用机器学习预测整个视频库的压缩耗时，更准且可实时更新。
Predicting total time to compress a video corpus using online inference systems
- 构建模型直接预测整库压缩总时间，而非单个视频。
- 相比单视频预测，整体时间误差降低两倍以上。
- 支持在线更新，实时预测误差低于5%，适合云服务使用。
预测视频库压缩/转码的计算成本对云服务和点播（VOD）提供商的资源管理至关重要。目前，云视频服务用户只有在任务完成后才知晓转码成本。以往研究聚焦于单个视频的压缩时间预测，从而估算成本。本文提出新的机器学习系统，直接预测整个视频库的总成本，这一目标更符合用户实际需求。我们在x264、x265两种视频编码器及一个新型高质量视频数据集上评估了该系统。结果表明，整库时间预测精度比单视频预测高出两倍以上。此外，我们设计了在线推理框架，随着文件处理进度动态更新模型。结合视频计算开销与合适预测器的选择，最终预测误差低于5%，约是之前通用预测方法的两倍改进。
原文摘要 · Abstract (English)
Predicting the computational cost of compressing/transcoding clips in a video corpus is important for resource management of cloud services and VOD (Video On Demand) providers. Currently, customers of cloud video services are unaware of the cost of transcoding their files until the task is completed. Previous work concentrated on predicting perclip compression time, and thus estimating the cost of video compression. In this work, we propose new Machine Learning (ML) systems which predict cost for the entire corpus instead. This is a more appropriate goal since users are not interested in per-clip cost but instead the cost for the whole corpus. In this work, we evaluate our systems with respect to two video codecs (x264, x265) and a novel high-quality video corpus. We find that the accuracy of aggregate time prediction for a video corpus more than two times better than using per-clip predictions. Furthermore, we present an online inference framework in which we update the ML models as files are processed. A consideration of video compute overhead and appropriate choice of ML predictor for each fraction of corpus completed yields a prediction error of less than 5%. This is approximately two times better than previous work which proposed generalised predictors.
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