Video quality assessment method, device and electronic equipment

Abstract

The present disclosure provides a method, apparatus, and electronic device for video quality assessment, relating to the field of artificial intelligence technology, particularly video assessment. The implementation scheme comprises: obtaining quality assessment data for a video comprising multiple video segments, wherein the quality assessment data includes a score on at least one quality assessment dimension; determining, for each quality assessment dimension, a score for at least one video segment evaluated on the quality assessment dimension among the multiple video segments; and determining a quality assessment result for the video based on one or more video segments in the at least one video segment having the same predetermined score.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, in particular to the field of data processing, and specifically to a method, device, electronic device, computer-readable storage medium, and computer program product for video quality assessment. Background Art

[0002] With innovations in content production, the use of AI (artificial intelligence) to automatically generate videos from images and text has become commonplace. This technology significantly enhances the user experience of text content. However, the quality of videos produced using AI often varies greatly, and video platforms often need to evaluate video quality to filter out substandard videos and ensure the quality of the videos they publish. Efficient and cost-effective video quality assessment has been a hot topic in this field.

[0003] The approaches described in this section are not necessarily approaches that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any approach described in this section is prior art simply by virtue of its inclusion in this section. Similarly, unless otherwise indicated, the issues raised in this section should not be considered as having been recognized in any prior art. Summary of the Invention

[0004] The present disclosure provides a method, apparatus, electronic device, computer-readable storage medium, and computer program product for video quality assessment.

[0005] According to one aspect of the present disclosure, a video quality assessment method is provided, comprising: obtaining quality assessment data for a video comprising a plurality of video segments, wherein the quality assessment data comprises a score on at least one quality assessment dimension; determining, for each quality assessment dimension, a respective score of at least one video segment evaluated on the quality assessment dimension among the plurality of video segments; and determining a quality assessment result of the video based on one or more video segments having a predetermined identical score among the at least one video segment.

[0006] According to another aspect of the present disclosure, a video quality assessment apparatus is provided, comprising: a data acquisition module configured to acquire quality assessment data of a video comprising a plurality of video segments, wherein the quality assessment data comprises a score on at least one quality assessment dimension; a score determination module configured to determine, for each quality assessment dimension, a score of each of at least one video segment evaluated on the quality assessment dimension in the plurality of video segments; and a result determination module configured to determine a quality assessment result of the video based on one or more video segments having a predetermined identical score in the at least one video segment.

[0007] According to another aspect of the present disclosure, a computer device is provided, comprising at least one processor; and a memory on which a computer program is stored, wherein when the computer program is executed by the processor, the processor executes the method provided above in the present disclosure.

[0008] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the processor executes the method provided above in the present disclosure.

[0009] According to another aspect of the present disclosure, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the processor is caused to perform the method provided above in the present disclosure.

[0010] According to one or more embodiments of the present disclosure, video quality can be evaluated efficiently and at low cost.

[0011] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The accompanying drawings illustrate exemplary embodiments and constitute a part of the specification. Together with the description of the specification, they serve to explain exemplary implementation of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals designate similar, but not necessarily identical, elements.

[0013] Figure 1 A schematic diagram illustrating an exemplary system in which the various methods described herein may be implemented according to an embodiment of the present disclosure;

[0014] Figure 2 A flowchart of a video quality assessment method according to an embodiment of the present disclosure is shown;

[0015] Figure 3 A schematic diagram illustrating setting judgment conditions according to an embodiment of the present disclosure is shown;

[0016] Figure 4 A schematic diagram showing a setting judgment condition according to another embodiment of the present disclosure is shown;

[0017] Figure 5 A schematic diagram showing a storage mapping relationship according to an embodiment of the present disclosure is shown;

[0018] Figure 6 A flowchart of a video quality assessment method according to another embodiment of the present disclosure is shown;

[0019] Figure 7 A schematic diagram showing a video quality assessment result according to an embodiment of the present disclosure is shown;

[0020] Figure 8 FIG2 shows a structural block diagram of a video quality assessment apparatus according to an embodiment of the present disclosure;

[0021] Figure 9 shows a structural block diagram of a video quality assessment device according to another embodiment of the present disclosure;

[0022] Figure 10 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0023] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be appreciated by those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0024] In this disclosure, unless otherwise specified, the use of terms such as "first" and "second" to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of the element, while in some cases, based on the context of the description, they may also refer to different instances.

[0025] The terms used in the descriptions of the various examples described in this disclosure are for the purpose of describing specific examples only and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element may be one or more. In addition, the term "and / or" used in this disclosure encompasses any one and all possible combinations of the listed items.

[0026] With innovations in content production, the use of AI (artificial intelligence) to automatically generate videos from images and text has become commonplace, significantly enhancing the user experience of text-based content. However, the quality of videos produced using AI often varies greatly, requiring video platforms to assess their quality and screen out substandard videos to ensure the quality of the videos they publish.

[0027] In related technologies, in order to evaluate the quality of videos and filter out unqualified videos, the time period range corresponding to each quality evaluation dimension (or indicator item) is often manually marked offline and the output report is manually calculated. Specifically, this method requires manual playback of the video to be evaluated in a browser, recording the time period occupied by the score of each indicator item, and then manually calculating the proportion of the total time period with the same score under the same indicator item to the total length of the video, determining the evaluation results based on the scores and proportions, and finally manually filling in the form. However, this method is inefficient, time-consuming and labor-intensive, and cannot control the progress of the evaluation. There is no online data result, which is not conducive to data tracing and the rapid output of evaluation results. Therefore, there is an urgent need for a method that can evaluate the quality of videos efficiently and at low cost.

