Adaptive video streaming requires efficient bitrate ladder construction to meet heterogeneous network conditions and end-user demands. Per-title optimized encoding typically traverses numerous encoding parameters to search the Pareto-optimal operating points for each video. Recently, researchers have attempted to predict the content-optimized bitrate ladder for pre-encoding overhead reduction. However, existing methods commonly estimate the encoding parameters on the Pareto front and still require subsequent pre-encodings. In this paper, we propose to directly predict the optimal transcoding resolution at each preset bitrate for efficient bitrate ladder construction. We adopt a Temporal Attentive Gated Recurrent Network to capture spatial-temporal features and predict transcoding resolutions as a multi-task classification problem. We demonstrate that content-optimized bitrate ladders can thus be efficiently determined without any pre-encoding. Our method well approximates the ground-truth bitrate-resolution pairs with a slight Bj{\o}ntegaard Delta rate loss of 1.21% and significantly outperforms the state-of-the-art fixed ladder.

直接预测每个预设比特率的最佳转码分辨率以实现高效比特率阶梯构建。采用时间注意力门控循环网络来捕捉时空特征，并将转码分辨率预测作为一个多任务分类问题。我们证明了通过此方法可以有效地确定内容优化的比特率阶梯，且不需要任何预编码。我们的方法近似于地面实况比特率-分辨率对，Bjøntegaard Delta 率损失为1.21％，并且明显优于现有的固定阶梯。

高效的逐个标题比特率梯度预测的最佳转码分辨率