  Humans do not perceive all parts of a scene with the same resolution, but
rather focus on few regions of interest (ROIs). Traditional Object-Based codecs
take advantage of this biological intuition, and are capable of non-uniform
allocation of bits in favor of salient regions, at the expense of increased
distortion the remaining areas: such a strategy allows a boost in perceptual
quality under low rate constraints. Recently, several neural codecs have been
introduced for video compression, yet they operate uniformly over all spatial
locations, lacking the capability of ROI-based processing. In this paper, we
introduce two models for ROI-based neural video coding. First, we propose an
implicit model that is fed with a binary ROI mask and it is trained by
de-emphasizing the distortion of the background. Secondly, we design an
explicit latent scaling method, that allows control over the quantization
binwidth for different spatial regions of latent variables, conditioned on the
ROI mask. By extensive experiments, we show that our methods outperform all our
baselines in terms of Rate-Distortion (R-D) performance in the ROI. Moreover,
they can generalize to different datasets and to any arbitrary ROI at inference
time. Finally, they do not require expensive pixel-level annotations during
training, as synthetic ROI masks can be used with little to no degradation in
performance. To the best of our knowledge, our proposals are the first
solutions that integrate ROI-based capabilities into neural video compression
models.
