PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Towards a Direct Measure of Video Quality Perception using EEG
S Scholler, S Bosse, MS Treder, B Blankertz, G Curio, KR Müller and T Wiegand
IEEE Trans Image Process 2012.

Abstract

An approach for the direct measurement of video quality change perception using electroencephalography (EEG) is presented. Subjects viewed 8s 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 or not they had perceived a quality change. In response to a quality change, a positive voltage change in EEG (the socalled P3 component) was observed at a 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 video quality change perception within the presented paradigm. By applying machine learning techniques, we could classify on a single-trial basis whether or not 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. Concluding, 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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:9453
Deposited By:Martijn Schreuder
Deposited On:16 March 2012