CN115225936B - Method, device, equipment and medium for determining definition index of video resource - Google Patents
Method, device, equipment and medium for determining definition index of video resource Download PDFInfo
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- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
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- 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
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- 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
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Abstract
The embodiment of the application provides a method, a device, equipment and a medium for determining a definition index of a video resource, which are used for positioning information related to a definition switching behavior of the video resource so as to accurately reflect the network quality condition of a user watching the video resource from a user side. The method comprises the following steps: acquiring target message data in an XDR ticket of an external data representation method; according to a target uniform resource identifier URI, determining Q-class first characteristic characters and at least one characteristic value corresponding to each class of first characteristic characters, wherein the target URI is determined by analyzing target message data; determining at least one type of second characteristic characters with obvious correlation with the definition switching behavior of the video resource in the Q type of first characteristic characters based on chi-square test; and determining a first target characteristic character with the largest correlation with the definition switching behavior of the video resource in at least one type of second characteristic characters as a definition index of the video resource based on the Pearson column co-efficient.
Description
Technical Field
The application belongs to the field of video detection, and particularly relates to a method, a device, equipment and a medium for determining a definition index of a video resource.
Background
Video services are one of the most rapidly developed services in recent years as high-code-stream, high-concurrency mobile services. Meanwhile, the video definition is an important measurement index of video image quality and network quality, and a user can select video resources with different definition to watch under the condition of different network quality so as to ensure the fluency of video playing.
In the prior art, quality assessment of video resources can only reflect the quality condition of users accessing video resources on the resource side and the network side. In addition, although the indexes obtained by the dial testing technology can evaluate the perception of the user accessing the video service from the user side angle, the dial testing is performed under specific conditions, and the quality of the video resource cannot be comprehensively and truly evaluated.
Therefore, the quality evaluation method for video resources cannot accurately reflect the network quality condition of the video resources watched by the user from the user perception angle.
Disclosure of Invention
The embodiment of the application provides a method, a device, equipment and a medium for determining a definition index of a video resource, which are used for positioning information related to a definition switching behavior of the video resource so as to accurately reflect the network quality condition of a user watching the video resource from a user side.
In a first aspect, an embodiment of the present application provides a method for determining a sharpness index of a video resource, where the method includes:
acquiring target message data in an XDR ticket of an external data representation method, wherein the target message data is the message data reported to a state receiving server by a client when a user performs switching behavior of video resource definition;
determining Q-type first characteristic characters and at least one characteristic value corresponding to each type of first characteristic characters according to a target uniform resource identifier URI, wherein the target URI is determined by analyzing target message data, the first characteristic characters are used for reflecting switching behaviors of video resource definition, and Q is a positive integer;
determining at least one type of second characteristic characters with obvious correlation with the definition switching behavior of the video resource in the Q type of first characteristic characters based on chi-square test;
and determining a first target characteristic character with the largest correlation with the definition switching behavior of the video resource in at least one type of second characteristic characters as a definition index of the video resource based on the Pearson column co-efficient.
In a second aspect, an embodiment of the present application provides a device for determining a sharpness index of a video resource, where the device includes:
The acquisition module is used for acquiring target message data in the XDR ticket of the external data representation, wherein the target message data is the message data reported to the state receiving server by the client when the user performs the switching action of the definition of the video resource;
the first determining module is used for determining Q types of first characteristic characters and at least one characteristic value corresponding to each type of first characteristic characters according to a target uniform resource identifier URI, wherein the target URI is determined by target message data analysis, the first characteristic characters are used for reflecting switching behaviors of video resource definition, and Q is a positive integer;
the second determining module is used for determining at least one type of second characteristic characters with obvious correlation with the definition switching behavior of the video resource in the Q type of first characteristic characters based on chi-square test;
and the third determining module is used for determining a first target characteristic character with the largest correlation with the definition switching behavior of the video resource in at least one type of second characteristic characters as a definition index of the video resource based on the Pearson's Lebber coefficient.
In a third aspect, an embodiment of the present application provides a device for determining a sharpness index of a video resource, where the device includes:
A processor and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the method for determining a sharpness index of a video resource as provided in the first aspect of the embodiment of the present application.
In a fourth aspect, embodiments of the present application provide a computer storage medium having stored thereon computer program instructions that, when executed by a processor, implement a method for determining a sharpness index of a video resource as provided in the first aspect of embodiments of the present application.
According to the method, the device, the equipment and the medium for determining the definition index of the video resource, target message data are obtained from an XDR ticket, URIs are extracted from the target message data, Q characteristic characters and at least one characteristic value corresponding to each characteristic character are determined according to the URIs, the range of the characteristic characters is gradually narrowed according to chi-square test and Pearson column-linkage coefficients, and finally the characteristic character with the largest correlation with the definition switching behavior of the video resource is determined to be the definition index of the video resource. Compared with the prior art, the target message data is the message data which is reported to the state receiving server by the client when the user performs the switching action of the definition of the video resource, so that the demand degree of the user on the flow can be deduced through the preference condition of the definition in the process of browsing the video resource by the user, and the network quality condition of the video resource watched by the user can be accurately reflected from the user side based on the determined information related to the clear switching action of the video resource.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described, and it is possible for a person skilled in the art to obtain other drawings according to these drawings without inventive effort.
Fig. 1 is a flow chart of a method for determining a definition index of a video resource according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a definition index determining apparatus for video resources according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a device for determining a definition index of a video resource according to an embodiment of the present application.
