CN112416987A - Experience quality determination method and device based on user portrait and electroencephalogram data - Google Patents
Experience quality determination method and device based on user portrait and electroencephalogram data Download PDFInfo
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Abstract
The invention provides a method and a device for determining experience quality based on user portrait and electroencephalogram data, which relate to the technical field of video quality evaluation and comprise the following steps: firstly, acquiring video quality parameters of a target video service and a user portrait of a target user; then mapping the user portrait based on a user portrait index table to obtain corresponding index parameters; searching corresponding electroencephalogram data from an electroencephalogram database based on the video quality parameters and the index parameters; finally, forecasting is carried out based on the electroencephalogram data to obtain a target experience quality evaluation result; and the target experience quality evaluation result is used for reflecting the evaluation of the target user on the target video service. The index parameters in the invention can reflect the specific characteristics and requirements of the target user, and accurate electroencephalogram data can be provided according to the index parameters, so that more accurate personalized and scenic evaluation is provided, and the authenticity and reliability of the target experience quality evaluation result are ensured.
Description
Technical Field
The invention relates to the technical field of video quality evaluation, in particular to a method and a device for determining experience quality based on user portrait and electroencephalogram data.
Background
The traditional video quality evaluation belongs to an objective evaluation method, such as MSE (Mean Squared Error), PSNE (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index), and the like. These objective video quality evaluation methods are based on a single picture and depend on the original code stream. There is a drawback in subjective consistency with this approach. In order to solve the defects of the traditional objective evaluation method, subjective experience evaluation standards including five aspects of picture definition, quality stability, video fluency, video content and observation conditions are presented, and quality evaluation is carried out by methods such as user grading and the like. However, the dominant Experience Quality evaluation method has the problems that the scoring dynamics are affected by human body bias and the like due to numerous terminal client groups, so that the subjective Experience Quality evaluation result with highly consistent rule is difficult to obtain, and the upper-layer application analysis is inconvenient to be performed based on the Quality of Experience (QOE) result.
In order to solve the problem, a recessive experience quality evaluation method represented by an EEG (Electroencephalogram) appears, subjective experience quality evaluation is performed through response of a brain to radio waves of external stimuli, and the EEG quality evaluation method has universality and is easy to obtain subjective experience quality evaluation results with consistent rules because of being based on a physiological system of a human body. However, with the refinement of network services, more precise personalized and scenic evaluation needs to be provided according to the specific characteristics and requirements of users. However, traditional EEG subjective quality of experience assessment does not solve this problem.
Disclosure of Invention
The invention aims to provide a user portrait and electroencephalogram data-based experience quality determination method and device, and aims to solve the technical problem that the traditional EEG subjective experience quality assessment in the prior art cannot provide more accurate personalized and scene assessment.
In a first aspect, the present invention provides a method for determining experience quality based on a user portrait and electroencephalogram data, including: acquiring video quality parameters of a target video service and a user portrait of a target user; mapping the user portrait based on a user portrait index table to obtain corresponding index parameters; searching corresponding electroencephalogram data from an electroencephalogram database based on the video quality parameters and the index parameters; predicting based on the electroencephalogram data to obtain a target experience quality evaluation result; and the target experience quality evaluation result is used for reflecting the evaluation of the target user on the target video service.
Further, predicting based on the electroencephalogram data to obtain a target experience quality evaluation result, and the method comprises the following steps: and predicting the target experience quality evaluation result by utilizing a target prediction mode based on the electroencephalogram data.
Further, the target prediction mode comprises a model prediction mode, and the step of predicting the target experience quality evaluation result by using the target prediction mode based on the electroencephalogram data comprises the following steps: and inputting the electroencephalogram data into a preset deep neural network model, and predicting the target experience quality evaluation result by using the model prediction mode.
Further, the target prediction mode includes a weighted prediction mode, and the step of predicting the target experience quality evaluation result by using the target prediction mode based on the electroencephalogram data includes: and carrying out weighting processing on the electroencephalogram data, and predicting the target experience quality evaluation result by using the weighted prediction mode.
Further, before searching corresponding electroencephalogram data from an electroencephalogram database based on the video quality parameter and the index parameter, the method further comprises: acquiring a database building sample, and building an electroencephalogram database based on the database building sample; wherein the library building sample comprises: video quality parameter samples, index parameter samples, and electroencephalogram data samples.
