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.

中文翻译：

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使用CNN估计视频编码的速率控制参数

速率控制对于确保高效的视频传输至关重要。典型的速率控制算法依赖于比特分配策略，以在帧之间适当地分配比特。由于参考帧对于利用时间冗余至关重要，因此帧内帧通常分配有较大部分的可用位。本文提出了一种精确估计帧内比特数和质量的方法，可用于速率控制方案中的比特分配。该算法基于深度学习，使用原始帧作为输入训练网络，而编码后压缩帧的失真和大小被用作基本事实。提出了两种方法来预测局部或全局失真。