Detailed Description
Features and exemplary embodiments of various aspects of the present application are described in detail below to make the objects, technical solutions and advantages of the present application more apparent, and to further describe the present application in conjunction with the accompanying drawings and the detailed embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative of the application and are not intended to be limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by showing examples of the present application.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
With the rapid development of the internet, network video services are increasingly used in daily life of people, and meanwhile, the network video services become one of the main traffic services of the internet based on the characteristics of high code stream and high concurrency of the network video services. The video definition is continuously broken through from fluency to stream saving, standard definition, high definition, 720P, 1024P and the like, so as to meet the content requirements of video viewers on high tone quality and high image quality of video services. Meanwhile, the video definition is an important measurement index of video image quality and network quality, and users can select video resources with different definition to watch under the condition of different network quality so as to ensure the fluency of video playing.
Therefore, knowing the definition characteristics of the video resources watched by the user plays a vital role in clearly grasping the user behavior preference, the flow demand and the like, and simultaneously, combining the video definition with the katon index can more accurately judge the real condition of the network quality from the user side.
In order to ensure the rapid development of video service and the access perception of watching video resources by users, grasping user behavior preference and the occurrence of the number of blocking times in the process of watching video resources with different definition by the definition characteristics of browsing video resources by users is an indispensable step in the process of ensuring the continuous development of video service. In the prior art, the following two schemes are adopted based on the network quality condition of the user side monitoring access to the video resource:
according to the scheme I, a dial testing technology is adopted, the dial testing probes deployed by each dial testing node are used for performing timing and quantitative dial testing on video packets, the behavior of watching video by a user is simulated, and the definition index, the blocking index and the quality evaluation index which are perceived by a user to be stronger in the process of dial testing can be counted.
The second scheme is that user internet log detected based on DPI is analyzed, DPI refers to deep message analysis technology, based on DPI probe equipment collection, mass internet data are collected to generate XDR ticket, and index data capable of reflecting video quality are obtained by applying quality index algorithm model to the full data of XDR ticket, such as: uplink and downlink rates, client/server time delay, success/failure rate, retransmission rate, and other network quality indexes.
On the one hand, in the prior art, the evaluation index of the network quality can only reflect the network quality condition of the video resource accessed by the user to a certain extent on the resource measurement and network side, and is insufficient for accurately reflecting the network quality condition of the video resource watched by the user from the user perception angle. For example, the downloading rate index is related to the definition of the video resource watched by the user and the video loading time, and when the definition of the video resource selected by the user is different, the downloading rate perceived by the user in the same vision is different; the video resource is loaded in a segmented cache way, namely the speed is higher in the process of buffering, data interaction is not needed after the buffering is finished, and the video loading speed is very low and is nearly 0.
On the other hand, although the index obtained by the dial testing technology in the first scheme can evaluate the perception of the user accessing the video service from the user side angle, the dial testing is always performed under specific conditions, and the user perception cannot be comprehensively and truly evaluated. Meanwhile, the index data collected by the DPI probe device in the second scheme does not include index data that can evaluate the network quality at the user side, for example, the video definition and the number of blocking times of the user accessing the video resource.
Aiming at the problem that the network quality condition of the video resource watched by the user cannot be accurately reflected from the user perception angle, the embodiment of the application provides a definition index determining method of the video resource, and the demand degree of the user on the flow is deduced through the preference condition of the video definition in the process of browsing the video resource by the user and the clamping condition in the process of switching the video definition, so that the network quality condition of the video resource watched by the user is accurately reflected from the user side based on the information related to the clear switching behavior of the video resource.
As shown in fig. 1, an embodiment of the present application provides a method for determining a definition index of a video resource, where the method includes:
s101, acquiring target message data in an XDR ticket of an external data representation, wherein the target message data is the message data reported to a state receiving server by a client when a user performs video resource definition switching behavior.
It should be noted that, under the condition that the user watches the video resource to perform the sharpness switching operation, the client side will automatically report the message information with the change of the sharpness characteristics of the video before and after switching to the state receiving server, acquire the message information for performing the sharpness switching operation of the video when the user browses the video resource through the wireshark packet capturing software, and extract the target message data related to the sharpness switching behavior from the message information for evaluating the sharpness switching condition when the user watches the video.
Specifically, the network sniffing packet capturing tool wireshark is utilized to capture packets of all interactive information generated when a user watches video resources through clients such as a web browser, a web client, a mobile phone APP, and the like, and the method comprises the following steps:
step A: when a user watches video resources through a client, firstly, a request for watching video episodes is sent to a page server; and the page server responds to the request and returns the server IP where the video episode entity resource is located.
And (B) step (B): a user sends a request message of a video entity to a server (i.e. a video server) where the video episode entity is located; the video server responds to the user request and returns a video entity fragmentation message, wherein the video entity fragmentation message at least carries video resource feature codes and video definition information.
Step C: under the condition that a user performs definition switching operation on the watched video resources, a request switching message is sent to a page server; and the page server responds to the switching request and returns the server IP where the entity resource of the video episode with specified definition is located.
Step D: the user sends a request message of appointed definition video episode to the IP (video server); the video server responds to the request information and returns a video entity fragment message, and after the video definition switching operation of the standby user is completed, the client side reports the related message information of the video definition before and after the switching to a state receiving server (namely a user log management platform).
