CN116523574A - Quality of experience evaluation method and system based on user portrait and electroencephalogram data - Google Patents
Quality of experience evaluation method and system based on user portrait and electroencephalogram data Download PDFInfo
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Abstract
The invention discloses a quality of experience evaluation method and a system based on user portrait and electroencephalogram data, wherein the method comprises the following steps: providing brain health service, acquiring target brain health data and target user portraits, and constructing an index table according to the target user portraits, wherein the index table comprises index parameters for searching portraits; pre-constructing an electroencephalogram database, wherein the electroencephalogram data comprises brain health data samples, index parameter samples and electroencephalogram data; searching corresponding electroencephalogram data by utilizing the index parameters and the brain health data; and inputting the brain electricity data, the brain health data and the index parameters into a trained brain electricity service quality prediction model for prediction to obtain a experience quality evaluation result of the target user on brain health service.
Description
Technical Field
The invention relates to the technical field of brain health, in particular to a quality of experience evaluation method and system based on user portraits and electroencephalogram data.
Background
Currently, conventional brain health data evaluation belongs to objective evaluation methods, such as MSE (Mean Squared Error, mean square error), PSNE (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index, structural similarity), and the like. These objective brain health data evaluation methods are based on a single picture and rely on the original code stream to complete. This approach suffers from a drawback in subjective consistency. In order to solve the defects of the traditional objective evaluation method, subjective experience evaluation standards are presented, wherein the subjective experience evaluation standards comprise five aspects of picture definition, quality stability, brain health smoothness, brain health content and observation conditions, and quality evaluation is performed through methods such as user scoring. However, in the explicit quality of experience assessment method, due to the problems that terminal client groups are numerous, scoring dynamics are affected by human body bias, and the like, subjective quality of experience assessment results with high uniformity of rules are difficult to obtain, and upper-layer application analysis based on QOE (Quality of Experience ) results is inconvenient.
Disclosure of Invention
One of the purposes of the invention is to provide a method and a system for evaluating the experience quality based on user portrait and electroencephalogram data, which utilize EEG (brain waves) to perform subjective experience evaluation, and perform subjective experience quality evaluation on brain wave response of external stimulus through the brain.
The invention further aims to provide a quality of experience evaluation method and a system based on user portraits and electroencephalogram data, wherein the method and the system are used for constructing personal fine feature selection by utilizing the user portraits, and constructing quality of experience evaluation according to the fine user portraits, so that a quality of experience result of a user can be displayed more truly.
The invention further aims to provide a quality of experience evaluation method and a system based on the user portrait and the electroencephalogram data, wherein the method and the system construct mapping of the user portrait and the electroencephalogram data through index parameters, and find accurate electroencephalogram data based on the user portrait through the index parameters, so that the quality of experience evaluation method is more accurate.
In order to achieve at least one of the above objects, the present invention further provides a quality of experience assessment method based on user portrait and electroencephalogram data, the method comprising:
providing brain health service, acquiring target brain health data and target user portraits, and constructing an index table according to the target user portraits, wherein the index table comprises index parameters for searching portraits;
pre-constructing an electroencephalogram database, wherein the electroencephalogram data comprises brain health data samples, index parameter samples and electroencephalogram data;
searching corresponding electroencephalogram data by utilizing the index parameters and the brain health data;
and inputting the brain electricity data, the brain health data and the index parameters into a trained brain electricity service quality prediction model for prediction to obtain a experience quality evaluation result of the target user on brain health service.
According to one preferred embodiment of the present invention, the brain health data includes: at least one of a clip, a content speed, an initial buffer, a sound and picture synchronization, a picture quality, and a noise.
According to another preferred embodiment of the present invention, the index table includes a scene index table, wherein the scene index table includes at least one index parameter selected from a group consisting of screen size, screen brightness, terminal score, operating system, network status, viewing mode, and environment classification.
According to another preferred embodiment of the present invention, the index table comprises a crowd index table, wherein the crowd index table comprises at least one index parameter of preference classification, content preference classification, color preference, viewing habit, viewing time, viewing period.
According to another preferred embodiment of the present invention, the target brain health data, the index parameter corresponding to the target user portrait, and the electroencephalogram data are input into the deep learning network, and the quality of experience score of the current electroencephalogram data of the deep learning network data is utilized, where the quality of experience score indicates the level of brain health service quality.
According to another preferred embodiment of the invention, the brain health services include cell phones, live, electroencephalogram phones, and VR/AR.
According to another preferred embodiment of the present invention, the method for constructing an electroencephalogram quality of service prediction model includes: and respectively constructing input features of each target individual by the electroencephalogram data, the brain health data and the index parameters, respectively constructing a training set and a testing set by the input features, and setting super parameters, wherein non-numerical features in the electroencephalogram data, the brain health data and the index parameters are converted into numerical features.
