@misc{RamasubbuKaupHerglotz, author = {Ramasubbu, Geetha and Kaup, Andr{\´e} and Herglotz, Christian}, title = {Modeling the Energy Consumption of the HEVC Software Encoding Process using Processor events}, series = {IEEE 26th International Workshop on Multimedia Signal Processing (MMSP)}, journal = {IEEE 26th International Workshop on Multimedia Signal Processing (MMSP)}, publisher = {IEEE}, isbn = {979-8-3503-8725-4}, issn = {2473-3628}, doi = {10.1109/MMSP61759.2024.10743858}, abstract = {Developing energy-efficient video encoding algorithms is highly important due to the high processing complexities and, consequently, the high energy demand of the encoding process. To accomplish this, the energy consumption of the video encoders must be studied, which is only possible with a complex and dedicated energy measurement setup. This emphasizes the need for simple energy estimation models, which estimate the energy required for the encoding. Our paper investigates the possibility of estimating the energy demand of a HEVC software CPU-encoding process using processor events. First, we perform energy measurements and obtain processor events using dedicated profiling software. Then, by using the measured energy demand of the encoding process and profiling data, we build an encoding energy estimation model that uses the processor events of the ultrafast encoding preset to obtain the energy estimate for complex encoding presets with a mean absolute percentage error of 5.36\% when averaged over all the presets. Additionally, we present an energy model that offers the possibility of obtaining energy distribution among various encoding sub-processes. energy models from literature. By using a unified evaluation framework we show how accurately the required decoding energy for different decoding systems can be approximated. We give thorough explanations on the model parameters and explain how the model variables are derived. To show the modeling capabilities in general, we test the estimation performance for different decoding software and hardware solutions, where we find that the proposed model outperforms the models from literature by reaching frame-wise mean estimation errors of less than 7\% for software and less than 15\% for hardware based systems.}, language = {en} } @misc{HerglotzKraenzlerXuetal., author = {Herglotz, Christian and Kr{\"a}nzler, Matthias and Xu, Bide and Kaup, Andr{\´e}}, title = {Decoding Energy Optimization for Video Coding Using Model-Driven Gradient Descent}, series = {IEEE 26th International Workshop on Multimedia Signal Processing (MMSP)}, journal = {IEEE 26th International Workshop on Multimedia Signal Processing (MMSP)}, isbn = {979-8-3503-8725-4}, issn = {2473-3628}, doi = {10.1109/MMSP61759.2024.10743503}, abstract = {Nowadays, a large part of the global energy consumption caused by video communications can be attributed to end-user devices such as smartphones, tablet PCs, and TV sets. In this paper, we present a method to increase the performance of an existing algorithm dedicated to reduce the end-user side energy consumption during video streaming. The algorithm, which is called decoding-energy-rate-distortion optimization (DERDO), exploits a decoding energy model during encoding and chooses coding modes in such a way that the software decoding energy is minimized. In this paper, we develop a dedicated gradient descent approach for DERDO that refines specific energy coefficients used for decoding energy modeling. We find that this approach boosts the performance of DERDO by increasing the energy savings by at least 5\% with respect to standard DERDO. As a consequence, we observe decoding energy savings of more than 40\% and more than 7\% for practical encoder and decoder implementations of HEVC and H.264/AVC, respectively, when compared to standard encoding using classic rate-distortion optimization.}, 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 = {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{KraenzlerHerglotzKaup, author = {Kr{\"a}nzler, Matthias and Herglotz, Christian and Kaup, Andr{\´e}}, title = {A Comprehensive Review of Software and Hardware Energy Efficiency of Video Decoders}, series = {Picture Coding Symposium (PCS) 2024}, journal = {Picture Coding Symposium (PCS) 2024}, publisher = {IEEE}, issn = {2472-7822}, doi = {10.1109/PCS60826.2024.10566363}, abstract = {Energy and compression efficiency are two essential parts of modern video decoder implementations that have to be considered. This work comprehensively studies the following six video coding formats regarding compression and decoding energy efficiency: AVC, VP9, HEVC, AV1, VVC, and AVM. We first evaluate the energy demand of reference and optimized software decoder implementations. Furthermore, we consider the influence of the usage of SIMD instructions on those decoder implementations. We find that AV1 is a sweet spot for optimized software decoder implementations with an additional energy demand of 16.55\% and bitrate savings of -43.95\% compared to VP9. We furthermore evaluate the hardware decoding energy demand of four video coding formats. Thereby, we show that AV1 has energy demand increases by 117.50\% compared to VP9. For HEVC, we found a sweet spot in terms of energy demand with an increase of 6.06\% with respect to VP9. Relative to their optimized software counterparts, hardware video decoders reduce the energy consumption to less than 9\% compared to software decoders}, language = {en} }