if the mth target matching degree between the target device and the mth video is not inquired, calculating the mth target matching degree according to the target device parameters and the video meta-information of the mth video.
4. The video list push method according to claim 2, characterized in that the method further comprises:
determining the target equipment grade of the target equipment according to the target equipment parameter;
inquiring an mth target playing effect evaluation value of the mth video from a cache according to the grade of the target equipment and the model of the target equipment; the cache is used for storing playing effect evaluation values of one or more videos on devices with different device grades and different device models in advance, the mth video is any one of the videos, and m is a positive integer greater than 0; and
and if the mth target playing effect evaluation value is not inquired according to the grade of the target equipment and the model of the target equipment, inquiring the mth target playing effect evaluation value from the cache according to the grade of the target equipment.
5. The video list push method according to claim 4, wherein said target device class is one of a plurality of device classes; the method further comprises the following steps of acquiring the play effect evaluation value of each equipment grade in advance:
acquiring a play log of each device, and calculating a play effect evaluation value of each device;
carrying out grade division according to equipment parameters of each equipment, calculating the playing effect evaluation mean value of each equipment grade, and obtaining the mapping relation between each equipment grade and the corresponding playing effect evaluation mean value; the average value of the evaluation of the playing effect of one device level is the average value of the evaluation values of the playing effect of each device under the device level.
6. The video list push method according to any one of claims 3 to 5, wherein said cache is further configured to store:
the popularity degree corresponding to each of the plurality of video identifications, and each video identification is associated with one video;
and the popularity degree of each video label corresponds to, and each video label is respectively associated with one or more videos.
7. The video list push method according to claim 6, further comprising:
according to the mth video identification of the mth video, querying the mth popularity degree associated with the mth video identification from the cache;
and if the mth popularity is not inquired according to the mth video identifier, acquiring the mth popularity from the cache according to one or more video tags associated with the mth video.
8. A video list push system, the system comprising:
a first determining module, configured to determine a plurality of videos in response to a video list request provided by a target device;
a second determining module, configured to determine a playing quality assessment value of each of the plurality of videos in the target device;
the sequencing module is used for sequencing the playing quality evaluation values of the videos from high to low to obtain a sequencing result; and
and the pushing module is used for generating a target video list and pushing the target video list to the target equipment based on the sorting result, wherein all videos in the target video list are sorted from high to low according to respective play quality evaluation values.
9. A method for evaluating playback quality, comprising:
obtaining a plurality of matching degrees according to the video metadata of each video and the equipment parameters of each equipment, and establishing a first mapping relation among each matching degree, the corresponding video and the corresponding equipment; each matching degree is calculated according to the video metadata of one of the videos and the equipment parameters of one of the equipment;
determining the equipment model and the equipment grade of each piece of equipment according to the equipment parameters of each piece of equipment; determining the playing effect evaluation value of each device according to the playing log of each device; establishing a second mapping relation among the equipment models and the equipment grades of the equipment and the corresponding playing effect evaluation values;
calculating the popularity of each video according to the interactive data associated with each video; establishing a third mapping relation between the video identification of each video and the corresponding popularity and/or a fourth mapping relation between the video label of each video and the corresponding popularity, wherein the video label is used for describing the characteristics of the video;
updating the first mapping relation, the second mapping relation, the third mapping relation and the fourth mapping relation into a cache;
the first mapping relation, the second mapping relation, the third mapping relation and the fourth mapping relation are located in the cache and used for being read by a processor and used for evaluating a playing quality evaluation value of the video in the equipment.
10. The playback quality evaluation method according to claim 9, further comprising:
establishing a fifth mapping relation between each equipment grade and the corresponding play effect evaluation mean value; the average value of the evaluation of the playing effect of one device level is the average value of the evaluation values of the playing effect of each device under the device level.
11. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor is adapted to carry out the steps of the method according to any one of claims 1 to 8 and 9 to 10 when executing the computer program.
