{"ID":2855892,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.12754","arxiv_id":"2510.12754","title":"A High-Level Feature Model to Predict the Encoding Energy of a Hardware Video Encoder","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.","short_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...","url_abs":"https://arxiv.org/abs/2510.12754","url_pdf":"https://arxiv.org/pdf/2510.12754v1","authors":"[\"Diwakara Reddy\",\"Christian Herglotz\",\"André Kaup\"]","published":"2025-10-14T17:33:45Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"eess.SP\"]","methods":"[]","has_code":false}