  Deep learning methods are increasingly being applied in the optimisation of
video compression algorithms and can achieve significantly enhanced coding
gains, compared to conventional approaches. Such approaches often employ
Convolutional Neural Networks (CNNs) which are trained on databases with
relatively limited content coverage. In this paper, a new extensive and
representative video database, BVI-DVC, is presented for training CNN-based
video compression systems, with specific emphasis on machine learning tools
that enhance conventional coding architectures, including spatial resolution
and bit depth up-sampling, post-processing and in-loop filtering. BVI-DVC
contains 800 sequences at various spatial resolutions from 270p to 2160p and
has been evaluated on ten existing network architectures for four different
coding tools. Experimental results show that this database produces significant
improvements in terms of coding gains over three existing (commonly used)
image/video training databases under the same training and evaluation
configurations. The overall additional coding improvements by using the
proposed database for all tested coding modules and CNN architectures are up to
10.3% based on the assessment of PSNR and 8.1% based on VMAF.
