论文标题
使用CNN进行视频编码的速率控制参数的估计
Estimation of Rate Control Parameters for Video Coding Using CNN
论文作者
论文摘要
费率控制对于确保有效的视频传递至关重要。典型的费率控制算法依赖于位分配策略，可以适当地在框架之间分发位。由于参考帧对于利用时间冗余是必不可少的，因此内部框架通常被分配给可用位的较大部分。在本文中，提出了一种准确的估计位数和质量质量的方法，该方法可用于速率控制方案中的位分配。该算法基于深度学习，在该算法中，使用原始框架作为输入对网络进行训练，而编码后的压缩框架的扭曲和大小用作地面真相。提出了两种预测局部或全球扭曲的方法。
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.
