UGC Quality Assessment: Exploring the Impact of Saliency in Deep Feature-Based Quality Assessment
This work addresses quality assessment for UGC, which is important for platforms and users, but it appears incremental as it builds on existing metrics without a major breakthrough.
The paper tackled the problem of assessing perceptual quality in User Generated Content (UGC) by exploring deep feature-based metrics and introducing saliency maps, finding that deep features alone achieved high correlations while saliency did not consistently boost performance.
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.