Step E: after the state receiving server acquires the related message information for switching the video definition, the related message information is grabbed by a wireshark tool by referring to the DPI probe depth message analysis capability and the XDR ticket synthesis function, and target message data of definition switching behavior when a user watches video resources is determined.
S102, determining Q types of first characteristic characters and at least one characteristic value corresponding to each type of first characteristic characters according to a target uniform resource identifier URI, wherein the target URI is determined by target message data analysis, the first characteristic characters are used for reflecting switching behaviors of video resource definition, and Q is a positive integer.
The purpose of the embodiment of the application is to gradually reduce the selection range of the characteristic characters, and finally determine the type of the characteristic characters with the largest correlation with the video definition switching behavior, so that a plurality of characteristic characters and corresponding characteristic values thereof are required to be classified according to different characteristic character types and divided into Q type first characteristic characters and at least one characteristic value corresponding to each type of first characteristic characters.
S103, determining at least one type of second characteristic characters with obvious correlation with the definition switching behavior of the video resource in the Q type of first characteristic characters based on chi-square test.
Chi-square testing is a widely used hypothesis testing method that can be applied to chi-square testing of multiple rates or multiple constituent ratios and correlation analysis of classification data in statistical inference of classification data. Therefore, in the embodiment of the application, chi-square test is used as a key step for detecting whether each type of first characteristic character has correlation with video definition switching behavior.
Specifically, chi-square test is performed on each characteristic character and the corresponding characteristic value in the Q-class first characteristic characters respectively. And taking the characteristic character key and q value values corresponding to the characteristic character key in the target packet as V1/V2/…/Vq. Taking the value of the definition and the value corresponding to the key as a list table, and checking the correlation between the definition characteristic and the key parameter by using chi-square checking, wherein the original assumption is H0: the sharpness feature is independent of the Key parameter, and the alternative assumption H1 is: the sharpness feature is related to the Key parameter. The degree of freedom df= (X1-1) ×x2-1 (X1 represents the number of lines, represents the definition type, such as current saving, fluency, high definition, super definition, blue light, etc.), and X2 represents the number of columns, represents the value type of the key).
The chi-square test statistics of the Q first characteristic characters are obtained through a chi-square test formula, and the chi-square test can be represented by a formula 1:
And carrying out chi-square statistic evaluation on all key combinations in R1 in the mode, comparing the chi-2 result data with a chi-square test critical value table, and taking the key with correlation as a second characteristic character.
S104, determining a first target characteristic character with the largest correlation with the definition switching behavior of the video resource in at least one type of second characteristic characters as a definition index of the video resource based on the Pearson column coefficient.
And the first target characteristic character with the largest correlation coefficient with the video definition is determined through the Pearson parallel coefficient and is used as a unique identifier of the video definition characteristic index so as to position the domain of the video definition information before and after the definition switching operation is performed when a user accesses the video resource.
Specifically, the Pearson column-wise coefficient is shown in formula 2:
as can be seen from the formula 2, the value is between 0 and 1. Calculating the correlation between each first characteristic character key in the set R2 and the definition through the Pearson column-associated coefficient, wherein when the correlation between the definition and the key value is smaller, the Pearson column-associated coefficient C is smaller and gradually approaches to 0; the Pearson column co-efficient C is larger and approaches 1 as the sharpness is more correlated with the key value. And comparing and positioning the key which is strongly related to the definition index, and taking the key as a unique characteristic identifier of the video definition.
According to the definition index determining method for the video resource, target message data is obtained from an XDR ticket, URIs are extracted from the target message data, Q characteristic characters and at least one characteristic value corresponding to each characteristic character are determined according to the URIs, the range of the characteristic characters is gradually narrowed according to chi-square test and Pearson column joint coefficients, and finally the characteristic character with the largest correlation with the definition switching behavior of the video resource is determined to be the definition index of the video resource. Compared with the prior art, the target message data is the message data which is reported to the state receiving server by the client when the user performs the switching action of the definition of the video resource, so that the demand degree of the user on the flow can be deduced through the preference condition of the definition in the process of browsing the video resource by the user, and the network quality condition of the video resource watched by the user can be accurately reflected from the user side based on the determined information related to the clear switching action of the video resource.
In some embodiments, before determining the Q class first feature characters and at least one feature value corresponding to each class first feature character according to the target uniform resource identifier URI, the method further includes:
Analyzing the target message data and determining at least one URI;
and filtering the URI which does not contain the characteristic characters in at least one URI to obtain a target URI.
