EEG-Based Video Quality Grading Using Transfer Learning
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Solution Overview
Problem
Existing methods for evaluating video data quality, particularly in cases of unsynchronized sound and picture, suffer from low accuracy due to subjective scoring methods that are influenced by personal prejudice and experience, leading to inconsistent and non-generalizable results.
Innovation Solution
A method utilizing electroencephalogram (EEG) data, including emotion and emotional response data, processed through transfer learning algorithms to determine a quality evaluation grade for video data with degraded quality, overcoming personal biases by introducing objective EEG-based evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If subjective scoring methods are used to evaluate video quality, then the evaluation process is simple and fast, but the accuracy and reliability of the evaluation results deteriorate due to personal subjectivity and prejudice
Solution Approach 1:
The patent replaces the mechanical system of subjective human scoring with an objective EEG-based measurement system. By using electroencephalogram data to detect and quantify user emotional responses to video quality degradation, the system eliminates personal subjectivity and prejudice while maintaining evaluation efficiency. The EEG signals provide physiological evidence of user experience, transforming the evaluation from a subjective mechanical process to an objective physiological measurement process.
2Measurement precision
If EEG data with both emotion and emotional response information is collected and processed through transfer learning, then the evaluation accuracy is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent applies transfer learning to pre-process and align the EEG data from different sources (emotion data and emotional response data) before final evaluation. This preliminary action of data alignment and feature extraction through transfer learning reduces the complexity of subsequent processing by establishing a unified data framework, making the system more manageable despite handling multiple data types.
Solution Approach 2:
The patent introduces transfer learning as an intermediary processing layer between raw EEG data collection and final quality evaluation. This intermediary component bridges the gap between different EEG data types (emotion and emotional response), enabling effective integration and analysis while managing system complexity through a structured intermediate processing stage.
Data Source
AI summary
The disclosure provides a method and an apparatus for determining a quality grade of video data, and relates to the field of data processing technologies, wherein the method includes: acquiring a plurality of initial EEG data; based on the plurality of initial EEG data, determining an initial EEG data set, wherein the initial EEG data set includes a first sub-data set and a second sub-data set, the first sub-data set is a data set built on the basis of emotional response electroencephalogram data, and the second sub-data set is a data set built on the basis of electroencephalogram emotion data; processing the first sub-data set and the second sub-data set by using a transfer learning algorithm to obtain a third sub-data set and a fourth sub-data set; and based on the third sub-data set and the fourth sub-data set, determining a quality evaluation grade of video data with degraded quality.