UGC Quality Assessment: Exploring the Impact of Saliency in Deep
  Feature-Based Quality Assessment
Xinyi Wang Angeliki Katsenou David Bull
The volume of User Generated Content (UGC) has increased in recent years. The
challenge with this type of content is assessing its quality. So far, the
state-of-the-art metrics are not exhibiting a very high correlation with
perceptual quality. In this paper, we explore state-of-the-art metrics that
extract/combine natural scene statistics and deep neural network features. We
experiment with these by introducing saliency maps to improve perceptibility.
We train and test our models using public datasets, namely, YouTube-UGC and
KoNViD-1k. Preliminary results indicate that high correlations are achieved by
using only deep features while adding saliency is not always boosting the
performance. Our results and code will be made publicly available to serve as a
benchmark for the research community and can be found on our project page:
https://github.com/xinyiW915/SPIE-2023-Supplementary.