Specifically, the target message data is parsed, and at least one URI contained in the target message data is determined, wherein the URI is a character string for identifying a certain internet resource name, and the format of the character string is as follows:
protocol? Query parameter D & query parameter E# shard F
For example: http: v/data.video.iqiyi.com/video/v 0/20190108/6c/8c/f3db5fefcb98604d15fb3daba 46ef2f6.f4vqd_tvid=1776034400 & qd_vipers=0 & qd_index=1 & qd_aid=216266201 & qd_st=0 & qd_sc=c 7548401dd7a4f30cb46639fcff08492& qd_sc=7b3018e 0197bf64179d 3d0683a7& qd_p=7c62 dca & qd_k=2 d.c=7d. 0e2c601663 cbfb00 fb213a688da546b926& qd_src=01010031010000000000 & qd_vipdyn=0 & qd_uid= & qd_tm=1551018003342 & qd_vip=0 & cross-domain=1 & qyid=58486728ed4bab00e4b8dca38a48a & qypid=1776034400_02020010000000000 & qypid=1776034400_020200310100010000000000 & rn & gt200310200100010000000000 & rn=1551551551558016 & ltv=0.1 & cross-domain=1 & ltqyiac=shijiazhuang_cm0906 & pv=0.1 & cross-domain
Analyzing the URI and determining the following information:
Protocol: http; domain name: video. The catalogue and the file are:
video/v 0/20190108/6c/8c/f3db5fefcb98604d15fb3daba46ef2f6.f4v; the query parameters are: qd_tvid=1776034400 & qd_vipres=0 & qd_index=1 & qd_aid=216266201 & qd_stert=0 & qd_scc=c 7548401dd7a4f30cb46639fcff08492& qd_sc=7b 0103018e 0197bf64719 d0c3d0683a7& qd_p=b7c62 dca & qd_k=0e2c 6063 cbmf00 fb213a688da546b926& qd_src=01031010000000000 & qd_vipdyn =0 & qd_uid= & qd_tm= 1551018003342& qd_vip=0 & cross-domain=1 & qdid=58486728edd4bab 00e4b8d ca38a8d48a & qd pid=1776034400_02020010000000000 & qypid=1776034400_02020031010000000 & rn=155101101801010906 & pv=0.1 & cross-domain=1 & pri_idc=shijiazhuang_cmnet
Based on the description of the URI, the URI which does not contain the query parameter is filtered to determine the target URI, wherein the query parameter contains the characteristic characters and the corresponding characteristic values.
The analysis of the target message data and the filtering of the URI extract the target URI which can identify the video resource definition switching behavior, so that the calculated amount in the chi-square inspection process is reduced, and the inspection efficiency is improved.
In some embodiments, determining the Q class first feature characters and at least one feature value corresponding to each class first feature character according to the target uniform resource identifier URI includes:
extracting M third characteristic characters and characteristic values corresponding to each third characteristic character from the target URI, wherein M is a positive integer;
filtering M third characteristic characters and characteristic values corresponding to each third characteristic character, and determining N fourth characteristic characters and characteristic values corresponding to each fourth characteristic character, wherein N is a positive integer which is smaller than M and not smaller than Q;
based on the feature character types, classifying N fourth feature characters and feature values corresponding to each fourth feature character, and determining Q class first feature characters and at least one feature value corresponding to each class first feature character.
There is still a lot of redundant information in the target URI and thus further feature extraction is needed to determine the first feature character for chi-square verification.
Specifically, target message data of M times of video definition switching operation in the process of browsing video resources by a user is obtained based on a dial testing technology, target URIs in the target message data are extracted, each target URI record is converted, M third characteristic characters and corresponding characteristic values < kj, vj >, kj and vj are generated to exist in pairs, so that global data R, R= { < u1, u1 (k), u1 (v) >, …, < um, um (k), um (v) > }, and the size of R depends on the order of magnitude of the query parameters in the target message data.
In some embodiments, filtering the M third feature characters and feature values corresponding to each third feature character to determine N fourth feature characters and feature values corresponding to each fourth feature character includes:
based on the video resource attributes of the URIs to which the feature characters and the corresponding feature values belong, grouping M third feature characters and the feature values corresponding to each third feature character, and determining at least one feature pair of a plurality of groups of different video resource attributes, wherein the feature pair comprises the feature characters and the corresponding feature values;
for at least one feature pair in each group, the following steps are performed:
and filtering at least two feature pairs with the same target feature values corresponding to the second target feature characters, wherein the second target feature characters are any feature character in the feature pairs.
Specifically, the < k, v > value pairs in each record reported to the state receiving server after M video sharpness switching operations are compared, key-value combination domains with k the same as v in M records are filtered and removed (the key-value pairs cannot be identified as sharpness characteristics because the key-value pairs are unchanged), the rest N fourth characteristic characters and the corresponding characteristic values key-value pairs are statistically summarized to form a new set r1= { < ui, ua (k), ua (v) > < ui, ub (k), ub (v) > …, < un, un (k), un (v) > }, the number of domains contained in R1 is the same as the number of domains contained in the set R, but the number of key-value pairs of each domain in R1 is smaller than the number of key-value pairs of each domain in R, and the corresponding characteristic values in Q are respectively checked for each key in the set R1 according to different types of the key pairs.
In some embodiments, determining at least one type of second feature character of the Q type of first feature characters that has significant relevance to the sharpness switching behavior of the video resource based on the chi-square test may include:
for each type of second characteristic character, the following steps are respectively executed:
establishing a list by taking at least one definition value of the video resource and at least one characteristic value corresponding to the first characteristic character;
carrying out chi-square test on the list, and determining the degree of freedom and chi-square statistic value of the first characteristic characters;
determining a chi-square test critical value according to the degree of freedom and a preset significance level;
and determining the first characteristic characters with the chi-square statistic value larger than the chi-square inspection critical value as second characteristic characters with obvious correlation with the definition switching behavior of the video resource.
