Machine learning (ML) is an essential part of recent advances in computer science. To fully exploit its potential, ML-based algorithms require a considerable amount of annotated data to be used for training. This represents a severe limitation in the field of image and video quality assessment since obtaining large-scale annotated databases is time-consuming and expensive. Moreover, the resulting quality estimators are mainly restricted only to the usecases included in the dataset used for their training. This paper proposes a strategy allowing for combination of multiple databases for training of objective image and video quality assessment algorithms. Using this strategy, the algorithms can be trained using all of the existing relevant databases together which allows to increase the amount of data-points and usecases in orders of magnitude. The potential of the proposed method is demonstrated by re-training the combination of features from Video Multimethod Assessment Fusion (VMAF) algorithm resulting in the significant improvement of its performance with respect to 20 video databases.

中文翻译：

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使用多个数据库训练目标图像和视频质量估计器

机器学习 (ML) 是计算机科学最新进展的重要组成部分。为了充分发挥其潜力，基于 ML 的算法需要大量带注释的数据用于训练。这代表了图像和视频质量评估领域的严重限制，因为获得大规模带注释的数据库既耗时又昂贵。此外，由此产生的质量估计器主要仅限于用于训练的数据集中包含的用例。本文提出了一种策略，允许组合多个数据库来训练目标图像和视频质量评估算法。使用这种策略，可以将所有现有的相关数据库一起使用来训练算法，这允许以数量级增加数据点和用例的数量。