[0028] In response to the above technical problems, according to one aspect of the present disclosure, a video quality assessment method is provided.

[0029] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0030] Figure 1 FIG2 is a schematic diagram of an exemplary system 100 in which the various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. Figure 1 , the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 can be configured to execute one or more applications.

[0031] In an embodiment of the present disclosure, the server 120 may run one or more services or software applications that enable the video quality assessment method to be performed.

[0032] In some embodiments, server 120 may also provide other services or software applications, which may include non-virtualized environments and virtualized environments. In some embodiments, these services may be provided as web-based services or cloud services, such as provided to users of client devices 101, 102, 103, 104, 105, and / or 106 under a software as a service (SaaS) model.

[0033] exist Figure 1In the configuration shown, the server 120 may include one or more components that implement the functions performed by the server 120. These components may include software components, hardware components, or a combination thereof that can be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 may, in turn, utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from the system 100. Therefore, Figure 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

[0034] The user may use client devices 101, 102, 103, 104, 105, and / or 106 to evaluate the video quality. The client device may provide an interface that enables the user of the client device to interact with the client device. The client device may also output information to the user via the interface. Figure 1 Only six client devices are depicted, but one skilled in the art will appreciate that the present disclosure can support any number of client devices.

[0035] Client devices 101, 102, 103, 104, 105, and / or 106 may include various types of computer devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptops), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, etc. These computer devices may run various types and versions of software applications and operating systems, such as Microsoft Windows, Apple iOS, UNIX-like operating systems, Linux, or Linux-like operating systems (such as Google Chrome OS); or include various mobile operating systems, such as Microsoft Windows Mobile OS, iOS, Windows Phone, and Android. Portable handheld devices may include cellular phones, smartphones, tablet computers, personal digital assistants (PDAs), etc. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Gaming systems may include various handheld gaming devices, internet-enabled gaming devices, etc. Client devices are capable of executing a variety of different applications, such as various internet-related applications, communication applications (such as email applications), and short message service (SMS) applications, and may use various communication protocols.

[0036] The network 110 may be any type of network known to those skilled in the art that can support data communications using any of a variety of available protocols, including but not limited to TCP / IP, SNA, IPX, etc. By way of example only, the one or more networks 110 may be a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a blockchain network, a public switched telephone network (PSTN), an infrared network, a wireless network (e.g., Bluetooth, WIFI), and / or any combination of these and / or other networks.

[0037] Server 120 may include one or more general-purpose computers, specialized server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain a server's virtual storage device). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0038] The computing units in the server 120 may run one or more operating systems including any of the operating systems described above as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and / or middle-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

[0039] In some implementations, server 120 may include one or more applications to analyze and consolidate data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105, and / or 106. Server 120 may also include one or more applications to display the data feeds and / or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and / or 106.

[0040] In some embodiments, server 120 may be a distributed system server or a server integrated with blockchain. Server 120 may also be a cloud server, or an intelligent cloud computing server or intelligent cloud host equipped with artificial intelligence technology. A cloud server is a host product within the cloud computing service system that addresses the management difficulties and poor scalability of traditional physical hosts and virtual private servers (VPS) services.

[0041] The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as audio files and video files. The databases 130 may reside in a variety of locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and communicate with the server 120 via a network-based or dedicated connection. The databases 130 may be of different types. In some embodiments, the databases used by the server 120 may be, for example, relational databases. One or more of these databases may store, update, and retrieve data to and from the databases in response to commands.

[0042] In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the applications may be different types of databases, such as a key-value store, an object store, or a conventional store backed by a file system.

[0043] Figure 1 The system 100 may be configured and operated in various ways to enable application of the various methods and apparatuses described in accordance with the present disclosure.

[0044] The video quality assessment method according to an embodiment of the present disclosure is described in detail below.

[0045] Figure 2 FIG. 2 shows a flow chart of a video quality assessment method 200 according to an embodiment of the present disclosure. Figure 2 As shown, method 200 includes steps S201 , S202 and S203 .

[0046] In step S201, quality assessment data of a video including a plurality of video segments is obtained. The quality assessment data includes a score on at least one quality assessment dimension.

[0047] In an example, the quality assessment data may be stored in a DB (Data Base) or a Remote Dictionary Server (Redis).

[0048] In the example, the quality assessment dimension can be a standard and angle for evaluating video quality, and the level of the video in various quality assessment dimensions can be directly related to the perception of the video to the user. The quality assessment dimension can be related to the quality of the video's picture or sound, for example, it can include quality assessment dimensions such as background sound, noise, freeze, picture correlation, picture jitter, and picture accuracy. The quality assessment dimension can be divided into multiple levels of quality assessment dimensions. For example, the first-level quality assessment dimension can include a quality assessment dimension related to the picture and a quality assessment dimension related to the sound, etc. Each first-level quality assessment dimension can be divided into multiple second-level quality assessment dimensions. For example, the above-mentioned background sound and noise can be second-level quality assessment dimensions under the first-level quality assessment dimension related to the sound, and the above-mentioned freeze, picture correlation, picture jitter, and picture accuracy can be second-level quality assessment dimensions under the first-level quality assessment dimension related to the picture.