Further, the index parameter includes a scene index table and/or a crowd index table, wherein the scene index table includes at least one of the following parameters: the method comprises the following steps of (1) screen size, screen brightness, terminal rating, an operating system, network conditions, viewing modes and environment classification; the crowd index table includes at least one of the following parameters: preference categories, content preference categories, color preferences, viewing habits, viewing time, viewing period.
Further, the method further comprises: and performing service pushing based on the user portrait so as to push interested video services to the target user.
In a second aspect, the present invention provides an experience quality determination apparatus based on a user portrait and electroencephalogram data, including: the system comprises an acquisition unit, a processing unit and a display unit, wherein the acquisition unit is used for acquiring video quality parameters of a target video service and a user portrait of a target user; the mapping unit is used for mapping the user portrait based on a user portrait index table to obtain a corresponding index parameter; the searching unit is used for searching corresponding electroencephalogram data from an electroencephalogram database based on the video quality parameters and the index parameters; the prediction unit is used for predicting based on the electroencephalogram data to obtain a target experience quality evaluation result; and the target experience quality evaluation result is used for reflecting the evaluation of the target user on the target video service.
In a third aspect, the present invention further provides an electronic device, including a memory and a processor, where the memory stores a computer program operable on the processor, and the processor executes the steps of the method for determining quality of experience based on a user portrait and electroencephalogram data, which is implemented when the processor executes the computer program.
In a fourth aspect, the present invention also provides a computer readable medium having non-volatile program code executable by a processor, wherein the program code causes the processor to perform the method for quality of experience determination based on user portrait and electroencephalogram data.
The invention provides a method and a device for determining experience quality based on user portrait and electroencephalogram data, wherein the method comprises the following steps: firstly, acquiring video quality parameters of a target video service and a user portrait of a target user; then mapping the user portrait based on a user portrait index table to obtain corresponding index parameters; searching corresponding electroencephalogram data from an electroencephalogram database based on the video quality parameters and the index parameters; finally, forecasting is carried out based on the electroencephalogram data to obtain a target experience quality evaluation result; and the target experience quality evaluation result is used for reflecting the evaluation of the target user on the target video service. The index parameters in the invention can reflect the specific characteristics and requirements of the target user, and accurate electroencephalogram data can be provided according to the index parameters, so that more accurate personalized and scenic evaluation is provided, and the authenticity and reliability of the target experience quality evaluation result are ensured.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart of a method for determining quality of experience based on user portrait and electroencephalogram data according to an embodiment of the present invention;
FIG. 2 is a flowchart of another experience quality determination method based on user portrait and electroencephalogram data according to an embodiment of the present invention;
FIG. 3 is a flowchart of another experience quality determination method based on user portrait and electroencephalogram data according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an experience quality determination apparatus based on a user portrait and electroencephalogram data according to an embodiment of the present invention.
Icon:
11-an acquisition unit; 12-a mapping unit; 13-a lookup unit; 14-prediction unit.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
With the development of communication network technology and the abundance of people's demand for information delivery, video-based services are rapidly developing, on-demand, live, video telephony, and VR/AR. In order to provide users with better quality of service, the service providers increasingly attach importance to the customer experience, thereby deriving and promoting the rapid development of quality of experience concepts and technologies. The experience quality refers to the subjective experience of the terminal user on the service and the network, is the comprehensive psychological experience established by the terminal user in the service using process, and relates to all aspects in the interaction process of people, the network, the service and the like. The experience quality can reflect the relation between the quality of the current service and network and the user experience, integrates all the influencing factors of a service level, a user level and a network level, and directly reflects the approval degree of a terminal user to the network service.
The traditional video quality evaluation belongs to an objective evaluation method, such as MSE, PSNE, SSIM and the like. These objective video quality evaluation methods are based on a single picture and depend on the original code stream. There is a drawback in subjective consistency with this approach. In order to solve the defects of the traditional objective evaluation method, subjective experience evaluation standards including five aspects of picture definition, quality stability, video fluency, video content and observation conditions are presented, and quality evaluation is carried out by methods such as user grading and the like. However, the dominant experience quality evaluation method has the problems that the scoring dynamics can be influenced by human body bias and the like due to numerous terminal client groups, so that the subjective experience quality evaluation result with highly consistent rule is difficult to obtain, and the method is inconvenient for upper-layer application analysis based on the QOE result.