Specifically, chi-square test is performed on each characteristic character and the corresponding characteristic value in the Q-class first characteristic characters respectively. And taking the characteristic character key and q value values corresponding to the characteristic character key in the target packet as V1/V2/…/Vq. Taking the value of the definition and the value corresponding to the key as a list table, and checking the correlation between the definition characteristic and the key parameter by using chi-square checking, wherein the original assumption is H0: the sharpness feature is independent of the Key parameter, and the alternative assumption H1 is: the sharpness feature is related to the Key parameter. The degree of freedom df= (X1-1) ×x2-1 (X1 represents the number of lines, represents the definition type, such as current saving, fluency, high definition, super definition, blue light, etc.), and X2 represents the number of columns, represents the value type of the key).
The chi-square test statistics of each group of keys are obtained through a chi-square test formula, and the chi-square test can be represented by a formula 1:
assuming that the target packet key value includes 3 types V1, V2, and V3, and the definition includes the current-saving, standard definition, high definition, and blue light 4 types, the degree of freedom df= (4-1) ×3-1) =6. The process of obtaining the chi-square statistic value of key1 by the chi-square test evaluation calculation formula can be represented by the following table 2:
TABLE 2 calculation of chi-square statistics
Chi-square test evaluation was performed according to the data of table 2 above, and the chi-square statistic value of key1 could be obtained by equation 3:
χ 2 ＝(M1-(M1+M2+M3)(M1+N1+P1+Q1)/L)2/(M1+M2+M3)(M1+N1+P1+Q1)/L+(M2-(M1+M2+M3)(M2+N2+P2+Q2)/L)2/(M1+M2+M3)(M2+N2+P2+Q2)/L+…+(Q3-(Q1+Q2+Q3)(M3+N3+P3+Q3)/L)2/(Q1+Q2+Q3)(M3+N3+P3+Q3)/L
wherein, the liquid crystal display device comprises a liquid crystal display device,
chi-square statistical value calculated by formula 3 2 Comparing with a preset chi-square test critical value table, (the degree of freedom of key1 is 6), if the chi-square value of the key is larger than 12.592 (alpha=0.05 is taken), we reject the original assumption, and consider that the key has a relation with the definition index.
Chi-square statistic evaluation is carried out on all key combinations in R according to the mode, and chi is calculated 2 And comparing the result data with a chi-square test critical value table, and taking the key value with correlation as a second characteristic character.
The preset chi-square test threshold value table is shown in table 3:
TABLE 3 chi-square test Critical Table
In some embodiments, determining, as the sharpness index of the video resource, a first target feature character having the greatest correlation with sharpness switching behavior of the video resource from among the at least one second feature character based on the pearson list coefficient includes:
respectively calculating the Pearson Lebby coefficient of each second characteristic character;
and determining the first target characteristic character with the maximum value of the Pelson coefficient as a definition index of the video resource.
Specifically, the Pearson column-wise coefficient is shown in formula 2:
as can be seen from the formula 2, the value is between 0 and 1. Calculating the correlation between each first characteristic character key in the set R2 and the definition through the Pearson column-associated coefficient, wherein when the correlation between the definition and the key value is smaller, the Pearson column-associated coefficient C is smaller and gradually approaches to 0; the Pearson column co-efficient C is larger and approaches 1 as the sharpness is more correlated with the key value. And comparing and positioning the key which is strongly related to the definition index, and taking the key as a unique characteristic identifier of the video definition.
In some embodiments, after filtering URIs that do not contain feature characters in at least one URI to obtain a target URI, the method further comprises:
dividing the target URI by using a plurality of preset symbols;
And extracting at least the video resource attribute, the characteristic character and the corresponding characteristic value in the target URI according to the segmentation result.
Because the target URI carries information such as a video name, a URI, a domain name, a query parameter and the like, wherein the query parameter comprises a feature character key and a feature value corresponding to each feature character, the target URI needs to be segmented and extracted so as to be convenient for statistical analysis of the feature characters.
Specifically, after determining the target URI, one can use: ' v ', ' v? The target URIs are segmented by preset symbols such as',', and the like, the feature character keys in the domain name and the query parameters in each target URI and the feature value corresponding to each feature character are extracted, and the extraction result can be shown in the table 4:
TABLE 4 extraction results of target URIs
As can be seen from table 2, when the user U performs the sharpness switching operation (for example, switches from the stream-saving mode to the blue-light mode) while watching the video episode a and the video episode B, a total of 5 domain names and 5 URIs with query parameters are obtained, and a total of 15 keys and values are obtained by splitting, where the domain names, keys, and values may be the same or different.
The method for determining the definition index of the video resource provided in the embodiment of the present application is described in detail below in a specific embodiment.
1. Obtaining message information
And opening a video resource playing interface, clicking a first set of video resources L for viewing, simultaneously opening a wireshark packet capturing tool to acquire message information of a user for viewing the video resources L, and finally acquiring the URI 308 strips based on video definition switching operation performed for a plurality of times.
2. Message information is initially processed based on URI rules
Filtering and dividing 308 URIs acquired in the first step based on the URI rule, and extracting key and value values in domain names and query parameters in each URI, as shown in table 5:
TABLE 5 message information extraction results
3. Determining key strongly related to video definition identifier in URI based on chi-square test and Pearson column-linkage coefficient
3.1, ticket filtering and feature pair extraction
The message information contains 45 target message data for reporting the user information due to the video definition before and after switching, namely 45 effective available records of msg.qy.net are filtered out according to the domain name.