[0049] In the example, a set of quality assessment dimensions can be pre-set, which may include, for example, quality assessment dimensions such as background sound, noise, freeze, picture correlation, picture jitter, and picture accuracy. When evaluating the quality of a video, the quality assessment data of the video may correspond to one or more quality assessment dimensions in the set of quality assessment dimensions. For different videos, the quality assessment may be performed from different quality assessment dimensions in the set of quality assessment dimensions. Therefore, by obtaining the quality assessment data of the video, it is possible to determine which quality assessment dimension or dimensions in the set of quality assessment dimensions have a score for the video segment, so as to determine from which quality assessment dimension or dimensions the video is quality assessed.

[0050] In the example, the score range on the quality assessment dimension can be pre-set. For example, the quality assessment dimension can be pre-set to have four score values: 0, 1, 2, and 3. These four score values ​​can represent the quality of the video segment on the quality assessment dimension. For example, 0 points can indicate that the quality of the video segment on the quality assessment dimension is very low, 1 point can indicate that the quality of the video segment on the quality assessment dimension is relatively low, 2 points can indicate that the quality of the video segment on the quality assessment dimension is relatively high, and 3 points can indicate that the quality of the video segment on the quality assessment dimension is very high. A score value of "all" can also be set for the quality assessment dimension to indicate that the quality assessment of the video involves this quality assessment dimension, but the video reviewer has not assigned a score to the video on this quality assessment dimension.

[0051] In the example, the score of the video segment on the quality assessment dimension can be a score given by a video reviewer through subjective judgment. The video reviewer can use Figure 1 The clients 101 , 102 , 103 , 104 , 105 and / or 106 shown for video review upload scores of video segments on quality assessment dimensions and other quality assessment data to the server 120 .

[0052] In step S202 , for each quality assessment dimension, a score of at least one video segment evaluated on the quality assessment dimension among the plurality of video segments is determined.

[0053] In an example, the score of a video segment on a quality assessment dimension may be related to a predetermined judgment condition. The predetermined judgment condition may indicate whether the video segment is greater than, greater than or equal to, less than, or less than or equal to a predetermined threshold under a quality assessment indicator. The quality assessment indicator may include, for example, the number of occurrences, duration, and duration ratio of quality issues.

[0054] Since the score of a video segment on a quality assessment dimension can be a subjective score determined by a video reviewer, it may not accurately indicate the quality of the video segment. However, after determining whether the video segment meets the predetermined criteria based on a standard judgment basis, it can be determined whether the score corresponding to the predetermined criteria applies to the video segment, and this score can be used as the final score for the quality assessment dimension. This final score can then more accurately indicate the quality of the video segment.

[0055] In the example, the predetermined judgment condition corresponding to the score of the video segment in the quality assessment dimension may involve multiple quality assessment indicators. In this case, the multiple quality assessment indicators may have a predetermined logical operation relationship to constitute the predetermined judgment condition.

[0056] In step S203, a quality assessment result of the video is determined based on one or more video segments having a predetermined same score in the at least one video segment.

[0057] In an example, the quality assessment result of a video may include a final score on one or more quality assessment dimensions, and the score range of the final score may also be pre-set. For example, the final score of the video on each quality assessment dimension may be pre-set to have four score values: 0, 1, 2, and 3. These four score values ​​may represent the quality of the quality assessment result of the video on that quality assessment dimension. For example, a score of 0 may indicate that the quality of the video on that quality assessment dimension is very low, a score of 1 may indicate that the quality of the video on that quality assessment dimension is relatively low, a score of 2 may indicate that the quality of the video on that quality assessment dimension is relatively high, and a score of 3 may indicate that the quality of the video on that quality assessment dimension is very high.

[0058] According to the video quality assessment method of the embodiment of the present disclosure, the quality assessment result of the video is determined by determining the respective scores of at least one video segment evaluated on each quality assessment dimension among multiple video segments based on the quality assessment data uploaded by the video reviewer. The final score of the video on each quality assessment dimension can be automatically calculated according to predetermined rules or judgment conditions. Compared with manual statistics of video quality scores, the efficiency is greatly improved, and the accuracy of data calculation is also guaranteed, making the video quality assessment result more accurate and reliable.

[0059] Various aspects of the video quality assessment method according to embodiments of the present disclosure are further described below.

[0060] According to some embodiments, each quality assessment dimension may include at least one quality assessment indicator, where the at least one quality assessment indicator includes at least one of the number of times, duration, and duration ratio. Step S203 of the video quality assessment method 200 may include: determining a quality assessment result of the video based on whether the at least one quality assessment indicator satisfies a predetermined judgment condition for one or more video segments having the same predetermined score.

[0061] Figure 3 A schematic diagram illustrating setting judgment conditions according to an embodiment of the present disclosure is shown.

[0062] like Figure 3 As shown, the quality of the video segment can be evaluated based on the quality evaluation dimension of "broadcast accuracy" to determine whether to assign "0 points" to the video. The quality evaluation dimension can also be called a labeling item.