In order to solve the problem, a recessive experience quality evaluation method represented by an EEG (electroencephalogram) is provided, subjective experience quality evaluation is performed through the response of a brain to radio waves of external stimuli, and the EEG quality evaluation method is based on a physiological system of a human body, has universality and is easy to obtain subjective experience quality evaluation results with consistent rules. However, with the refinement of network services, more precise personalized and scenic evaluation needs to be provided according to the specific characteristics and requirements of users. However, traditional EEG subjective quality of experience assessment does not solve this problem. Based on the above, the embodiment of the invention provides the experience quality determination method and device based on the user portrait and the electroencephalogram data, the specific characteristics and requirements of the target user are reflected through the index parameters, the accurate electroencephalogram data can be provided, further more accurate personalized and scene evaluation is provided, and the authenticity and reliability of the target experience quality evaluation result are ensured.
In order to facilitate understanding of the embodiment, a method for determining experience quality based on user portrait and electroencephalogram data disclosed by the embodiment of the present invention is first described in detail.
Example 1:
in accordance with an embodiment of the present invention, there is provided an embodiment of a method for quality of experience determination based on user portrait and electroencephalogram data, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different than here.
Fig. 1 is a flowchart of a method for determining experience quality based on user portrait and electroencephalogram data according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S101, obtaining video quality parameters of the target video service and a user portrait of the target user.
In embodiments of the present invention, the target video service includes, but is not limited to, short video, television program, live broadcast, and the like. Target users may refer to video users with a common commonality, such as: a class of people between the ages of 30-40, a class of people interested in motion pictures. The manner in which the video quality parameters of the target video service are obtained and the manner in which the user representation of the target user is obtained may be two completely different manners. The video quality parameter of the target video service can be used as a factor for searching electroencephalogram data, and under a certain condition, the change of the video quality parameter has little influence on the electroencephalogram data. The video quality parameter may be selected from a video quality parameter index table as shown in table 1, for example: katton, content speed, initial buffering, picture synchronization, picture quality, and noise. Generally, the video quality parameters can be classified according to electroencephalogram test results (electroencephalogram data described below) in application, and the classification is usually 5.
Table 1 video quality parameter index table
| Parameter(s) | Explanation of the invention |
| Catton | Fluency of content |
| Content speed | Video frame rate |
| Initial buffering | / |
| Synchronization of sound and picture | Audio/video synchronization effects |
| Picture quality | Video resolution |
| Noise(s) | / |
And step S102, mapping the user portrait based on the user portrait index table to obtain corresponding index parameters.
The user profile is a user attribute classification established according to the operation habits of extractable target users. The mapping index parameters are different for different user portraits, so that the index parameters can reflect certain parameter information of the target user. The index parameters comprise a scene index table and/or a crowd index table, and the scene index table mainly considers the application scene of a target user and determines the external environment where the user is located when receiving the service. As shown in table 2, the scene index table includes, but is not limited to, the following parameters: screen size, screen brightness, terminal rating, operating system, network status, viewing mode, environment classification.
Table 2 scene index table
The crowd index table mainly considers the biology and the sociality of target users, and can realize the classification of users receiving services, and target users with different classifications (namely categories) are reflected in the fine difference of the biology and the difference caused by social accumulation. As shown in fig. 3, the crowd index table includes, but is not limited to, the following parameters: preference categories, content preference categories, color preferences, viewing habits, viewing time, viewing period.
TABLE 3 crowd index Table
| Parameter(s) | Explanation of the invention |
| Preference classification | Sports/movie play/cartoon/game/comprehensive art/live broadcast |
| Content preference classification | Action/scenario/nature/recording |
| Color preference | Color temperature/contrast ratio |
| Viewing habits | Skip/finish broadcast/search |
| Viewing time | 9:00-18:00/18:00-22:00/22:00-2:00/2:00/9:00 |
| Viewing period | Daily/weekend/occasionally |
And S103, searching corresponding electroencephalogram data from the electroencephalogram database based on the video quality parameters and the index parameters.