3.2 feature pair filtering
And comparing 45 video definition switching operations, and reporting the key-value combination fields with the same k and v in the 45 records to the query parameter and parameter value pairs in each record of the state receiving server, and filtering and eliminating the key-value combination fields with the same k and v in the 45 records (because the key-value values are unchanged, the keys can be judged to be incapable of identifying definition characteristics), wherein if the parameter values of the two records corresponding to the bstp parameter in the video resource L are 6, the query parameter and parameter value pairs are eliminated. In 45 records, the remaining query parameter and parameter value pairs are used as Q groups for chi-square test.
3.3 chi-square test
And (3) respectively carrying out chi-square test on the Q group data obtained in the step (3.2), and carrying out test on the correlation between the definition characteristics and the tra3 parameters by using the chi-square test, wherein the original assumption is H0: the sharpness feature is independent of the tra3 parameter, assuming H1 as: the sharpness characteristic is related to the tra3 parameter. Taking the Value of definition and the Value corresponding to the tra3 parameter as a list table, if the degree of freedom df= (X1-1) = (X2-1) = (X1 represents the number of rows and represents the definition category, such as 7 categories of current saving, fluency, high definition, super definition, blue light and the like, and X2 represents the number of columns and represents the Value category of tra3, and 4 categories in total), if df= (7-1) = (4-1) = 18.
The chi-square test statistic of the tra3 parameter is obtained through the chi-square test formula: 65. taking this as an example, the correlation between the sharpness feature and other query parameters o3, aid, pu, ra1 and va is checked by using chi-square test, and the verification results are shown in table 6:
table 6 chi-square test results
| Key | Degree of freedom | Chi square value | Level of significance |
| tra3 | 18 | 65 | 28.8 |
| o3 | 12 | 421 | 21.03 |
| Aid | 12 | 121 | 21.03 |
| pu | 8 | 83 | 15.51 |
| ra1 | 20 | 214 | 31.41 |
| va | 16 | 86 | 26.3 |
As can be seen from the contents of Table 6, the chi-square statistics are all greater than the chi-square test threshold, and the original assumption can be rejected for the above 6 query parameters, and the sharpness characteristics are considered to be related to the above 6 parameters. 3.4, locating the key value with strong correlation based on Pearson column-linkage coefficient
And locating a key value with strong correlation with the definition index through the Pearson column joint coefficient, and using the key value as a unique characteristic identifier of the video definition index. And respectively carrying out statistical calculation on the tra3, o3, aid, pu, ra1 and va parameters which have relation with the definition index in the chi-square test result in the third step through a Pearson column-linkage coefficient formula, wherein the corresponding column-linkage coefficient corresponds to the table 7:
TABLE 7 Pearson column-co-factor results
| key | Chi square value | n is a value | Pearson column co-efficient |
| tra3 | 65 | 35 | 0.650 |
| o3 | 421 | 45 | 0.903 |
| aid | 121 | 27 | 0.818 |
| pu | 83 | 35 | 0.703 |
| ra1 | 214 | 25 | 0.895 |
| va | 86 | 23 | 0.789 |
Comparing the result with the standard values 1 and 0, the Pearson column-association coefficient of o3 is the maximum value closest to 1, so that the correlation between o3 and the definition index is determined to be the maximum, namely, the o3 parameter in uri is determined to be the unique identification field of the definition index.
According to the specific embodiment of the method for determining the definition index of the video resource, the range of the characteristic characters is gradually narrowed according to the chi-square test and the Pearson column-linkage coefficient, and finally the characteristic character o3 with the largest correlation with the definition switching behavior of the video resource is determined to be the unique index of the definition of the video resource. Compared with the prior art, the demand degree of the user on the flow can be deduced through the preference condition of the user on the definition in the process of browsing the video resource, and the network quality condition of the user watching the video resource is accurately reflected from the user side based on the determined information related to the clear switching behavior of the video resource.
Based on the same inventive concept, the embodiment of the application also provides a definition index determining device of video resources.
As shown in fig. 2, an embodiment of the present application provides a device for determining a definition index of a video resource, which may include:
the obtaining module 201 is configured to obtain target message data in an XDR ticket of an external data representation, where the target message data is message data that is reported to a state receiving server by a client when a user performs a switching behavior of video resource definition;
a first determining module 202, configured to determine, according to a target uniform resource identifier URI, a Q-class first feature character and at least one feature value corresponding to each class of first feature character, where the target URI is determined by parsing the target message data, the first feature character is used to reflect a switching behavior of video resource definition, and Q is a positive integer;
a second determining module 203, configured to determine, based on a chi-square test, at least one type of second feature characters that has a significant correlation with the sharpness switching behavior of the video resource from the Q type of first feature characters;
the third determining module 204 is configured to determine, based on the pearson list coefficient, a first target feature character having a greatest correlation with the sharpness switching behavior of the video resource from the at least one second feature character as a sharpness index of the video resource.
In some embodiments, the apparatus may further comprise:
the analysis module is used for analyzing the target message data and determining at least one URI;
and the filtering module is used for filtering the URI which does not contain the characteristic characters in the at least one URI to obtain a target URI.
In some embodiments, the first determination module may include:
the extraction unit is used for extracting M third characteristic characters and characteristic values corresponding to each third characteristic character in the URI, wherein M is a positive integer;
the filtering unit is used for filtering the M third characteristic characters and the characteristic values corresponding to each third characteristic character, and determining N fourth characteristic characters and the characteristic values corresponding to each fourth characteristic character, wherein N is a positive integer which is less than M and not less than Q;
the classification unit is used for classifying the N fourth characteristic characters and the characteristic values corresponding to each fourth characteristic character based on the characteristic character types and determining Q class first characteristic characters and at least one characteristic value corresponding to each class first characteristic character.
