TY - GEN
T1 - Classifying perceptual experience of tone-mapped high dynamic range videos through EEG
AU - Moon, Seong Eun
AU - Lee, Jong Seok
PY - 2014/11/7
Y1 - 2014/11/7
N2 - High dynamic range (HDR) imaging has potential for providing immersive experience of multimedia contents. HDR contents are expected to have better perceptual quality than conventional low dynamic range (LDR) contents, but the perceptual difference in the brain between HDR and LDR contents has not been adequately studied. In this paper, we investigate perceptual experience of tone-mapped HDR videos based on electroencephalography (EEG) classification. A support vector machine (SVM) classification system is constructed using the acquired EEG signals to explore implicitly measured perceptual difference between tone-mapped HDR and LDR videos. As a result, average accuracies of 82.14% and 42.86% are obtained in a subject-dependent scenario and a subject-independent scenario, respectively. This shows that it is possible to distinguish perceptual responses for tone-mapped HDR and LDR videos in a subjectdependent manner. Further, features selected for classification are investigated in each classification scenario. Although the spatial position of the features on the scalp varies across subjects, gamma band powers are generally effective for classification.
AB - High dynamic range (HDR) imaging has potential for providing immersive experience of multimedia contents. HDR contents are expected to have better perceptual quality than conventional low dynamic range (LDR) contents, but the perceptual difference in the brain between HDR and LDR contents has not been adequately studied. In this paper, we investigate perceptual experience of tone-mapped HDR videos based on electroencephalography (EEG) classification. A support vector machine (SVM) classification system is constructed using the acquired EEG signals to explore implicitly measured perceptual difference between tone-mapped HDR and LDR videos. As a result, average accuracies of 82.14% and 42.86% are obtained in a subject-dependent scenario and a subject-independent scenario, respectively. This shows that it is possible to distinguish perceptual responses for tone-mapped HDR and LDR videos in a subjectdependent manner. Further, features selected for classification are investigated in each classification scenario. Although the spatial position of the features on the scalp varies across subjects, gamma band powers are generally effective for classification.
KW - Electroencephalography (EEG)
KW - Gamma band
KW - High dynamic range (HDR)
KW - Quality of experience (QoE)
UR - http://www.scopus.com/inward/record.url?scp=84919371807&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84919371807&partnerID=8YFLogxK
U2 - 10.1145/2662996.2663010
DO - 10.1145/2662996.2663010
M3 - Conference contribution
AN - SCOPUS:84919371807
T3 - PIVP 2014 - Proceedings of the 1st International Workshop on Perception Inspired Video Processing, Workshop of MM 2014
SP - 27
EP - 32
BT - PIVP 2014 - Proceedings of the 1st International Workshop on Perception Inspired Video Processing, Workshop of MM 2014
PB - Association for Computing Machinery
T2 - PIVP 2014 - 1st International Workshop on Perception Inspired Video Processing, Workshop of MM 2014
Y2 - 7 November 2014
ER -