  Digital media is ubiquitous and produced in ever-growing quantities. This
necessitates a constant evolution of compression techniques, especially for
video, in order to maintain efficient storage and transmission. In this work,
we aim at exploiting non-local redundancies in video data that remain difficult
to erase for conventional video codecs. We design convolutional neural networks
with a particular emphasis on low memory and computational footprint. The
parameters of those networks are trained on the fly, at encoding time, to
predict the residual signal from the decoded video signal. After the training
process has converged, the parameters are compressed and signalled as part of
the code of the underlying video codec. The method can be applied to any
existing video codec to increase coding gains while its low computational
footprint allows for an application under resource-constrained conditions.
Building on top of High Efficiency Video Coding, we achieve coding gains
similar to those of pretrained denoising CNNs while only requiring about 1% of
their computational complexity. Through extensive experiments, we provide
insights into the effectiveness of our network design decisions. In addition,
we demonstrate that our algorithm delivers stable performance under conditions
met in practical video compression: our algorithm performs without significant
performance loss on very long random access segments (up to 256 frames) and
with moderate performance drops can even be applied to single frames in
high-resolution low delay settings.
