IEEE Trans Image Process - Toward a direct measure of video quality perception using EEG.

Tópicos

{ chang(1828) time(1643) increas(1301) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ activ(1138) subject(705) human(624) }
{ signal(2180) analysi(812) frequenc(800) }
{ model(2656) set(1616) predict(1553) }
{ network(2748) neural(1063) input(814) }
{ use(2086) technolog(871) perceiv(783) }
{ imag(2675) segment(2577) method(1081) }
{ clinic(1479) use(1117) guidelin(835) }
{ howev(809) still(633) remain(590) }
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Resumo

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.

Resumo Limpo

approach direct measur percept video qualiti chang use electroencephalographi eeg present subject view s video clip brain activ regist use eeg video signal either uncompress full length chang uncompress lower qualiti level random time point distort introduc hybrid video codec subject indic whether perceiv qualiti chang respons qualiti chang posit voltag chang eeg socal p compon observ latenc ms subject voltag chang posit correl magnitud video qualiti chang substanti p compon grade neural index percept video qualiti chang within present paradigm appli machin learn techniqu classifi singletri basi whether subject perceiv qualiti chang interest video clip wherein chang miss ie report subject classifi qualiti chang suggest brain detect chang although subject press button conclus abrupt chang video qualiti give rise specif compon eeg can detect singletri basi potenti neurotechnolog approach video assess lead object quantif qualiti chang detect overcom limit subject approach subject bias requir overt respons furthermor allow realtim applic wherein brain respons video clip monitor view

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