12. A computer-readable storage medium, in which a computer program is stored which is executable by at least one processor to cause the at least one processor to perform the steps of the method according to any one of claims 1 to 7 and 9 to 10.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111412110.5A CN114095763B (en) | 2021-11-25 | 2021-11-25 | Video list pushing method and play quality assessment method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111412110.5A CN114095763B (en) | 2021-11-25 | 2021-11-25 | Video list pushing method and play quality assessment method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114095763A true CN114095763A (en) | 2022-02-25 |
| CN114095763B CN114095763B (en) | 2024-03-12 |
Family
ID=80304398
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111412110.5A Active CN114095763B (en) | 2021-11-25 | 2021-11-25 | Video list pushing method and play quality assessment method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114095763B (en) |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011217209A (en) * | 2010-03-31 | 2011-10-27 | Sony Corp | Electronic apparatus, content recommendation method, and program |
| US20130046623A1 (en) * | 2011-08-17 | 2013-02-21 | Telefonaktiebolaget L M Ericsson (Publ) | Method For Providing a Recommendation, Recommender System, and Recommender Computer Program Product |
| US20130144871A1 (en) * | 2011-12-02 | 2013-06-06 | Verizon Patent And Licensing Inc. | Dynamic catalog ranking |
| US8813156B1 (en) * | 2012-05-22 | 2014-08-19 | Google Inc. | Resolution recommendation for displaying content items |
| CN104219575A (en) * | 2013-05-29 | 2014-12-17 | 酷盛(天津）科技有限公司 | Related video recommending method and system |
| US20180167619A1 (en) * | 2016-12-12 | 2018-06-14 | Netflix, Inc. | Source-consistent techniques for predicting absolute perceptual video quality |
| CN109729433A (en) * | 2019-01-22 | 2019-05-07 | 北京奇艺世纪科技有限公司 | A kind of video playing appraisal procedure and device |
| CN111125429A (en) * | 2019-12-20 | 2020-05-08 | 腾讯科技（深圳）有限公司 | Video pushing method and device and computer readable storage medium |
| US20200211073A1 (en) * | 2018-12-26 | 2020-07-02 | Samsung Electronics Co., Ltd. | Method for dynamically recommending catalog and electronic device thereof |
-
2021
- 2021-11-25 CN CN202111412110.5A patent/CN114095763B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2011217209A (en) * | 2010-03-31 | 2011-10-27 | Sony Corp | Electronic apparatus, content recommendation method, and program |
| US20130046623A1 (en) * | 2011-08-17 | 2013-02-21 | Telefonaktiebolaget L M Ericsson (Publ) | Method For Providing a Recommendation, Recommender System, and Recommender Computer Program Product |
| US20130144871A1 (en) * | 2011-12-02 | 2013-06-06 | Verizon Patent And Licensing Inc. | Dynamic catalog ranking |
| US8813156B1 (en) * | 2012-05-22 | 2014-08-19 | Google Inc. | Resolution recommendation for displaying content items |
| CN104219575A (en) * | 2013-05-29 | 2014-12-17 | 酷盛(天津）科技有限公司 | Related video recommending method and system |
| US20180167619A1 (en) * | 2016-12-12 | 2018-06-14 | Netflix, Inc. | Source-consistent techniques for predicting absolute perceptual video quality |
| US20200211073A1 (en) * | 2018-12-26 | 2020-07-02 | Samsung Electronics Co., Ltd. | Method for dynamically recommending catalog and electronic device thereof |
| CN109729433A (en) * | 2019-01-22 | 2019-05-07 | 北京奇艺世纪科技有限公司 | A kind of video playing appraisal procedure and device |
| CN111125429A (en) * | 2019-12-20 | 2020-05-08 | 腾讯科技（深圳）有限公司 | Video pushing method and device and computer readable storage medium |
Non-Patent Citations (3)
| Title |
|---|
| YAMAN KUMAR 等: "IceBreaker: Solving Cold Start Problem for Video Recommendation Engines", 《IEEE》 * |
| 张雪纯: "个性化电影主题播单推荐系统的设计与实现", 《中国优秀硕士学位论文全文数据库电子期刊》 * |
| 赵娜;宋佳润;邹文杰;: "面向UHDTV的多分辨率视频显示质量评估研究", 液晶与显示, no. 10 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114095763B (en) | 2024-03-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| He et al. | Rubiks: Practical 360-degree streaming for smartphones | |
| CN110149558B (en) | Video playing real-time recommendation method and system based on content identification | |
| WO2022116888A1 (en) | Method and device for video data processing, equipment, and medium | |
| US10623795B2 (en) | Systems and methods for advertising continuity | |
| US10305947B2 (en) | Pre-buffering audio streams | |
| US9278288B2 (en) | Automatic generation of a game replay video | |
| US11973819B1 (en) | Image recommendations for thumbnails for online media items based on user activity | |
| CN110121098B (en) | Video playing method and device, storage medium and electronic device | |
| KR20150135544A (en) | Video Recommendation based on Video Co-occurrence Statistics | |
| CN109120949B (en) | Video message pushing method, device, equipment and storage medium for video set | |
| CN111858969B (en) | Multimedia data recommendation method, device, computer equipment and storage medium | |
| CN109829063B (en) | Data processing method, device and storage medium | |
| US11388561B2 (en) | Providing a summary of media content to a communication device | |
| US11677796B2 (en) | System and method for video encoding optimization and broadcasting | |
| CN113422988A (en) | Live broadcast cover generation method and device, electronic equipment and storage medium | |
| US11386152B1 (en) | Automatic generation of highlight clips for events | |
| CN114598919A (en) | Video processing method, video processing device, computer equipment and storage medium | |
| CN111597361B (en) | Multimedia data processing method, device, storage medium and equipment | |
| CN110515908B (en) | Photo live broadcasting system and method thereof | |
| CN112055258B (en) | Time delay testing method and device for loading live broadcast picture, electronic equipment and storage medium | |
| CN114095763B (en) | Video list pushing method and play quality assessment method | |
| US8306992B2 (en) | System for determining content topicality, and method and program thereof | |
| US11381887B2 (en) | Systems and methods for managing interruption of content presentation | |
| US20230142432A1 (en) | Content Generating Device, Content Distribution System, Content Generating Method, And Content Generating Program | |
| CN111918140B (en) | Video playing control method and device, computer equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |