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.

翻译：在线视频流量在全球二氧化碳排放中的占比持续增长。为满足视频媒体需求，专用压缩技术不断优化，但代价是编码端计算需求急剧攀升，导致能耗持续上升。为在压缩效率与能耗之间寻求最佳平衡，建立覆盖广泛编码参数的编码能耗模型至关重要。本研究基于编码时间与视频参数及编码器配置之间的经验关系，提出针对SVT-AV1的编码时间与能耗模型。此外，通过空间信息、时间信息等成熟的内容描述符建模视频内容的影响。利用预测的编码时间估算所需能耗，最终实现编码时间预测误差19.6%、编码能耗预测误差20.9%的性能。