In the embodiment of the invention, the electroencephalogram database can be referred to as an individualized electroencephalogram database, and the individualized electroencephalogram database can also be referred to as an individualized electroencephalogram standard database. The personalized electroencephalogram standard database is a multi-index system and is a multi-dimensional electroencephalogram database established through different influence parameters such as the external environment of a target user for receiving a target video service and the self quality of a video. The personalized video experience is classified according to factors influencing the personalized video experience, and the personalized video experience can be divided into the following steps: a scene index table, a crowd index table, and a video quality parameter index table. In practical application, the personalized electroencephalogram database can be connected with other databases to jointly evaluate the target video service. Or the personalized electroencephalogram database can also be modified in response according to requirements so as to meet the demand of experience quality evaluation in real time.
The traditional electroencephalogram quality evaluation database can only provide electroencephalogram response data sets based on service quality, such as: the video service performs electroencephalogram experiments, acquisition and processing based on parameters influencing video quality, and obtains universal electroencephalogram response data for storage and index use. On the basis, the obtained user portrait can be effectively connected with the personalized electroencephalogram database after being mapped into the index parameters through big data analysis. Therefore, the personalized electroencephalogram database in the embodiment of the invention provides feasibility for an experience quality evaluation (namely evaluation) method based on user portrait and electroencephalogram data.
And step S104, predicting based on the electroencephalogram data to obtain a target experience quality evaluation result. And the target experience quality evaluation result is used for reflecting the evaluation of the target user on the target video service.
As shown in fig. 3, in the experience quality determination method based on the user portrait and the electroencephalogram data provided by the embodiment of the present invention, the method connects the video quality parameter acquired from the target video service as a factor for searching the electroencephalogram data to the personalized electroencephalogram database; the user portrait of the target user can be mapped in a maximized manner according to the user portrait index table through a user portrait index table mapping module (namely a virtual module where the user portrait index table is located), and an index parameter obtained after mapping is used as another factor for searching electroencephalogram data and is connected to a personalized electroencephalogram database; and performing electroencephalogram data indexing according to the video quality parameter and the indexing parameter of the current video (namely, the target video service), and outputting electroencephalogram data capable of representing the user portrait. And (4) searching and finding out corresponding electroencephalogram data in the personalized electroencephalogram database, sending the electroencephalogram data into a predictor (namely a structure for realizing a target prediction mode), and predicting the final video service quality (namely the target experience quality evaluation result). In the predictor, there may be different implementations, and the weights may be obtained and weighted based on experience or big data test, or the classified prediction may be performed based on the deep neural network model described below.
The index parameters in the embodiment of the invention can reflect the specific characteristics and requirements of the target user, and accurate electroencephalogram data can be provided according to the index parameters, so that more accurate personalized and scenic evaluation is provided, and the authenticity and reliability of the target experience quality evaluation result are ensured.
In an alternative embodiment, as shown in fig. 2, in step S104, performing prediction based on electroencephalogram data to obtain a target quality of experience evaluation result, including: and S105, predicting a target experience quality evaluation result by using a target prediction mode based on the electroencephalogram data.
The target prediction mode comprises a model prediction mode and a weighted prediction mode, when the target prediction mode is the model prediction mode, the step S105, based on the electroencephalogram data, of predicting the target experience quality evaluation result by using the target prediction mode comprises the following steps: and inputting the electroencephalogram data into a preset deep neural network model, and predicting a target experience quality evaluation result by using a model prediction mode. When the target prediction mode is a weighted prediction mode, step S105, based on the electroencephalogram data, predicting a target experience quality evaluation result by using the target prediction mode, including: and performing weighting processing on the electroencephalogram data, and predicting a target experience quality evaluation result by using a weighting prediction mode. The multiple target prediction modes provide reliability for predicting the target experience quality evaluation result.
In an optional embodiment, before searching the corresponding electroencephalogram data from the electroencephalogram database based on the video quality parameter and the index parameter, the method further comprises: acquiring a database building sample, and building an electroencephalogram database based on the database building sample; wherein, the database establishing sample comprises: video quality parameter samples, index parameter samples, and electroencephalogram data samples. Besides the video quality parameter samples and the index parameter samples, other index tables can be added into the establishment process of the personalized electroencephalogram database to play a role of the personalized electroencephalogram database.
In an optional embodiment, the method further comprises: and performing service pushing based on the user portrait so as to push interested video services to the target user. The embodiment of the invention can reduce the service cost, enhance the service efficiency and improve the attention and the viscosity of the user by pushing the service based on the user image.
