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An EEG-Based Study on Perception of Video Distortion Under Various Content Motion Conditions
- Source :
- IEEE Transactions on Multimedia. 22:949-960
- Publication Year :
- 2020
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2020.
-
Abstract
- Human perception sensitivity to video distortion is vital for visual quality assessment (VQA). Different from the perception mechanism of image distortion that has been thoroughly studied, the perception of video distortion is inevitably influenced by motion of dynamic content due to the characteristics of the human visual system (HVS). In this paper, electroencephalography (EEG) is used as a novel psychophysiological method to study the human perception sensitivity to quantification-aroused video distortion under various content motion conditions. For this purpose, we conduct experiments to record the EEG signals of the subjects when they are watching distorted videos. According to the feature analysis of EEG data, the P300 component aroused by human perception of video quality change is selected as the indicator of human perception of distortion. By the means of classification based on linear discriminant analysis (LDA), it is found that the separability of the P300 component, which is measured by the area under curve (AUC) of the receiver operating characteristic (ROC), is positively correlated with the perceptibility of distortion. The correlation provides a valid psychophysiological method, which is exempt from being influenced by subjective bias due to human high-level cognitive activities, for evaluating distortion perceptibility. In addition, the regression analysis results demonstrate a sigmoid-typed quantitative relation between the perceptibility of distortion and separability of the P300 component. Based on such relation, the perceptibility thresholds of distortion corresponding to various content motion speeds are calibrated by EEG signals and it is found that the content motion speed has a significant impact on distortion perceptibility.
- Subjects :
- genetic structures
Computer science
media_common.quotation_subject
02 engineering and technology
Electroencephalography
Video quality
behavioral disciplines and activities
Perception
Distortion
0202 electrical engineering, electronic engineering, information engineering
Media Technology
medicine
Sensitivity (control systems)
Electrical and Electronic Engineering
media_common
medicine.diagnostic_test
business.industry
Pattern recognition
Linear discriminant analysis
Computer Science Applications
Signal Processing
Human visual system model
020201 artificial intelligence & image processing
sense organs
Artificial intelligence
business
psychological phenomena and processes
Subjects
Details
- ISSN :
- 19410077 and 15209210
- Volume :
- 22
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Multimedia
- Accession number :
- edsair.doi...........8e6814ec9ff4abd23895a28b4991fd10