用高阶特征预测硬件视频编码能耗，误差仅9%
A High-Level Feature Model to Predict the Encoding Energy of a Hardware Video Encoder
- 基于高阶特征与高斯过程回归建模
- 仅用P帧和单个关键帧时误差约9%
- 可预估不同分辨率和编码标准的能耗
当今社会，实时视频流和电池供电设备生成的内容广泛存在。实时视频编码依赖硬件编码器，而本文提出一种基于高斯过程回归的高阶特征模型，用于预测硬件视频编码器的编码能耗。在仅包含P帧和单个关键帧的评估设置下，模型预测能耗的平均绝对百分比误差约为9%。通过消融实验进一步验证，空间分辨率是影响硬件编码能耗的关键高阶特征。该模型可用于提前估算在不同空间分辨率、编码标准及编码预设下的视频编码能耗，具有实际应用价值。
原文摘要 · Abstract (English)
In today's society, live video streaming and user generated content streamed from battery powered devices are ubiquitous. Live streaming requires real-time video encoding, and hardware video encoders are well suited for such an encoding task. In this paper, we introduce a high-level feature model using Gaussian process regression that can predict the encoding energy of a hardware video encoder. In an evaluation setup restricted to only P-frames and a single keyframe, the model can predict the encoding energy with a mean absolute percentage error of approximately 9%. Further, we demonstrate with an ablation study that spatial resolution is a key high-level feature for encoding energy prediction of a hardware encoder. A practical application of our model is that it can be used to perform a prior estimation of the energy required to encode a video at various spatial resolutions, with different coding standards and codec presets.
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