  Rate-control is essential to ensure efficient video delivery. Typical
rate-control algorithms rely on bit allocation strategies, to appropriately
distribute bits among frames. As reference frames are essential for exploiting
temporal redundancies, intra frames are usually assigned a larger portion of
the available bits. In this paper, an accurate method to estimate number of
bits and quality of intra frames is proposed, which can be used for bit
allocation in a rate-control scheme. The algorithm is based on deep learning,
where networks are trained using the original frames as inputs, while
distortions and sizes of compressed frames after encoding are used as ground
truths. Two approaches are proposed where either local or global distortions
are predicted.
