@inproceedings{134c3ec649f8478bb17db3fdded2e65a,
title = "No-reference video quality assessment via feature learning",
abstract = "In this paper, we propose a novel 'Opinion Free' (OF) No-Reference Video Quality Assessment (NR-VQA) algorithm based on frame-level unsupervised feature learning and hysteresis temporal pooling. The system consists of three components: feature extraction with max-min pooling, frame quality prediction and temporal pooling. Frame level features are first extracted by unsupervised feature learning and used to train a linear Support Vector Regressor (SVR) for predicting quality scores frame by frame. Frame-level quality scores are then combined by temporal pooling to obtain a single video quality score. We tested the proposed method on the LIVE video quality database and experimental results show that without training on human opinion scores the proposed method is comparable to state-of-the-art NR-VQA algorithms.",
keywords = "feature learning, human opinion, no-reference, temporal pooling, Video quality assessment",
author = "Jingtao Xu and Peng Ye and Yong Liu and David Doermann",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.",
year = "2014",
month = jan,
day = "28",
doi = "10.1109/ICIP.2014.7025098",
language = "English",
series = "2014 IEEE International Conference on Image Processing, ICIP 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "491--495",
booktitle = "2014 IEEE International Conference on Image Processing, ICIP 2014",
address = "United States",
}