  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.