[0063] In the example, the final score of the video on the quality assessment dimension of "broadcast accuracy" can be related to a predetermined judgment condition. Specifically, the user sets the predetermined judgment condition for the video on the quality assessment dimension of "broadcast accuracy" to include a score of "0", a type (i.e., quality assessment index) of "number of times", a condition of ">=", and a threshold of "5". Therefore, the predetermined judgment condition for the video to obtain a final score of "0" on the quality assessment dimension of "broadcast accuracy" is: the number of video segments that obtain a score of "0" on the quality assessment dimension of "broadcast accuracy" is greater than or equal to 5. In other words, it can be determined whether the video segments that are subjectively judged as "0" on the quality assessment dimension of "broadcast accuracy" by the video reviewers in the video have accumulated five or more occurrences. If so, the final score of the video on the quality assessment dimension of "broadcast accuracy" can be determined to be 0 points.

[0064] It is understandable that Figure 3This is just an example. Depending on the actual application, the "Score" column in the predetermined judgment condition content can also be checked as "1", "2", "3", or "all", the "Type" column can also be checked as "Duration" or "Duration Percentage", the "Condition" column can also be checked as ">", "<", or "<=", and the "Threshold" column can be entered with other custom values ​​to set different predetermined judgment conditions.

[0065] According to an embodiment of the present disclosure, by automatically calculating the final score of the video in each quality assessment dimension based on predetermined judgment conditions, the video quality assessment result can have higher accuracy and reliability compared to manual subjective scoring.

[0066] According to some embodiments, in response to each quality assessment dimension including at least two quality assessment indicators, a predetermined logical operation relationship may exist between the at least two quality assessment indicators to constitute a predetermined judgment condition.

[0067] Figure 4 A schematic diagram of setting judgment conditions according to another embodiment of the present disclosure is shown.

[0068] like Figure 4 As shown, the quality of the video segment can be evaluated based on the quality evaluation dimension of "picture relevance" to determine whether to assign a "0 score" to the video.

[0069] In the example, the final score of the video in the quality assessment dimension of "picture relevance" can be related to a predetermined judgment condition. Specifically, the user sets the predetermined judgment condition for the video in the quality assessment dimension of "picture relevance" to include two strategies. One of the strategies includes a score of "0", a type (i.e., quality assessment indicator) of "duration" (or duration), a condition of ">=", and a threshold of "30". The other strategy includes a score of "0", a type (i.e., quality assessment indicator) of "duration ratio", a condition of ">=", and a threshold of "0.2". The connection relationship between the two strategies (i.e., the predetermined logical operation relationship) is set to "||" (i.e., logical OR). Therefore, the predetermined judgment condition for the video to obtain a final score of "0" in the quality assessment dimension of "picture relevance" is: the cumulative duration of the video segments that obtain a score of "0" in the quality assessment dimension of "picture relevance" is greater than or equal to 30, or the cumulative duration of the video segments that obtain a score of "0" in the quality assessment dimension of "picture relevance" accounts for a proportion of the entire video duration that is greater than or equal to 0.2.

[0070] That is to say, it can be determined whether the cumulative duration of video segments that are subjectively judged as "0" by video reviewers in the quality assessment dimension of "broadcast accuracy" reaches 30 seconds or more, and whether the cumulative duration reaches 20% or more of the total duration of the entire video. If one or both of the following conditions are met: the cumulative duration of video segments that are subjectively judged as "0" by video reviewers in the quality assessment dimension of "broadcast accuracy" reaches 30 seconds or more, and the cumulative duration reaches 20% or more of the total duration of the entire video, then the final score of the video in the quality assessment dimension of "picture relevance" can be determined to be 0 points.

[0071] It is understandable that Figure 4 This is just an example. Depending on the actual application, the predetermined judgment condition may include more strategies, for example, 4 or 5. In the content of these strategies, the "Score" column can be checked as "0", "1", "2", "3" or "all", the "Type" column can be checked as "Number of times", "Duration" or "Percentage of duration", the "Condition" column can be checked as ">="", ">", "<" or "<=", the "Threshold" column can be used to enter various customized values, and the "Continuation" column can be checked as "||" (logical OR) or "&&" (logical AND) to set different predetermined judgment conditions.

[0072] In the example, the logical operation relationship checked in the "Continuation" column can indicate that the logical operation is performed on the result of the logical operation of the strategy and all previous strategies. When there is only one previous strategy, it means that the logical operation is performed on the previous strategy. When there is only one strategy under the quality assessment dimension, such as Figure 3 As shown, there is no need to predetermine the logical operation relationship.

[0073] According to an embodiment of the present disclosure, by connecting at least two quality assessment indicators with a predetermined logical operation relationship to form a predetermined judgment condition, the video can be scored in each quality assessment dimension from multiple angles at the same time, thereby further improving the reliability of the video quality assessment results.

[0074] According to some embodiments, in the quality assessment data, for each quality assessment dimension, a mapping relationship between a score on the quality assessment dimension and one or more video segments corresponding to the score may be stored. Figure 2 Step S202 of the video quality assessment method 200 may include: finding one or more video segments corresponding to the scores according to the scores on the quality assessment dimension based on the mapping relationship; and determining the scores of the one or more video segments.

[0075] In the example, since information related to the mapping relationship between scores on quality assessment dimensions and one or more video segments corresponding to the scores may be frequently read and written, such information can be cached using Redis's hash structure to support users in adding quality assessment data for video segments for multiple quality assessment dimensions at different time points.

