BVI-DVC: A Training Database for Deep Video Compression

Fan Zhang, Di Ma, David Bull

Abstract:

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. BVIDVC 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 five 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.