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.
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.
Abstract:Current developments in video encoding technology lead to continuously improving compression performance but at the expense of increasingly higher computational demands. Regarding the online video traffic increases during the last years and the concomitant need for video encoding, encoder complexity control mechanisms are required to restrict the processing time to a sufficient extent in order to find a reasonable trade-off between performance and complexity. We present a complexity control mechanism in SVT-AV1 by using speed-adaptive preset switching to comply with the remaining time budget. This method enables encoding with a user-defined time constraint within the complete preset range with an average precision of 8.9 \% without introducing any additional latencies.