TY - GEN
T1 - Automated Quality Assessment for Compressed Vibrotactile Signals Using Multi-Method Assessment Fusion
AU - Noll, Andreas
AU - Hofbauer, Markus
AU - Muschter, Evelyn
AU - Li, Shu Chen
AU - Steinbach, Eckehard
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Design and optimization of vibrotactile codecs require precise measurements of the compressed signals' perceptual quality. In this paper, we present two computational approaches for estimating vibrotactile signal quality. First, we propose a novel full-reference vibrotactile quality metric called Spectral Perceptual Quality Index (SPQI), which computes a similarity score based on a computed perceptually weighted error measure. Second, we use the concept of Multi-Method Assessment Fusion (MAF) to predict the subjective quality. MAF uses a Support Vector Machine regressor to fuse multiple elementary metrics into a final quality score, which preserves the strengths of the individual metrics. We evaluate both proposed quality assessment methods on an extended subjective dataset, which we introduce as part of this work. For two of three tested vibrotactile codecs, the MSE between subjective ratings and the SPQI is reduced by 64% and 92%, respectively compared to the state of the art. With our MAF approach, we obtain the only currently available metric that accurately predicts real human user experiments for all three tested codecs. The MAF estimations reduce the average MSE to the subjective ratings over all three tested codecs by 59% compared to the best performing elementary metric.
AB - Design and optimization of vibrotactile codecs require precise measurements of the compressed signals' perceptual quality. In this paper, we present two computational approaches for estimating vibrotactile signal quality. First, we propose a novel full-reference vibrotactile quality metric called Spectral Perceptual Quality Index (SPQI), which computes a similarity score based on a computed perceptually weighted error measure. Second, we use the concept of Multi-Method Assessment Fusion (MAF) to predict the subjective quality. MAF uses a Support Vector Machine regressor to fuse multiple elementary metrics into a final quality score, which preserves the strengths of the individual metrics. We evaluate both proposed quality assessment methods on an extended subjective dataset, which we introduce as part of this work. For two of three tested vibrotactile codecs, the MSE between subjective ratings and the SPQI is reduced by 64% and 92%, respectively compared to the state of the art. With our MAF approach, we obtain the only currently available metric that accurately predicts real human user experiments for all three tested codecs. The MAF estimations reduce the average MSE to the subjective ratings over all three tested codecs by 59% compared to the best performing elementary metric.
UR - http://www.scopus.com/inward/record.url?scp=85130602165&partnerID=8YFLogxK
U2 - 10.1109/HAPTICS52432.2022.9765599
DO - 10.1109/HAPTICS52432.2022.9765599
M3 - Conference contribution
AN - SCOPUS:85130602165
T3 - IEEE Haptics Symposium, HAPTICS
BT - 2022 IEEE Haptics Symposium, HAPTICS 2022
PB - IEEE Computer Society
T2 - 27th IEEE Haptics Symposium, HAPTICS 2022
Y2 - 21 March 2022 through 24 March 2022
ER -