Toward a Direct Measure of Video Quality Perception Using EEG

• Published in: IEEE transactions on image processing Vol. 21; no. 5; pp. 2619 - 2629
• Main Authors: Scholler, S., Bosse, S., Treder, M. S., Blankertz, B., Curio, G., Muller, Klaus-Robert, Wiegand, T.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2012; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Applied sciences; Artificial Intelligence; Assessments; Biological and medical sciences; Brain; Brain - physiology; Change detection; Clips; Computerized, statistical medical data processing and models in biomedicine; Covariance matrix; Detection, estimation, filtering, equalization, prediction; Distortion measurement; Electric potential; Electroencephalography; Electroencephalography (EEG); Electroencephalography - methods; Evoked Potentials, Visual - physiology; Exact sciences and technology; Female; Humans; Image Interpretation, Computer-Assisted - methods; Image processing; Information, signal and communications theory; Male; Materials; Medical management aid. Diagnosis aid; Medical sciences; Perception; Reproducibility of Results; Sensitivity and Specificity; Signal and communications theory; Signal processing; Signal, noise; Telecommunications and information theory; Vectors; video coding; Video compression; video quality; Video Recording - methods; Vision Tests - methods; Visual Perception - physiology; Visualization; Voltage; Video coding; Change detection; Objective analysis; Video signal; Gradient index; video quality; Electroencephalography; Video codecs; Subjective evaluation; Video signal processing; perception; Direct method; Image quality; Calling line identification presentation; Electroencephalography (EEG); Artificial intelligence
• ISSN: 1057-7149; 1941-0042; 1941-0042
• DOI: 10.1109/TIP.2012.2187672

An approach to the direct measurement of perception of video quality change using electroencephalography (EEG) is presented. Subjects viewed 8-s video clips while their brain activity was registered using EEG. The video signal was either uncompressed at full length or changed from uncompressed to a lower quality level at a random time point. The distortions were introduced by a hybrid video codec. Subjects had to indicate whether they had perceived a quality change. In response to a quality change, a positive voltage change in EEG (the so-called P3 component) was observed at latency of about 400-600 ms for all subjects. The voltage change positively correlated with the magnitude of the video quality change, substantiating the P3 component as a graded neural index of the perception of video quality change within the presented paradigm. By applying machine learning techniques, we could classify on a single-trial basis whether a subject perceived a quality change. Interestingly, some video clips wherein changes were missed (i.e., not reported) by the subject were classified as quality changes, suggesting that the brain detected a change, although the subject did not press a button. In conclusion, abrupt changes of video quality give rise to specific components in the EEG that can be detected on a single-trial basis. Potentially, a neurotechnological approach to video assessment could lead to a more objective quantification of quality change detection, overcoming the limitations of subjective approaches (such as subjective bias and the requirement of an overt response). Furthermore, it allows for real-time applications wherein the brain response to a video clip is monitored while it is being viewed.