One of the main challenges in no-reference video quality assessment is temporal variation in a video. Methods typically were designed and tested on videos with artificial distortions, without considering spatial and temporal variations simultaneously. We propose a no-reference spatiotemporal feature combination model which extracts spatiotemporal information from a video, and tested it on a database with authentic distortions. Comparing with other methods, our model gave satisfying performance for assessing the quality of natural videos.
%0 Conference Paper
%1 conf/qomex/MenLS18
%A Men, Hui
%A Lin, Hanhe
%A Saupe, Dietmar
%B Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX)
%D 2018
%I IEEE
%K from:leonkokkoliadis 2018 sfbtrr161 A05
%P 1-3
%R 10.1109/QoMEX.2018.8463426
%T Spatiotemporal Feature Combination Model for No-Reference Video Quality Assessment
%U https://ieeexplore.ieee.org/document/8463426
%X One of the main challenges in no-reference video quality assessment is temporal variation in a video. Methods typically were designed and tested on videos with artificial distortions, without considering spatial and temporal variations simultaneously. We propose a no-reference spatiotemporal feature combination model which extracts spatiotemporal information from a video, and tested it on a database with authentic distortions. Comparing with other methods, our model gave satisfying performance for assessing the quality of natural videos.
@inproceedings{conf/qomex/MenLS18,
abstract = {One of the main challenges in no-reference video quality assessment is temporal variation in a video. Methods typically were designed and tested on videos with artificial distortions, without considering spatial and temporal variations simultaneously. We propose a no-reference spatiotemporal feature combination model which extracts spatiotemporal information from a video, and tested it on a database with authentic distortions. Comparing with other methods, our model gave satisfying performance for assessing the quality of natural videos.},
added-at = {2020-02-26T16:10:47.000+0100},
author = {Men, Hui and Lin, Hanhe and Saupe, Dietmar},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/207d983cbe44a7c8dfbdf1f2103eee296/sfbtrr161},
booktitle = {Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX)},
doi = {10.1109/QoMEX.2018.8463426},
ee = {https://doi.org/10.1109/QoMEX.2018.8463426},
interhash = {f987b75e73bae1f391c009858c8ba932},
intrahash = {07d983cbe44a7c8dfbdf1f2103eee296},
keywords = {from:leonkokkoliadis 2018 sfbtrr161 A05},
pages = {1-3},
publisher = {IEEE},
timestamp = {2020-02-26T15:10:47.000+0100},
title = {Spatiotemporal Feature Combination Model for No-Reference Video Quality Assessment},
url = {https://ieeexplore.ieee.org/document/8463426},
year = 2018
}