The filter unit is specifically used for:
grouping the M third feature characters and the feature values corresponding to each third feature character based on the video resource attributes of the URIs to which the feature characters and the corresponding feature values belong, and determining at least one feature pair of a plurality of groups of different video resource attributes, wherein the feature pair comprises the feature characters and the corresponding feature values;
For at least one feature pair in each group, the following steps are performed:
and filtering at least two feature pairs with the same target feature values corresponding to the second target feature characters, wherein the second target feature characters are any feature character in the feature pairs.
In some embodiments, the second determining module may be specifically configured to:
for each type of the second characteristic characters, the following steps are respectively executed:
establishing a list by using the value of at least one definition of the video resource and at least one characteristic value corresponding to the first characteristic character;
carrying out chi-square test on the list, and determining the degree of freedom and chi-square statistic value of the first characteristic characters;
determining a chi-square test critical value according to the degree of freedom and a preset significance level;
and determining the first characteristic characters with the chi-square statistic value larger than the chi-square inspection critical value as the second characteristic characters with obvious correlation with the definition switching behavior of the video resource.
In some embodiments, the third determining module may be specifically configured to:
respectively calculating the Piercon coefficient of each second characteristic character;
And determining the first target characteristic character with the maximum value of the Pelson coefficient as the definition index of the video resource.
In some embodiments, the apparatus may further comprise:
and the acquisition module is used for analyzing the DPI probe through the deep message and acquiring the XDR ticket.
In some embodiments, the apparatus may further comprise:
the segmentation module is used for segmenting the target URI by utilizing a plurality of preset symbols;
and the extraction module is used for extracting at least the video resource attribute, the characteristic character and the corresponding characteristic value in the target URI according to the segmentation result.
Other details of the device for determining the definition index of the video resource provided according to the embodiment of the present application are similar to those of the method for determining the definition index of the video resource according to the embodiment of the present application described above in connection with fig. 1, and are not described herein.
Fig. 3 is a schematic hardware structure diagram of definition index determination of video resources according to an embodiment of the present application.
The method and the device for determining the definition index of the video resource according to the embodiments of the present application described in connection with fig. 1 and fig. 2 may be implemented by a definition index determining device of the video resource. Fig. 3 is a schematic diagram showing a hardware configuration 300 of a definition index determination device of a video asset according to an embodiment of the invention.
A processor 301 and a memory 302 storing computer program instructions may be included in the sharpness index determination arrangement of the video resource.
In particular, the processor 301 may include a central processing unit (Central Processing Unit, CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured to implement one or more integrated circuits of embodiments of the present application.
In one example, memory 302 may be Read Only Memory (ROM). In one example, the ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.
The processor 301 reads and executes the computer program instructions stored in the memory 302 to implement the methods/steps S101 to S103 in the embodiment shown in fig. 1, and the methods/steps S201 to S205 in the embodiment shown in fig. 2, and achieve the corresponding technical effects achieved by executing the methods/steps in the embodiment shown in fig. 1 and/or fig. 2, which are not described herein for brevity.
In one example, the sharpness index determination device of the video asset may further include a communication interface 303 and a bus 310. As shown in fig. 3, the processor 301, the memory 302, and the communication interface 303 are connected to each other by a bus 310 and perform communication with each other.
The communication interface 303 is mainly used to implement communication between each module, device, unit and/or apparatus in the embodiments of the present application.
According to the definition index determining device for the video resources, the demand degree of the user on the flow can be deduced through the preference condition of the definition in the process of browsing the video resources by the user, and the network quality condition of the user watching the video resources is accurately reflected from the user side based on the determined information related to the clear switching behavior of the video resources.
In addition, in combination with the method for determining the definition index of the video resource in the above embodiment, the embodiment of the application may be implemented by providing a computer storage medium. The computer storage medium has stored thereon computer program instructions; the computer program instructions, when executed by a processor, implement a method for determining a sharpness index of a video asset in any of the above embodiments.
It should be clear that the present application is not limited to the particular arrangements and processes described above and illustrated in the drawings. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and those skilled in the art can make various changes, modifications, and additions, or change the order between steps, after appreciating the spirit of the present application.
The functional blocks shown in the above-described structural block diagrams may be implemented in hardware, software, firmware, or a combination thereof. When implemented in hardware, it may be, for example, an electronic circuit, an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), suitable firmware, a plug-in, a function card, or the like. When implemented in software, the elements of the present application are the programs or code segments used to perform the required tasks. The program or code segments may be stored in a machine readable medium or transmitted over transmission media or communication links by a data signal carried in a carrier wave. A "machine-readable medium" may include any medium that can store or transfer information. Examples of machine-readable media include electronic circuitry, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio Frequency (RF) links, and the like. The code segments may be downloaded via computer networks such as the internet, intranets, etc.
It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above-described steps, that is, the steps may be performed in the order mentioned in the embodiments, may be different from the order in the embodiments, or several steps may be performed simultaneously.
Aspects of the present disclosure are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to being, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware which performs the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In the foregoing, only the specific embodiments of the present application are described, and it will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the systems, modules and units described above may refer to the corresponding processes in the foregoing method embodiments, which are not repeated herein. It should be understood that the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present application, which are intended to be included in the scope of the present application.