According to another preferred embodiment of the invention, the training set is input into a deep convolutional neural network for training, the electroencephalogram service quality prediction model is constructed after the super parameters are adjusted, the experience quality evaluation result meeting the prediction requirement is output, and the test set is utilized to verify the electroencephalogram service quality prediction model.
In order to achieve at least one of the above objects, the present invention further provides a quality of experience evaluation system based on user portraits and electroencephalogram data, which performs the above quality of experience evaluation method based on user portraits and electroencephalogram data.
The present invention provides a computer-readable storage medium storing a computer program that is executed by a processor to implement the above-described quality of experience assessment method based on user portraits and electroencephalogram data.
Drawings
FIG. 1 shows a flow chart of a quality of experience assessment method based on user portraits and electroencephalogram data.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the invention. The preferred embodiments in the following description are by way of example only and other obvious variations will occur to those skilled in the art. The basic principles of the invention defined in the following description may be applied to other embodiments, variations, modifications, equivalents, and other technical solutions without departing from the spirit and scope of the invention.
It will be understood that the terms "a" and "an" should be interpreted as referring to "at least one" or "one or more," i.e., in one embodiment, the number of elements may be one, while in another embodiment, the number of elements may be plural, and the term "a" should not be interpreted as limiting the number.
Referring to fig. 1, the invention discloses a quality of experience evaluation method and a system based on user portrait and electroencephalogram data, wherein the method comprises the following steps: providing brain health service for users, acquiring brain health data of target users, further constructing user portraits, and mapping the user portraits to obtain an index table of the user portraits, wherein the index table comprises index parameters, and an index value based on the user portraits can be constructed by using the index parameters.
And further constructing an electroencephalogram database, wherein the electroencephalogram data comprises user brain health data, index parameters and electroencephalogram data with association relations, the electroencephalogram data can be searched for corresponding electroencephalogram data in the electroencephalogram database by utilizing the brain health data and the index parameters, the electroencephalogram data, the brain health data and the index parameters are input into a deep learning model to conduct experience quality prediction, evaluation scores of the user on experience quality service of the electroencephalogram service are obtained, and whether the evaluation scores meet the requirements of the experience quality service of the electroencephalogram service is judged.
Specifically, the constructed user portrait index table comprises a scene index table and a crowd index table, wherein index parameters in the scene index table comprise: at least one of screen size, screen brightness, terminal scoring, operating system, network status, viewing mode, and environmental classification. The index parameters in the crowd index table comprise at least one of preference classification, content preference classification, color preference, viewing habit, viewing time and viewing period. After the index parameters and the brain health data are acquired, the index parameters and the brain health data are further utilized to find the brain electrical data from a database, wherein the brain electrical data are acquired after a user performs brain electrical service, and at the moment, the brain electrical data are selected and evaluated based on the aspects of the user's portrait and the brain health data and then are stored in the brain electrical database, so that each brain electrical data has related brain health data and index parameter association relation.
It is worth mentioning that the deep learning model is utilized in the invention for predicting the evaluation value of the experience quality of the brain health service. Specifically, an index parameter, brain health data and brain electrical data acquired by each brain health server of each user are built into a single feature sample of a single brain electrical service quality prediction model, the index parameter, brain health data and brain electrical data are built into feature data meeting the requirements of a deep learning model format, the index parameter, brain health data and brain electrical data acquired by all brain health services of all users are built into a training sample set and a testing sample set, and the training sample set is input into the deep learning model. Preferably the deep learning model may be a model including but not limited to a deep convolutional neural network. In the training process of the training set, the deep convolutional neural network model meets the deviation requirement of the experience quality true value by adjusting the super-parameters, so that the electroencephalogram service quality prediction model is constructed. After the electroencephalogram service quality prediction model is built, electroencephalogram service quality of experience assessment results according to the self portrait characteristics of the user can be obtained by inputting brain health data, index parameters and corresponding electroencephalogram data related to the user.
The invention discloses an electroencephalogram service, which comprises but is not limited to on-demand, live broadcast, electroencephalogram telephone, VR/AR and the like, wherein the experience quality in the invention refers to subjective experience of a terminal user on a service and a network, is comprehensive experience of a psychological 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 quality of experience can reflect the current relationship between the quality of service and network and user experience, integrates all influencing factors of a service level, a user level and a network level, and directly reflects the acceptance degree of the terminal user on the network service. The quality of experience in the invention is obtained from five experience evaluation results of picture definition, quality stability, brain current smoothness, brain electrical content and observation conditions in the brain health data.
According to the implicit experience quality evaluation method taking EEG as a core, subjective experience quality evaluation is carried out through the response of the brain to the electric wave of external stimulus, and the EEG quality evaluation method is based on a physiological system of a human body, so that universality is achieved, and the subjective experience quality evaluation result with consistent rules is easy to obtain. The index parameter in the invention can reflect the specific characteristics and requirements of the target user, can provide accurate electroencephalogram data according to the index parameter, and can provide more accurate personalized and scenic assessment according to the specific characteristics and requirements of the user.
