TY - GEN A1 - Herglotz, Christian A1 - Och, Hannah A1 - Meyer, Anna A1 - Ramasubbu, Geetha A1 - Eichermüller, Lena A1 - Kränzler, Matthias A1 - Brand, Fabian A1 - Fischer, Kristian A1 - Nguyen, Dat Thanh A1 - Regensky, Andy A1 - Kaup, André T1 - The Bjøntegaard Bible Why Your Way of Comparing Video Codecs May Be Wrong T2 - IEEE Transactions on Image Processing N2 - In this paper, we provide an in-depth assessment on the Bjøntegaard Delta. We construct a large data set of video compression performance comparisons using a diverse set of metrics including PSNR, VMAF, bitrate, and processing energies. These metrics are evaluated for visual data types such as classic perspective video, 360° video, point clouds, and screen content. As compression technology, we consider multiple hybrid video codecs as well as state-of-the-art neural network based compression methods. Using additional supporting points in-between standard points defined by parameters such as the quantization parameter, we assess the interpolation error of the Bjøntegaard-Delta (BD) calculus and its impact on the final BD value. From the analysis, we find that the BD calculus is most accurate in the standard application of rate-distortion comparisons with mean errors below 0.5 percentage points. For other applications and special cases, e.g., VMAF quality, energy considerations, or inter-codec comparisons, the errors are higher (up to 5 percentage points), but can be halved by using a higher number of supporting points. We finally come up with recommendations on how to use the BD calculus such that the validity of the resulting BD-values is maximized. Main recommendations are as follows: First, relative curve differences should be plotted and analyzed. Second, the logarithmic domain should be used for saturating metrics such as SSIM and VMAF. Third, BD values below a certain threshold indicated by the subset error should not be used to draw recommendations. Fourth, using two supporting points is sufficient to obtain rough performance estimates. KW - Measurement KW - Calculus KW - Codecs KW - Interpolation KW - Image coding KW - Distortion KW - Visualization Y1 - 2024 U6 - https://doi.org/10.1109/TIP.2023.3346695 SN - 1057-7149 SN - 1941-0042 IS - Volume 33 SP - 987 EP - 1001 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 - 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 -