TY - GEN
T1 - Empirical evaluation of no-reference VQA methods on a natural video quality database
AU - Men, Hui
AU - Lin, Hanhe
AU - Saupe, Dietmar
N1 - We thank the German Research Foundation (DFG) for financial support within project A05 of SFB/Transregio 161.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/3
Y1 - 2017/7/3
N2 - No-Reference (NR) Video Quality Assessment (VQA) is a challenging task since it predicts the visual quality of a video sequence without comparison to some original reference video. Several NR-VQA methods have been proposed. However, all of them were designed and tested on databases with artificially distorted videos. Therefore, it remained an open question how well these NR-VQA methods perform for natural videos. We evaluated two popular VQA methods on our newly built natural VQA database KoNViD-1k. In addition, we found that merely combining five simple VQA-related features, i.e., contrast, colorfulness, blurriness, spatial information, and temporal information, already gave a performance about as well as those of the established NR-VQA methods. However, for all methods we found that they are unsatisfying when assessing natural videos (correlation coefficients below 0.6). These findings show that NR-VQA is not yet matured and in need of further substantial improvement.
AB - No-Reference (NR) Video Quality Assessment (VQA) is a challenging task since it predicts the visual quality of a video sequence without comparison to some original reference video. Several NR-VQA methods have been proposed. However, all of them were designed and tested on databases with artificially distorted videos. Therefore, it remained an open question how well these NR-VQA methods perform for natural videos. We evaluated two popular VQA methods on our newly built natural VQA database KoNViD-1k. In addition, we found that merely combining five simple VQA-related features, i.e., contrast, colorfulness, blurriness, spatial information, and temporal information, already gave a performance about as well as those of the established NR-VQA methods. However, for all methods we found that they are unsatisfying when assessing natural videos (correlation coefficients below 0.6). These findings show that NR-VQA is not yet matured and in need of further substantial improvement.
KW - empirical evaluation
KW - feature combination
KW - no-reference
KW - video quality assessment
UR - http://www.scopus.com/inward/record.url?scp=85026784228&partnerID=8YFLogxK
U2 - 10.1109/QoMEX.2017.7965644
DO - 10.1109/QoMEX.2017.7965644
M3 - Conference contribution
AN - SCOPUS:85026784228
SN - 978-1-5386-4025-8
T3 - 2017 9th International Conference on Quality of Multimedia Experience, QoMEX 2017
BT - 2017 9th International Conference on Quality of Multimedia Experience (QoMEX 2017)
PB - IEEE
T2 - 9th International Conference on Quality of Multimedia Experience, QoMEX 2017
Y2 - 29 May 2017 through 2 June 2017
ER -