@misc{EichermuellerChaudhariKatsavounidisetal., author = {Eicherm{\"u}ller, Lena and Chaudhari, Gaurang and Katsavounidis, Ioannis and Lei, Zhijun and Tmar, Hassene and Herglotz, Christian and Kaup, Andr{\´e}}, title = {Encoding Time and Energy Model for SVT-AV1 Based on Video Complexity}, series = {ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, journal = {ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, publisher = {IEEE}, isbn = {979-8-3503-4485-1}, issn = {2379-190X}, doi = {10.1109/ICASSP48485.2024.10446602}, pages = {3370 -- 3374}, 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.}, language = {en} } @misc{EichermuellerChaudhariKatsavounidisetal., author = {Eicherm{\"u}ller, Lena and Chaudhari, Gaurang and Katsavounidis, Ioannis and Lei, Zhijun and Tmar, Hassene and Herglotz, Christian and Kaup, Andr{\´e}}, title = {SVT-AV1 Encoding Bitrate Estimation Using Motion Search Information}, series = {32nd European Signal Processing Conference (EUSIPCO)}, journal = {32nd European Signal Processing Conference (EUSIPCO)}, publisher = {IEEE}, isbn = {978-9-4645-9361-7}, issn = {2076-1465}, pages = {937 -- 941}, abstract = {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.}, language = {en} } @misc{EichermuellerChaudhariKatsavounidisetal., author = {Eicherm{\"u}ller, Lena and Chaudhari, Gaurang and Katsavounidis, Ioannis and Lei, Zhijun and Tmar, Hassene and Herglotz, Christian and Kaup, Andre}, title = {Encoding Time and Energy Model for SVT-AV1 based on Video Complexity}, series = {arXiv - accepted for IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, journal = {arXiv - accepted for IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, doi = {10.48550/arXiv.2401.16067}, pages = {5}, 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.}, language = {en} }