A Bitstream Feature Based Model for Video Decoding Energy Estimation

TL;DR

This paper presents a model that uses a small set of bitstream features to accurately estimate the decoding energy consumption across multiple video codecs, aiding energy-efficient video processing.

Contribution

The authors introduce a novel bitstream feature-based model capable of accurately estimating decoding energy for various codecs, demonstrating broad applicability and low feature requirements.

Findings

Less than 20 features achieve under 8% mean error

Model applies to HEVC, H.264, H.263, VP9 codecs

Enables energy-aware video decoding optimization

Abstract

In this paper we show that a small amount of bit stream features can be used to accurately estimate the energy consumption of state-of-the-art software and hardware accelerated decoder implementations for four different video codecs. By testing the estimation performance on HEVC, H.264, H.263, and VP9 we show that the proposed model can be used for any hybrid video codec. We test our approach on a high amount of different test sequences to prove the general validity. We show that less than 20 features are sufficient to obtain mean estimation errors that are smaller than 8%. Finally, an example will show the performance trade-offs in terms of rate, distortion, and decoding energy for all tested codecs.