  Human perception is at the core of lossy video compression, with numerous
approaches developed for perceptual quality assessment and improvement over the
past two decades. In the determination of perceptual quality, different
spatio-temporal regions of the video differ in their relative importance to the
human viewer. However, since it is challenging to infer or even collect such
fine-grained information, it is often not used during compression beyond
low-level heuristics. We present a framework which facilitates research into
fine-grained subjective importance in compressed videos, which we then utilize
to improve the rate-distortion performance of an existing video codec (x264).
The contributions of this work are threefold: (1) we introduce a web-tool which
allows scalable collection of fine-grained perceptual importance, by having
users interactively paint spatio-temporal maps over encoded videos; (2) we use
this tool to collect a dataset with 178 videos with a total of 14443 frames of
human annotated spatio-temporal importance maps over the videos; and (3) we use
our curated dataset to train a lightweight machine learning model which can
predict these spatio-temporal importance regions. We demonstrate via a
subjective study that encoding the videos in our dataset while taking into
account the importance maps leads to higher perceptual quality at the same
bitrate, with the videos encoded with importance maps preferred $1.8 \times$
over the baseline videos. Similarly, we show that for the 18 videos in test
set, the importance maps predicted by our model lead to higher perceptual
quality videos, $2 \times$ preferred over the baseline at the same bitrate.