[0076] In the hash structure, a constant + user email prefix + video ID (Identity document) can be used as the key, and the value of the time point can be used as the field. JSON characters can be used to store the start and end time points of each video segment and the duration of the video segment, and the current JSON characters can be used as the value of the hash structure.

[0077] In the example, other quality assessment data that does not need to be read and written frequently can be stored in the DB to ensure the stability of the quality assessment data.

[0078] In the example, you can first concatenate the keys of the hash structure corresponding to a certain user (for example, a video reviewer) and the video he or she evaluates, and query the values ​​of all hash structures corresponding to this key. Then you can traverse the json characters corresponding to the values ​​of different domains in the hash structure and convert the data type into an array. In this process, outliers can be eliminated. For example, outliers can be data where the start time of the video segment is less than 0, the end time of the video segment is greater than the total length of the video, or the end time of the video segment is less than the start time of the video segment. Next, a new array can be constructed for the quality assessment dimension. The new array can be used to store the mapping relationship between the score on the quality assessment dimension and one or more video segments corresponding to the score.

[0079] Figure 5 A schematic diagram of a storage mapping relationship according to an embodiment of the present disclosure is shown.

[0080] In this example, array 500 stores information at three levels, including a first level of quality assessment dimensions, a second level of score values ​​under a single quality assessment dimension, and a third level of video segments assigned the score values ​​under the single quality assessment dimension.

[0081] At the first level, array 500 may include quality assessment dimension 511, quality assessment dimension 512, quality assessment dimension 513 and multiple other quality assessment dimensions not shown. These quality assessment dimensions may be, for example, background sound, noise, broadcast accuracy, freeze, picture correlation, picture jitter, picture accuracy and other quality assessment dimensions.

[0082] Each quality assessment dimension may have multiple second-level score values. Taking quality assessment dimension 511 as an example, quality assessment dimension 511 may correspond to score value 521 (e.g., 0), score value 522 (e.g., 1), score value 523 (e.g., 2), and multiple other score values ​​not shown. In some embodiments, each quality assessment dimension may correspond to four different score values, such as 0, 1, 2, and 3.

[0083] Corresponding to each score value of each quality assessment dimension, there may be a corresponding video segment, and the information of these video segments may constitute the third level of array 500. In some embodiments, one or some score values ​​of some quality assessment dimensions may not have a corresponding video segment. Taking the score value 521 of the quality assessment dimension 511 as an example, the video segments with a score value 521 in the quality assessment dimension 511 may include video segment 531 (for example, 00:01-00:03 of the video), video segment 532 (for example, 00:05-00:10 of the video), video segment 533 (for example, 00:18-00:19 of the video), and multiple other video segments not shown.

[0084] In the example, by traversing array 500 and aggregating the data, the aggregated evaluation information of the video on a certain quality assessment dimension can be obtained, such as "[Quality Assessment Dimension 511] 00:01-00:03 (for example, it can correspond to video segment 531), 00:05-00:10 (for example, it can correspond to video segment 532), 00:18-00:19 (for example, it can correspond to video segment 533), a total of 8 seconds, accounting for 5%."

[0085] According to the embodiment of the present disclosure, by storing the quality assessment data of the video in a mapping relationship between the score on the quality assessment dimension and the video segment corresponding to the score, it is possible to facilitate the determination of the aggregated assessment information of the video on each quality assessment dimension, such as the total number of times the quality problem corresponding to the quality assessment dimension appears in the entire video, the cumulative duration, and the proportion of the cumulative duration in the entire video duration. At the same time, the quality assessment data of the video stored in this way can also facilitate video reviewers to control the progress of video quality assessment and trace quality assessment data. When it is necessary to add, modify or delete video segment information, the quality assessment data can be updated efficiently.

[0086] Figure 6 FIG. 6 is a flow chart of a video quality assessment method 600 according to another embodiment of the present disclosure. Figure 6As shown, the video quality assessment method 600 may include steps S601, S602, S603, S604, S605, S606, S607, S608 and S609. The method 600 may be executed for each quality assessment dimension to determine a quality assessment result of the video in the quality assessment dimension.

[0087] First, in step S601 , it may be determined whether the current quality assessment dimension is applied to the quality assessment of the current video.

[0088] In an example, a set of quality assessment dimensions may be pre-set, such as Figure 5 The first level of the array 500 is shown. During video quality assessment, a set of quality assessment dimensions can be traversed to determine which quality assessment dimension(s) in the set have scores for the video segment, thereby determining the quality assessment dimensions or dimensions from which the video is assessed.

[0089] If the current quality assessment dimension does not have a score for the video segment, that is, it has not been applied to the quality assessment of the current video, then the assessment result judgment of the quality assessment dimension can be directly ended.

[0090] If the current quality assessment dimension has a score for the video segment, that is, it is applied to the quality assessment of the current video, then step S602 can be continued to sort the score values ​​under the quality assessment dimension from low to high to facilitate subsequent data traversal.

[0091] In this example, since the predetermined criteria corresponding to a lower score value are often a subset of the predetermined criteria corresponding to a higher score value, when both the predetermined criteria corresponding to the lower score value and the predetermined criteria corresponding to the higher score value are met, the lower score value can be given greater weight, and the final score can even be determined as the lower score value. Therefore, by sorting the scores under this quality assessment dimension from low to high and then traversing the data, the video assessment results can be determined more efficiently.

