Energy Demand Prediction for Hardware Video Decoders Using Software Profiling

Abstract: Energy efficiency for video communications is essential for mobile devices with a limited battery capacity. Therefore, hardware decoder implementations are commonly used to significantly reduce the energetic load of video playback. The energy consumption of such a hardware implementation largely depends on a previously published specification of a video coding standard that defines which coding tools and methods are included. However, during the standardization of a video coding standard, the energy demand of a hardware implementation is unknown. Hence, the hardware complexity of coding tools is judged subjectively by experts from the field of hardware programming without using standardized assessment procedures. To solve this problem, we propose a method that accurately models the energy demand of existing hardware decoders with an average error of 1.79% by exploiting information from software decoder profiling. Motivated by the low estimation error, we propose a hardware decoding energy metric that can predict and estimate the energy demand of an unknown hardware implementation using information from existing hardware decoder implementations and available software implementations of the future video decoder. By using multiple video coding standards for model training, we can predict the relative energy demand of an unknown hardware decoder with a minimum error of 4.54% without using the corresponding hardware decoder for training.