  Deep learning is now playing an important role in enhancing the performance
of conventional hybrid video codecs. These learning-based methods typically
require diverse and representative training material for optimization in order
to achieve model generalization and optimal coding performance. However,
existing datasets either offer limited content variability or come with
restricted licensing terms constraining their use to research purposes only. To
address these issues, we propose a new training dataset, named BVI-AOM, which
contains 956 uncompressed sequences at various resolutions from 270p to 2160p,
covering a wide range of content and texture types. The dataset comes with more
flexible licensing terms and offers competitive performance when used as a
training set for optimizing deep video coding tools. The experimental results
demonstrate that when used as a training set to optimize two popular network
architectures for two different coding tools, the proposed dataset leads to
additional bitrate savings of up to 0.29 and 2.98 percentage points in terms of
PSNR-Y and VMAF, respectively, compared to an existing training dataset,
BVI-DVC, which has been widely used for deep video coding. The BVI-AOM dataset
is available for download under this link: (TBD).
