Abstract
Steady-state visual evoked potentials (SSVEPs) are neural responses, measurable using electroencephalography (EEG), that are directly linked to sensory processing of visual stimuli. In this paper, SSVEP is used to assess the perceived quality of texture images. The EEG-based assessment method is compared with conventional methods, and recorded EEG data are correlated to obtained mean opinion scores (MOSs). A dimensionality reduction technique for EEG data called spatio-spectral decomposition (SSD) is adapted for the SSVEP framework and used to extract physiologically meaningful and plausible neural components from the EEG recordings. It is shown that the use of SSD not only increases the correlation between neural features and MOS to r=-0.93 , but also solves the problem of channel selection in an EEG-based image-quality assessment.
| Original language | English |
|---|---|
| Article number | 7902111 |
| Pages (from-to) | 1694-1706 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Circuits and Systems for Video Technology |
| Volume | 28 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - 2018 Aug 1 |
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Keywords
- classification
- EEG
- MOS
- spatio-spectral decomposition
- SSVEP
- video quality assessment
ASJC Scopus subject areas
- Media Technology
- Electrical and Electronic Engineering
Cite this
Assessing Perceived Image Quality Using Steady-State Visual Evoked Potentials and Spatio-Spectral Decomposition. / Bosse, Sebastian; Acqualagna, Laura; Samek, Wojciech; Porbadnigk, Anne K.; Curio, Gabriel; Blankertz, Benjamin; Muller, Klaus; Wiegand, Thomas.
In: IEEE Transactions on Circuits and Systems for Video Technology, Vol. 28, No. 8, 7902111, 01.08.2018, p. 1694-1706.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Assessing Perceived Image Quality Using Steady-State Visual Evoked Potentials and Spatio-Spectral Decomposition
AU - Bosse, Sebastian
AU - Acqualagna, Laura
AU - Samek, Wojciech
AU - Porbadnigk, Anne K.
AU - Curio, Gabriel
AU - Blankertz, Benjamin
AU - Muller, Klaus
AU - Wiegand, Thomas
PY - 2018/8/1
Y1 - 2018/8/1
N2 - Steady-state visual evoked potentials (SSVEPs) are neural responses, measurable using electroencephalography (EEG), that are directly linked to sensory processing of visual stimuli. In this paper, SSVEP is used to assess the perceived quality of texture images. The EEG-based assessment method is compared with conventional methods, and recorded EEG data are correlated to obtained mean opinion scores (MOSs). A dimensionality reduction technique for EEG data called spatio-spectral decomposition (SSD) is adapted for the SSVEP framework and used to extract physiologically meaningful and plausible neural components from the EEG recordings. It is shown that the use of SSD not only increases the correlation between neural features and MOS to r=-0.93 , but also solves the problem of channel selection in an EEG-based image-quality assessment.
AB - Steady-state visual evoked potentials (SSVEPs) are neural responses, measurable using electroencephalography (EEG), that are directly linked to sensory processing of visual stimuli. In this paper, SSVEP is used to assess the perceived quality of texture images. The EEG-based assessment method is compared with conventional methods, and recorded EEG data are correlated to obtained mean opinion scores (MOSs). A dimensionality reduction technique for EEG data called spatio-spectral decomposition (SSD) is adapted for the SSVEP framework and used to extract physiologically meaningful and plausible neural components from the EEG recordings. It is shown that the use of SSD not only increases the correlation between neural features and MOS to r=-0.93 , but also solves the problem of channel selection in an EEG-based image-quality assessment.
KW - classification
KW - EEG
KW - MOS
KW - spatio-spectral decomposition
KW - SSVEP
KW - video quality assessment
UR - http://www.scopus.com/inward/record.url?scp=85038229055&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85038229055&partnerID=8YFLogxK
U2 - 10.1109/TCSVT.2017.2694807
DO - 10.1109/TCSVT.2017.2694807
M3 - Article
AN - SCOPUS:85038229055
VL - 28
SP - 1694
EP - 1706
JO - IEEE Transactions on Circuits and Systems for Video Technology
JF - IEEE Transactions on Circuits and Systems for Video Technology
SN - 1051-8215
IS - 8
M1 - 7902111
ER -