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.

翻译：近年来，用户生成内容（UGC）的数量大幅增长。这类内容面临的挑战在于如何评估其质量。目前，最先进的评价指标与感知质量之间的相关性并不高。本文探索了结合自然场景统计与深度神经网络特征的先进评价指标，并引入显著性图来提升感知能力。我们使用公开数据集（YouTube-UGC和KoNViD-1k）对模型进行训练与测试。初步结果表明，仅使用深度特征即可实现高相关性，而添加显著性并不总能提升性能。我们的结果和代码将公开发布，作为研究社区的基准，详情请见项目页面：https://github.com/xinyiW915/SPIE-2023-Supplementary。