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arXiv · 2510.12754

A High-Level Feature Model to Predict the Encoding Energy of a Hardware Video Encoder

Diwakara Reddy·Christian Herglotz·André Kaup
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Abstract

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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Diwakara Reddy, Christian Herglotz, André Kaup. 2025-10-14. A High-Level Feature Model to Predict the Encoding Energy of a Hardware Video Encoder. https://arxiv.org/abs/2510.12754

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