[0092] In this example, all score values ​​under the quality assessment dimension may correspond to predetermined judgment conditions including one or more strategies, or only some score values ​​may correspond to predetermined judgment conditions including one or more strategies. Each quality assessment dimension may include one or more quality assessment indicators, so each predetermined judgment condition of these quality assessment dimensions may also include one or more strategies. These strategies may be, for example, Figure 3 (the predetermined judgment condition corresponding to the quality assessment dimension includes a strategy) or Figure 4 As shown in FIG5 (the predetermined judgment condition corresponding to the quality assessment dimension includes two strategies), the content of each strategy may include a score, a type (ie, a quality assessment indicator), a condition, and a threshold.

[0093] For each strategy corresponding to each strategy score value under the quality assessment dimension, step S605 can be executed. In step S605, it can first be determined whether the type (i.e., quality assessment indicator) in the content of the strategy is the number of times, duration, or duration ratio. By traversing Figure 5 The array 500 shown aggregates the data to obtain aggregated evaluation information of the video on the quality assessment dimension. The aggregated evaluation information may include the total number of times the quality problem corresponding to the quality assessment dimension appears in the entire video, the cumulative duration, and the proportion of the cumulative duration in the entire video duration. In some embodiments, when the quality assessment of the video involves the quality assessment dimension, but the video reviewer does not assign a score to the video on the quality assessment dimension, the quality assessment dimension can be configured as a score value of "all". In this case, the number of video segments can be directly accumulated to obtain the total number of times, or the duration of the video segments can be accumulated to obtain the cumulative duration, and the proportion of the cumulative duration in the entire video duration can be further obtained without traversing such as Figure 5 The array 500 shown aggregates the data. Based on the aggregated evaluation information on the quality evaluation dimension and the predetermined judgment conditions of the policy, the evaluation result of the policy can be determined. If the aggregated evaluation information on the quality evaluation dimension meets the predetermined judgment conditions of the policy, a Boolean value "true" can be output.

[0094] In step S606, it can be determined whether the score value corresponds to multiple strategies. If the score value corresponds to multiple strategies, then step S607 can be executed, and the result of this round (i.e., the Boolean value output by the current strategy) and the result of the previous round (i.e., the Boolean value obtained after the logical operation of all previous strategies) can be logically operated to update the output Boolean value. When this strategy is the first strategy corresponding to the score value, step S607 can be skipped. The logical operation relationship can also be pre-set in the strategy. If the logical operation relationship is set to "||", then when at least one of the Boolean value output by this strategy and the Boolean value obtained after the logical operation of all previous strategies is "true", the Boolean value "true" can be output. If the logical operation relationship is set to "&&", then when the Boolean value output by this strategy and the Boolean value obtained after the logical operation of all previous strategies are both "true", the Boolean value "true" can be output.

[0095] Next, in step S604, a determination is made as to whether all policies corresponding to the score value have been traversed. If not, steps S605 through S607 are continued until all policies corresponding to the score value have been traversed. Once all policies corresponding to the score value have been traversed, step S608 is executed to determine whether the current score value matches. If the score value corresponds to only one policy, this means that all policies corresponding to the score value have been traversed in step S606, and the process can then jump to step S608.

[0096] In step S608, if the Boolean value obtained at this time is "true", it can be said that it can match the current score value, and the score value is the final quality assessment result on the quality assessment dimension, and the assessment result judgment of the quality assessment dimension is completed.

[0097] If the Boolean value obtained at this time is not "true", it may indicate that the current score value has not been matched. In this case, step S603 may be used to determine whether all policies corresponding to all score values ​​have been traversed. If there are other score values ​​corresponding to policies under this quality assessment dimension, steps S604, S605, S607, and S608 may be performed for the next score value corresponding to a policy. If there are no other score values ​​corresponding to policies under this quality assessment dimension, step S609 may be performed to determine that the score is "null", indicating that this quality assessment dimension has no final score. At this point, the evaluation result judgment for this quality assessment dimension ends.

[0098] According to some embodiments, Figure 2 The video quality assessment method 200 shown or Figure 6 The video quality assessment method 600 may further include: obtaining updated quality assessment data; and re-determining a quality assessment result of the video based on the updated quality assessment data.

[0099] In this example, the updated quality assessment data may be the same as or different from the original quality assessment data. The updated quality assessment data may add new video segment data to the original quality assessment data, modify data related to one or more video segments in one or more quality assessment dimensions in the original quality assessment data, or delete data related to one or more video segments in one or more quality assessment dimensions in the original quality assessment data.

[0100] In this example, if the video reviewer believes that the original quality assessment data is missing, they can supplement and report the newly added quality assessment data. In this case, the value of the new time point can be used as the field of the hash structure, and the new video segment can be added as the value of the hash structure. Then, based on the updated quality assessment data, the video quality assessment result can be re-determined using the video quality assessment method of the above embodiment.

[0101] In this example, if the video reviewer believes that the original quality assessment data contains errors, they can logically delete the corresponding portion of the quality assessment data. In this case, the value of the time point corresponding to the video segment to be deleted can be used as the field of the hash structure to search and delete the corresponding hash structure value. Then, based on the updated quality assessment data, the video quality assessment result can be re-determined using the video quality assessment method of the above embodiment.

