# Estimation of Rate Control Parameters for Video Coding Using CNN

arXiv: https://arxiv.org/abs/2003.06315 · HTML: https://mlsanity.com/paper/2003.06315/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2020.

## Built on by (most cited first, 1 of 1)

- [Towards Transparent Application of Machine Learning in Video Processing](https://mlsanity.com/paper/2105.12700/) (2021)

## Abstract (arXiv)

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.