The invention mainly comprises the following three parts: 1. establishing an individualized electroencephalogram database; 2. indexing the user portrait based on the user portrait index table; 3. the obtained target experience quality evaluation result is the result of the personalized experience quality evaluation.
The methods described in the embodiments of the present invention can be implemented in various forms of software, hardware, or a combination of software and hardware. Therefore, the embodiment of the invention can construct an individualized experience quality evaluation system by combining the individualized electroencephalogram database and the user portrait index table, and the system comprises the following steps: the acquisition terminal and the acquisition terminal are used for acquiring video quality parameters, can exist in a hardware/SDK/APP mode, complete data acquisition and transmit the data to an individual electroencephalogram database and an individual electroencephalogram database of an APP/cloud, a user portrait, index parameters and a target experience quality evaluation result output by a predictor can be stored in the cloud, and data transmission and function control of all APP/cloud storage can be achieved through a user UI and a user interface. The user UI may exist in the form of hardware/SDK/APP, among others.
In order to solve the problem of the traditional EEG subjective experience quality evaluation method, the embodiment of the invention provides an EEG2.0 experience quality evaluation method based on user portrait, a personalized electroencephalogram standard database is established in advance, a user portrait constructed based on certain service software is applied, electroencephalogram data matched with the user portrait is extracted for experience quality evaluation, quality evaluation results are provided according to specific characteristics and requirements of users, corresponding services are provided, and the balance of service cost and user experience quality is improved.
Example 2:
the embodiment of the invention provides a user portrait and electroencephalogram data-based experience quality determination device, which is mainly used for executing the user portrait and electroencephalogram data-based experience quality determination method provided by the embodiment 1.
Fig. 4 is a schematic structural diagram of an experience quality determination apparatus based on a user portrait and electroencephalogram data according to an embodiment of the present invention. As shown in fig. 4, the experience quality determination apparatus based on user portrait and electroencephalogram data mainly includes: an obtaining unit 11, a mapping unit 12, a lookup unit 13 and a prediction unit 14, wherein:
an obtaining unit 11, configured to obtain a video quality parameter of a target video service and a user profile of a target user;
the mapping unit 12 is configured to map the user portrait based on the user portrait index table to obtain a corresponding index parameter;
the searching unit 13 is used for searching corresponding electroencephalogram data from the electroencephalogram database based on the video quality parameters and the index parameters;
the prediction unit 14 is used for predicting based on the electroencephalogram data to obtain a target experience quality evaluation result; and the target experience quality evaluation result is used for reflecting the evaluation of the target user on the target video service.
The experience quality determining device based on the user portrait and the electroencephalogram data, provided by the embodiment of the invention, comprises the steps of firstly, acquiring a video quality parameter of a target video service and the user portrait of a target user by using an acquisition unit 11; then, mapping the user portrait by using a mapping unit 12 based on a user portrait index table to obtain corresponding index parameters; then, the searching unit 13 searches corresponding electroencephalogram data from an electroencephalogram database based on the video quality parameter and the index parameter; finally, a prediction unit 14 is used for predicting based on the electroencephalogram data to obtain a target experience quality evaluation result; and the target experience quality evaluation result is used for reflecting the evaluation of the target user on the target video service. The index parameters in the embodiment of the invention can reflect the specific characteristics and requirements of the target user, and accurate electroencephalogram data can be provided according to the index parameters, so that more accurate personalized and scenic evaluation is provided, and the authenticity and reliability of the target experience quality evaluation result are ensured.
Optionally, the prediction unit is further configured to: and predicting a target experience quality evaluation result by using a target prediction mode based on the electroencephalogram data.
Optionally, the target prediction mode includes a model prediction mode, and the prediction unit is further configured to: and inputting the electroencephalogram data into a preset deep neural network model, and predicting a target experience quality evaluation result by using a model prediction mode.
Optionally, the target prediction mode includes a weighted prediction mode, and the prediction unit is further configured to: and performing weighting processing on the electroencephalogram data, and predicting a target experience quality evaluation result by using a weighting prediction mode.
Optionally, the device further comprises a library establishing unit, configured to obtain a library establishing sample, and establish an electroencephalogram database based on the library establishing sample; wherein, the database establishing sample comprises: video quality parameter samples, index parameter samples, and electroencephalogram data samples.