[0102] According to an embodiment of the present disclosure, by re-determining the quality evaluation result of the video when the quality evaluation data is updated, the quality evaluation result of the video can be corrected in time to ensure the accuracy of the quality evaluation result of the video.

[0103] According to some embodiments, Figure 2 The video quality assessment method 200 shown or Figure 6 The illustrated video quality assessment method 600 may further include: receiving a request to display a quality assessment result of the video; and displaying the quality assessment result of the video.

[0104] Figure 7 A schematic diagram showing a video quality assessment result according to an embodiment of the present disclosure is shown.

[0105] See also Figure 7 The display interface of the video quality evaluation result includes a video browsing frame, a current video information frame, a current video standard information frame and a video quality evaluation result display frame. Figure 7 The video quality assessment results shown in the figure show that the video shown on the left is evaluated for multiple picture-related quality assessment dimensions such as watermark / advertising issues, picture truncation, mosaics, and picture correlation, and the final scores of the video on these quality assessment dimensions are shown.

[0106] According to the embodiment of the present disclosure, by displaying the quality evaluation result of the video, the user can intuitively obtain the quality evaluation result of the video.

[0107] According to another aspect of the present disclosure, a network configuration device is also provided.

[0108] Figure 8 FIG. 8 is a structural block diagram of a video quality assessment apparatus 800 according to an embodiment of the present disclosure.

[0109] like Figure 8 As shown, the video quality assessment apparatus 800 includes: a data acquisition module 810 configured to acquire quality assessment data for a video comprising multiple video segments. The quality assessment data includes a score on at least one quality assessment dimension; a score determination module 820 configured to determine, for each quality assessment dimension, a score for at least one video segment evaluated on the quality assessment dimension from among the multiple video segments; and a result determination module 830 configured to determine a quality assessment result for the video based on one or more video segments from among the at least one video segment having a predetermined identical score.

[0110] Since the data acquisition module 810, the score determination module 820, and the result determination module 830 in the video quality assessment apparatus 800 may correspond to the following Figure 2 The steps S201 to S203 are described, so the details of each aspect are not repeated here.

[0111] In addition, the video quality assessment apparatus 800 and the modules included therein may further include submodules, which will be described in conjunction with the following. Figure 9 Provide detailed explanation.

[0112] According to an embodiment of the present disclosure, the quality assessment result of the video is determined by determining the respective scores of at least one video segment evaluated on each quality assessment dimension among multiple video segments based on the quality assessment data uploaded by the video reviewer. The final score of the video on each quality assessment dimension can be automatically calculated according to predetermined rules or judgment conditions. Compared with manual statistics of video quality scores, the efficiency is greatly improved, and the accuracy of data calculation is also guaranteed, making the video quality assessment result more accurate and reliable.

[0113] Figure 9 FIG. 4 shows a structural block diagram of a video quality assessment apparatus 900 according to another embodiment of the present disclosure.

[0114] like Figure 9 As shown, the video quality assessment apparatus 900 may include a data acquisition module 910, a score determination module 920, and a result determination module 930. The data acquisition module 910, the score determination module 920, and the result determination module 930 may be used together with the following examples: Figure 8 The data acquisition module 810, score determination module 820, and result determination module 830 shown correspond to each other, so their details are not repeated here.

[0115] In an example, each quality assessment dimension may include at least one quality assessment indicator, where the at least one quality assessment indicator includes at least one of the number of times, duration, and duration ratio. Result determination module 930 may include a condition judgment module 931 configured to determine a quality assessment result for the video based on whether the at least one quality assessment indicator satisfies a predetermined judgment condition for one or more video segments having the same predetermined score.

[0116] Therefore, by automatically calculating the final score of the video in each quality assessment dimension based on predetermined judgment conditions, the video quality assessment result can have higher accuracy and reliability compared with manual subjective scoring.

[0117] In an example, in response to each quality assessment dimension including at least two quality assessment indicators, a predetermined logical operation relationship may exist between the at least two quality assessment indicators to constitute a predetermined judgment condition.

[0118] Therefore, by connecting at least two quality assessment indicators with a predetermined logical operation relationship to form a predetermined judgment condition, the video can be scored in each quality assessment dimension from multiple angles at the same time, thereby further improving the reliability of the video quality assessment result.

[0119] In an example, for each quality assessment dimension, a mapping relationship between a score on the quality assessment dimension and one or more video segments corresponding to the score may be stored in the quality assessment data. Score determination module 920 may include: a video segment search module 921 configured to find one or more video segments corresponding to the score based on the score on the quality assessment dimension based on the mapping relationship; and a video segment scoring module 922 configured to determine the scores of the one or more video segments.

[0120] Therefore, by storing the quality assessment data of the video in a mapping relationship between the scores on the quality assessment dimensions and the video segments corresponding to the scores, it is possible to easily determine the aggregated assessment information of the video on each quality assessment dimension, such as the total number of times the quality problem corresponding to the quality assessment dimension appears in the entire video, the cumulative duration, and the proportion of the cumulative duration in the entire video duration. At the same time, the quality assessment data of the video stored in this way can also make it easier for video reviewers to control the progress of the video quality assessment and trace the quality assessment data. When it is necessary to add, modify or delete video segment information, the quality assessment data can be updated efficiently.

[0121] In an example, the apparatus 900 may further include: a data updating module 940 configured to obtain updated quality assessment data; and a result updating module 950 configured to re-determine a quality assessment result of the video based on the updated quality assessment data.

