Video Quality Assessment (VQA) is a very
challenging task due to its highly subjective nature. Moreover, many
factors influence VQA. Compression of video content, while necessary for
minimising transmission and storage requirements, introduces
distortions which can have detrimental effects on the perceived quality.
Especially when dealing with modern video coding standards, it is
extremely difficult to model the effects of compression due to the
unpredictability of encoding on different content types. Moreover,
transmission also introduces delays and other distortion types which
affect the perceived quality. Therefore, it would be highly beneficial
to accurately predict the perceived quality of video to be distributed
over modern content distribution platforms, so that specific actions
could be undertaken to maximise the Quality of Experience (QoE) of the
users. Traditional VQA techniques based on feature extraction and
modelling may not be sufficiently accurate. In this paper, a novel Deep
Learning (DL) framework is introduced for effectively predicting VQA of
video content delivery mechanisms based on end-to-end feature learning.
The proposed framework is based on Convolutional Neural Networks, taking
into account compression distortion as well as transmission delays.
Training and evaluation of the proposed framework are performed on a
user annotated VQA dataset specifically created to undertake this work.
The experiments show that the proposed methods can lead to high accuracy
of the quality estimation, showcasing the potential of using DL in
complex VQA scenarios.
@article{Giannopoulos_2018b,title = {Convolutional Neural Networks for Video Quality Assessment},
author = {Giannopoulos, Michalis and Tsagkatakis, Grigorios and Saverio, Blasi and Farzad, Toutounchi and Mouchtaris, Athanasios and Tsakalides, Panagiotis and Marta, Mrak and Ebroul, Izquierdo},
year = {2018},
date = {2018-09-26},
journal = {arXiv preprint arXiv:1809.10117},
abstract = {Video Quality Assessment (VQA) is a very challenging task due to its highly subjective nature. Moreover, many factors influence VQA. Compression of video content, while necessary for minimising transmission and storage requirements, introduces distortions which can have detrimental effects on the perceived quality. Especially when dealing with modern video coding standards, it is extremely difficult to model the effects of compression due to the unpredictability of encoding on different content types. Moreover, transmission also introduces delays and other distortion types which affect the perceived quality. Therefore, it would be highly beneficial to accurately predict the perceived quality of video to be distributed over modern content distribution platforms, so that specific actions could be undertaken to maximise the Quality of Experience (QoE) of the users. Traditional VQA techniques based on feature extraction and modelling may not be sufficiently accurate. In this paper, a novel Deep Learning (DL) framework is introduced for effectively predicting VQA of video content delivery mechanisms based on end-to-end feature learning. The proposed framework is based on Convolutional Neural Networks, taking into account compression distortion as well as transmission delays. Training and evaluation of the proposed framework are performed on a user annotated VQA dataset specifically created to undertake this work. The experiments show that the proposed methods can lead to high accuracy of the quality estimation, showcasing the potential of using DL in complex VQA scenarios.},
keywords = {Deep Learning, Higher-order Convolution, Video QoE Prediction},
pubstate = {published},
tppubtype = {article}
}