  Enabling high compression efficiency while keeping encoding energy
consumption at a low level, requires prioritization of which videos need more
sophisticated encoding techniques. However, the effects vary highly based on
the content, and information on how good a video can be compressed is required.
This can be measured by estimating the encoded bitstream size prior to
encoding. We identified the errors between estimated motion vectors from Motion
Search, an algorithm that predicts temporal changes in videos, correlates well
to the encoded bitstream size. Combining Motion Search with Random Forests, the
encoding bitrate can be estimated with a Pearson correlation of above 0.96.