[0122] Therefore, by re-determining the quality evaluation result of the video when the quality evaluation data is updated, the quality evaluation result of the video can be corrected in time to ensure the accuracy of the quality evaluation result of the video.

[0123] In an example, the apparatus 900 may further include: a request receiving module 960 configured to receive a request for displaying a quality assessment result of a video; and a result display module 970 configured to display the quality assessment result of the video.

[0124] Therefore, by displaying the quality evaluation result of the video, the user can intuitively obtain the quality evaluation result of the video.

[0125] According to an embodiment of the present disclosure, an electronic device, a readable storage medium, and a computer program product are also provided.

[0126] refer to Figure 10 , a block diagram of an electronic device 1000 that can serve as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0127] like Figure 10 As shown, the device 1000 includes a computing unit 1001, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a random access memory (RAM) 1003. Various programs and data required for the operation of the device 1000 can also be stored in the RAM 1003. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0128] Multiple components in the device 1000 are connected to the I / O interface 1005, including: an input unit 1006, an output unit 1007, a storage unit 1008, and a communication unit 1009. The input unit 1006 can be any type of device that can input information to the device 1000. The input unit 1006 can receive input digital or character information and generate key signal input related to user settings and / or function control of the electronic device, and can include but is not limited to a mouse, a keyboard, a touch screen, a trackpad, a trackball, a joystick, a microphone, and / or a remote control. The output unit 1007 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 1008 can include but is not limited to a magnetic disk and an optical disk. The communication unit 1009 allows the device 1000 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver and / or a chipset, such as a Bluetooth device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0129] The computing unit 1001 can be various general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 1001 performs the various methods and processes described above, such as the video quality assessment method. For example, in some embodiments, the video quality assessment method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 1008. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 1000 via the ROM 1002 and / or the communication unit 1009. When the computer program is loaded into the RAM 1003 and executed by the computing unit 1001, one or more steps of the video quality assessment method described above can be performed. Alternatively, in other embodiments, the computing unit 1001 may be configured to execute the video quality assessment method in any other appropriate manner (for example, by means of firmware).

[0130] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0131] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0132] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0133] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0134] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), the Internet, and a blockchain network.

[0135] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0136] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0137] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but is only limited by the claims after authorization and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. In addition, the steps may be performed in an order different from that described in this disclosure. Further, the various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear after this disclosure.

Claims

1. A video quality assessment method, comprising: Obtaining quality assessment data of a video, the video comprising a plurality of video segments, wherein the quality assessment data comprises a mapping relationship between: at least one quality assessment dimension, at least one score associated with each quality assessment dimension, and one or more video segments associated with each score among the plurality of video segments; Based on the mapping relationship, finding one or more corresponding target video segments according to the specified quality assessment dimension and the specified score; and For the one or more target video segments, based on whether at least one quality assessment indicator corresponding to the specified quality assessment dimension meets a predetermined judgment condition, determine the quality assessment result of the video under the specified quality assessment dimension, wherein the at least one quality assessment indicator includes at least one of the cumulative number of times, the cumulative duration and the proportion of the cumulative duration.

2. The method according to claim 1, wherein In response to each quality assessment dimension including at least two quality assessment indicators, there is a predetermined logical operation relationship between the at least two quality assessment indicators to constitute the predetermined judgment condition.

3. The method according to claim 1 or 2, further comprising: Obtain updated quality assessment data; as well as The quality assessment result of the video is re-determined based on the updated quality assessment data.

4. The method according to claim 1 or 2, further comprising: receiving a request for displaying a quality assessment result of the video; as well as The quality assessment result of the video is displayed.

5. A video quality assessment device, comprising: a data acquisition module configured to acquire quality assessment data of a video, the video comprising a plurality of video segments, wherein the quality assessment data comprises a mapping relationship between: at least one quality assessment dimension, at least one score associated with each quality assessment dimension, and one or more video segments associated with each score in the plurality of video segments; a score determination module configured to find one or more corresponding target video segments based on the mapping relationship and the specified quality assessment dimension and the specified score; and The result determination module is configured to determine, for the one or more target video segments, a quality assessment result of the video under the specified quality assessment dimension based on whether at least one quality assessment indicator corresponding to the specified quality assessment dimension meets a predetermined judgment condition, wherein the at least one quality assessment indicator includes at least one of the cumulative number of times, the cumulative duration, and the proportion of the cumulative duration.

6. The device according to claim 5, wherein In response to each quality assessment dimension including at least two quality assessment indicators, there is a predetermined logical operation relationship between the at least two quality assessment indicators to constitute the predetermined judgment condition.

7. The apparatus according to claim 5 or 6, further comprising: a data updating module configured to obtain updated quality assessment data; as well as The result updating module is configured to re-determine the quality evaluation result of the video based on the updated quality evaluation data.

8. The apparatus according to claim 5 or 6, further comprising: a request receiving module, configured to receive a request for displaying a quality assessment result of the video; as well as The result display module is configured to display the quality evaluation result of the video.

9. A computer device comprising: at least one processor; as well as a memory having a computer program stored thereon, When the computer program is executed by the processor, the processor is caused to perform the method according to any one of claims 1 to 4.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 4.

11. A computer program product comprising a computer program, wherein when the computer program is executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 4.

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