Optionally, the index parameter includes a scene index table and/or a crowd index table, where the scene index table includes at least one of the following parameters: the method comprises the following steps of (1) screen size, screen brightness, terminal rating, an operating system, network conditions, viewing modes and environment classification; the crowd-index table includes at least one of the following parameters: preference categories, content preference categories, color preferences, viewing habits, viewing time, viewing period.
Optionally, the apparatus further comprises a service pushing unit for performing service pushing based on the user representation to push the video service of interest to the target user.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In an optional embodiment, the present embodiment further provides an electronic device, which includes a memory and a processor, where the memory stores a computer program operable on the processor, and the processor executes the computer program to implement the steps of the method of the foregoing method embodiment.
In an alternative embodiment, the present embodiment also provides a computer readable medium having non-volatile program code executable by a processor, wherein the program code causes the processor to perform the method of the above method embodiment.
In the description of the present embodiment, it should be noted that the terms "in" and the like indicate the orientation or the positional relationship based on the orientation or the positional relationship shown in the drawings, which are only for the convenience of describing the present invention and simplifying the description, but do not indicate or imply that the referred device or element must have a specific orientation, be configured in a specific orientation and operate, and thus, should not be construed as limiting the present embodiment. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the embodiments provided in the present embodiment, it should be understood that the disclosed method and apparatus may be implemented in other manners. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present embodiment or parts of the technical solution may be essentially implemented in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein.
Claims (10)
1. A method for determining experience quality based on user portrait and electroencephalogram data is characterized by comprising the following steps:
acquiring video quality parameters of a target video service and a user portrait of a target user;
mapping the user portrait based on a user portrait index table to obtain corresponding index parameters;
searching corresponding electroencephalogram data from an electroencephalogram database based on the video quality parameters and the index parameters;
predicting based on the electroencephalogram data to obtain a target experience quality evaluation result; and the target experience quality evaluation result is used for reflecting the evaluation of the target user on the target video service.
2. The method of claim 1, wherein predicting based on the electroencephalogram data to obtain a target quality of experience assessment result comprises:
and predicting the target experience quality evaluation result by utilizing a target prediction mode based on the electroencephalogram data.
3. The method of claim 2, wherein the target prediction mode comprises a model prediction mode, and the step of predicting the target quality of experience assessment result based on the electroencephalogram data by using the target prediction mode comprises:
and inputting the electroencephalogram data into a preset deep neural network model, and predicting the target experience quality evaluation result by using the model prediction mode.
4. The method of claim 2, wherein the target prediction mode comprises a weighted prediction mode, and the step of predicting the target quality of experience assessment result based on the electroencephalogram data by using the target prediction mode comprises:
and carrying out weighting processing on the electroencephalogram data, and predicting the target experience quality evaluation result by using the weighted prediction mode.
5. The method of claim 1, wherein prior to looking up corresponding brain electrical data from a brain electrical database based on the video quality parameter and the index parameter, the method further comprises:
acquiring a database building sample, and building an electroencephalogram database based on the database building sample; wherein the library building sample comprises: video quality parameter samples, index parameter samples, and electroencephalogram data samples.
6. The method according to claim 1, wherein the index parameters comprise a scene index table and/or a crowd index table, wherein the scene index table comprises at least one of the following parameters: the method comprises the following steps of (1) screen size, screen brightness, terminal rating, an operating system, network conditions, viewing modes and environment classification; the crowd index table includes at least one of the following parameters: preference categories, content preference categories, color preferences, viewing habits, viewing time, viewing period.
7. The method of claim 6, further comprising:
and performing service pushing based on the user portrait so as to push interested video services to the target user.
8. An experience quality determination apparatus based on a user portrait and electroencephalogram data, comprising:
the system comprises an acquisition unit, a processing unit and a display unit, wherein the acquisition unit is used for acquiring video quality parameters of a target video service and a user portrait of a target user;
the mapping unit is used for mapping the user portrait based on a user portrait index table to obtain a corresponding index parameter;
the searching unit is used for searching corresponding electroencephalogram data from an electroencephalogram database based on the video quality parameters and the index parameters;
the prediction unit is used for predicting based on the electroencephalogram data to obtain a target experience quality evaluation result; and the target experience quality evaluation result is used for reflecting the evaluation of the target user on the target video service.
9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the method of any of claims 1 to 7.
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