A High-Level Feature Model to Predict the Encoding Energy of a Hardware Video Encoder
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917013437284352 |
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| author | Reddy, Diwakara Herglotz, Christian Kaup, André |
| author_facet | Reddy, Diwakara Herglotz, Christian Kaup, André |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_12754 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A High-Level Feature Model to Predict the Encoding Energy of a Hardware Video Encoder Reddy, Diwakara Herglotz, Christian Kaup, André Image and Video Processing Signal Processing 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. |
| title | A High-Level Feature Model to Predict the Encoding Energy of a Hardware Video Encoder |
| topic | Image and Video Processing Signal Processing |
| url | https://arxiv.org/abs/2510.12754 |