title: Toward a direct measure of video quality perception using EEG.
abstract: 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.
authors.affiliation: Machine Learning Laboratory, Berlin Institute of Technology, 10587 Berlin, Germany. simon.scholler@googlemail.com
authors.forename: Simon
authors.initials: S
authors.lastname: Scholler
authors.forename: Sebastian
authors.initials: S
authors.lastname: Bosse
authors.forename: Matthias Sebastian
authors.initials: MS
authors.lastname: Treder
authors.forename: Benjamin
authors.initials: B
authors.lastname: Blankertz
authors.forename: Gabriel
authors.initials: G
authors.lastname: Curio
authors.forename: Klaus-Robert
authors.initials: KR
authors.lastname: Müller
authors.forename: Thomas
authors.initials: T
authors.lastname: Wiegand
mesh_terms: D000328:Adult; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual / Q000502:physiology*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014741:Video Recording / Q000379:methods*; D014787:Vision Tests / Q000379:methods; D014796:Visual Perception / Q000502:physiology*