Estimation of Rate Control Parameters for Video Coding Using CNN
This work addresses efficient video delivery for streaming and compression applications, but it is incremental as it builds on existing rate-control methods with deep learning enhancements.
The paper tackles the problem of estimating bits and quality for intra frames in video coding to improve rate control, proposing a CNN-based method that predicts local or global distortions with results showing accurate estimation.
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.