Claims (11)
1. A method for determining a sharpness index of a video asset, the method comprising:
acquiring target message data in an XDR ticket of an external data representation method, wherein the target message data is the message data reported to a state receiving server by a client when a user performs switching behavior of video resource definition;
determining Q-type first characteristic characters and at least one characteristic value corresponding to each type of first characteristic characters according to a target uniform resource identifier URI, wherein the target URI is determined by analyzing target message data, the first characteristic characters are used for reflecting switching behaviors of video resource definition, and Q is a positive integer;
based on chi-square test, determining at least one type of second characteristic characters with obvious correlation with the definition switching behavior of the video resource in the Q type of first characteristic characters;
and determining a first target characteristic character with the largest correlation with the definition switching behavior of the video resource in the at least one type of second characteristic characters as a definition index of the video resource based on the Pearson column coefficient.
2. The method of claim 1, wherein prior to determining the class Q first feature and the at least one feature value corresponding to each class first feature from the target uniform resource identifier URI, the method further comprises:
Analyzing the target message data and determining at least one URI;
and filtering the URI which does not contain the characteristic characters in the at least one URI to obtain a target URI.
3. The method of claim 1, wherein determining the Q class first feature and the at least one feature value corresponding to each class first feature according to the target uniform resource identifier URI comprises:
extracting M third characteristic characters and characteristic values corresponding to each third characteristic character from the target URI, wherein M is a positive integer;
filtering the M third characteristic characters and characteristic values corresponding to each third characteristic character, and determining N fourth characteristic characters and characteristic values corresponding to each fourth characteristic character, wherein N is a positive integer which is less than M and not less than Q;
and classifying the N fourth characteristic characters and the characteristic values corresponding to each fourth characteristic character based on the characteristic character types, and determining Q class first characteristic characters and at least one characteristic value corresponding to each class first characteristic character.
4. The method of claim 3, wherein the filtering the M third feature characters and the feature values corresponding to each third feature character to determine N fourth feature characters and the feature values corresponding to each fourth feature character comprises:
Grouping the M third feature characters and the feature values corresponding to each third feature character based on the video resource attributes of the URIs to which the feature characters and the corresponding feature values belong, and determining at least one feature pair of a plurality of groups of different video resource attributes, wherein the feature pair comprises the feature characters and the corresponding feature values;
for at least one feature pair in each group, the following steps are performed:
and filtering at least two feature pairs with the same target feature values corresponding to the second target feature characters, wherein the second target feature characters are any feature character in the feature pairs.
5. The method of claim 1, wherein determining at least one type of second feature character of the type Q first feature character that has significant relevance to the sharpness switching behavior of the video asset based on a chi-square test comprises:
for each type of the second characteristic characters, the following steps are respectively executed:
establishing a list by using the value of at least one definition of the video resource and at least one characteristic value corresponding to the first characteristic character;
carrying out chi-square test on the list, and determining the degree of freedom and chi-square statistic value of the first characteristic characters;
Determining a chi-square test critical value according to the degree of freedom and a preset significance level;
and determining the first characteristic characters with the chi-square statistic value larger than the chi-square inspection critical value as the second characteristic characters with obvious correlation with the definition switching behavior of the video resource.
6. The method of claim 5, wherein determining, based on the pearson-list coefficient, a first target feature character of the at least one second feature character that has a greatest correlation with a sharpness switching behavior of the video resource as a sharpness indicator of the video resource comprises:
respectively calculating the Piercon coefficient of each second characteristic character;
and determining the first target characteristic character with the maximum value of the Pelson coefficient as the definition index of the video resource.
7. The method of claim 1, wherein prior to the obtaining the target message data in the external data representation XDR ticket, the method further comprises:
and analyzing the DPI probe through the deep message, and collecting the XDR ticket.
8. The method of claim 2, wherein after said filtering URIs that do not contain feature characters in said at least one URI to obtain a target URI, the method further comprises:
Dividing the target URI by using a plurality of preset symbols;
and extracting at least the video resource attribute, the characteristic character and the corresponding characteristic value in the target URI according to the segmentation result.
9. A definition index determining apparatus for a video resource, the apparatus comprising:
the system comprises an acquisition module, a state receiving server and a state receiving server, wherein the acquisition module is used for acquiring target message data in an XDR ticket of an external data representation, wherein the target message data is the message data reported to the state receiving server by a client when a user performs switching behavior of video resource definition;
the first determining module is used for determining Q types of first characteristic characters and at least one characteristic value corresponding to each type of first characteristic characters according to a target uniform resource identifier URI, wherein the target URI is determined by analyzing target message data, the first characteristic characters are used for reflecting switching behaviors of video resource definition, and Q is a positive integer;
the second determining module is used for determining at least one type of second characteristic characters with obvious correlation with the definition switching behavior of the video resource in the Q type of first characteristic characters based on chi-square test;
and the third determining module is used for determining a first target characteristic character with the largest correlation with the definition switching behavior of the video resource in the at least one type of second characteristic characters as a definition index of the video resource based on the Pearson column coefficient.
10. A sharpness index determining apparatus for a video asset, the apparatus comprising: a processor and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the method of determining a sharpness indicator of a video resource according to any of claims 1-8.
11. A computer storage medium having stored thereon computer program instructions which, when executed by a processor, implement the method of sharpness index determination of a video resource according to any of claims 1-8.
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