In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such embodiments, the computer program may be downloaded and installed from a network via a communication portion, and/or installed from a removable medium. The above-described functions defined in the method of the present application are performed when the computer program is executed by a Central Processing Unit (CPU). It should be noted that the computer readable medium described in the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wire segments, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
It will be understood by those skilled in the art that the embodiments of the present invention described above and shown in the drawings are merely illustrative and not restrictive of the current invention, and that this invention has been shown and described with respect to the functional and structural principles thereof, without departing from such principles, and that any modifications or adaptations of the embodiments of the invention may be possible and practical.
Claims (10)
1. A quality of experience assessment method based on user portraits and electroencephalogram data is characterized by comprising the following steps:
providing brain health service, acquiring target brain health data and target user portraits, and constructing an index table according to the target user portraits, wherein the index table comprises index parameters for searching portraits;
pre-constructing an electroencephalogram database, wherein the electroencephalogram database comprises brain health data samples, index parameter samples and electroencephalogram data;
searching corresponding electroencephalogram data by utilizing the index parameters and the brain health data;
and inputting the brain electricity data, the brain health data and the index parameters into a trained brain electricity service quality prediction model for prediction to obtain a experience quality evaluation result of the target user on brain health service.
2. The method of claim 1, wherein the brain health data comprises: at least one of a clip, a content speed, an initial buffer, a sound and picture synchronization, a picture quality, and a noise.
3. The method of claim 1, wherein the index table comprises a scene index table, wherein the scene index table comprises at least one index parameter selected from the group consisting of screen size, screen brightness, terminal score, operating system, network status, viewing mode, and environmental classification.
4. The method of claim 1, wherein the index table comprises a crowd index table, wherein the crowd index table comprises at least one index parameter selected from a group consisting of preference classification, content preference classification, color preference, viewing habit, viewing time, and viewing period.
5. The quality of experience assessment method based on user portraits and electroencephalogram data according to claim 1, wherein the target brain health data, index parameters corresponding to the target user portraits and electroencephalogram data are input into the deep learning network, the score of the quality of experience of the current electroencephalogram data is output by utilizing the deep learning network data, and the quality of experience score indicates the level of brain health service quality.
6. The method of claim 1, wherein the brain health services include live, electroencephalogram, and VR/AR.
7. The method for evaluating the quality of experience based on user portraits and electroencephalogram data according to claim 1, wherein the method for constructing the prediction model of the quality of electroencephalogram service comprises the following steps: and respectively constructing input features of each target individual by the electroencephalogram data, the brain health data and the index parameters, respectively constructing a training set and a testing set by the input features, and setting super parameters, wherein non-numerical features in the electroencephalogram data, the brain health data and the index parameters are converted into numerical features.
8. The quality of experience assessment method based on user portraits and electroencephalogram data according to claim 7, wherein the training set is input into a deep convolutional neural network for training, the electroencephalogram quality of service prediction model is constructed after super parameters are adjusted, quality of experience assessment results meeting prediction requirements are output, and the electroencephalogram quality of service prediction model is verified by the aid of the testing set.
9. A quality of experience assessment system based on user portraits and electroencephalogram data, characterized in that the system performs a quality of experience assessment method based on user portraits and electroencephalogram data as claimed in any one of claims 1 to 8.
10. A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program that is executed by a processor to implement a quality of experience assessment method based on user portraits and electroencephalogram data according to any one of claims 1 to 8.
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| CN112416987A (en) * | 2020-11-24 | 2021-02-26 | 致讯科技(天津)有限公司 | Experience quality determination method and device based on user portrait and electroencephalogram data |
| CN113554597A (en) * | 2021-06-23 | 2021-10-26 | 清华大学 | Image quality evaluation method and device based on electroencephalogram characteristics |
| CN114358089A (en) * | 2022-01-24 | 2022-04-15 | 北京蕴岚科技有限公司 | Training method and device of speech evaluation model based on electroencephalogram and electronic equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112383828A (en) * | 2019-12-12 | 2021-02-19 | 致讯科技(天津)有限公司 | Experience quality prediction method, equipment and system with brain-like characteristic |
| CN112416987A (en) * | 2020-11-24 | 2021-02-26 | 致讯科技(天津)有限公司 | Experience quality determination method and device based on user portrait and electroencephalogram data |
| CN113554597A (en) * | 2021-06-23 | 2021-10-26 | 清华大学 | Image quality evaluation method and device based on electroencephalogram characteristics |
| CN114358089A (en) * | 2022-01-24 | 2022-04-15 | 北京蕴岚科技有限公司 | Training method and device of speech evaluation model based on electroencephalogram and electronic equipment |
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