Encoding Time and Energy Model for SVT-AV1 based on Video Complexity

Eichermüller L, Chaudhari G, Katsavounidis I, Lei Z, Tmar H, Herglotz C, Kaup A (2024)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2024

Event location: Seoul KR

Open Access Link: https://arxiv.org/abs/2401.16067

Abstract

The share of online video traffic in global carbon dioxide emissions is growing steadily. To comply with the demand for video media, dedicated compression techniques are continuously optimized, but at the expense of increasingly higher computational demands and thus rising energy consumption at the video encoder side. In order to find the best trade-off between compression and energy consumption, modeling encoding energy for a wide range of encoding parameters is crucial. We propose an encoding time and energy model for SVT-AV1 based on empirical relations between the encoding time and video parameters as well as encoder configurations. Furthermore, we model the influence of video content by established content descriptors such as spatial and temporal information. We then use the predicted encoding time to estimate the required energy demand and achieve a prediction error of 19.6 % for encoding time and 20.9 % for encoding energy.

Authors with CRIS profile

Lena Eichermüller Lehrstuhl für Multimediakommunikation und Signalverarbeitung Christian Herglotz Lehrstuhl für Multimediakommunikation und Signalverarbeitung Andre Kaup Lehrstuhl für Multimediakommunikation und Signalverarbeitung

Involved external institutions

Facebook, Inc. US United States (USA) (US)

How to cite

APA:

Eichermüller, L., Chaudhari, G., Katsavounidis, I., Lei, Z., Tmar, H., Herglotz, C., & Kaup, A. (2024). Encoding Time and Energy Model for SVT-AV1 based on Video Complexity. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2024. Seoul, KR.

MLA:

Eichermüller, Lena, et al. "Encoding Time and Energy Model for SVT-AV1 based on Video Complexity." Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2024, Seoul 2024.

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