TY - GEN A1 - Eichermüller, Lena A1 - Chaudhari, Gaurang A1 - Katsavounidis, Ioannis A1 - Lei, Zhijun A1 - Tmar, Hassene A1 - Herglotz, Christian A1 - Kaup, André T1 - Encoding Time and Energy Model for SVT-AV1 Based on Video Complexity T2 - ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) N2 - 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. KW - Energy consumption KW - Estimation error KW - Multicore processing KW - Computational modeling KW - Signal processing KW - Media KW - Encoding Y1 - 2024 SN - 979-8-3503-4485-1 SN - 979-8-3503-4486-8 U6 - https://doi.org/10.1109/ICASSP48485.2024.10446602 SN - 2379-190X SN - 1520-6149 SP - 3370 EP - 3374 PB - IEEE ER - TY - GEN A1 - Eichermüller, Lena A1 - Chaudhari, Gaurang A1 - Katsavounidis, Ioannis A1 - Lei, Zhijun A1 - Tmar, Hassene A1 - Herglotz, Christian A1 - Kaup, André T1 - SVT-AV1 Encoding Bitrate Estimation Using Motion Search Information T2 - 32nd European Signal Processing Conference (EUSIPCO) N2 - Enabling high compression efficiency while keeping encoding energy consumption at a low level, requires prioritization of which videos need more sophisticated encoding techniques. However, the effects vary highly based on the content, and information on how good a video can be compressed is required. This can be measured by estimating the encoded bitstream size prior to encoding. We identified the errors between estimated motion vectors from Motion Search, an algorithm that predicts temporal changes in videos, correlates well to the encoded bitstream size. Combining Motion Search with Random Forests, the encoding bitrate can be estimated with a Pearson correlation of above 0.96. KW - Bit rate KW - Estimation KW - Signal processing algorithms KW - Signal processing KW - Feature extraction KW - Size measurement KW - Encoding KW - Vectors KW - Random forests KW - Videos Y1 - 2024 SN - 978-9-4645-9361-7 SN - 979-8-3315-1977-3 SN - 2076-1465 SN - 2219-5491 SP - 937 EP - 941 PB - IEEE ER - TY - GEN A1 - Eichermüller, Lena A1 - Chaudhari, Gaurang A1 - Katsavounidis, Ioannis A1 - Lei, Zhijun A1 - Tmar, Hassene A1 - Herglotz, Christian A1 - Kaup, Andre T1 - Encoding Time and Energy Model for SVT-AV1 based on Video Complexity T2 - arXiv - accepted for IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) N2 - 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. Y1 - 2024 U6 - https://doi.org/10.48550/arXiv.2401.16067 ER -