Skip to Main content Skip to Navigation
Journal articles

Profiling and Modelling of HEVC Intra Video Encoder’s Energy Consumption for Next Generation WVSNS

Achraf Ait-Beni-Ifit Othmane Alaoui-Fdili Patrick Corlay 1, 2 François-Xavier Coudoux 1, 2 Driss Aboutajdine 
2 COMNUM - IEMN - COMmunications NUMériques - IEMN
IEMN-DOAE - Institut d’Électronique, de Microélectronique et de Nanotechnologie - Département Opto-Acousto-Électronique - UMR 8520
Abstract : Energy consumption is of main concern in the field of Wireless Video Sensor Networks (WVSNs) where energy resources are limited, consisting only in the battery of the sensor nodes that determines their lifetime. In this paper, we propose an empirical parametric model to predict the energy consumption of a High Efficiency Video Coding (HEVC) based video encoder in its intra-only mode, used in the context of the next generation WVSNs. Such model is of great interest to adapt the waste of energy of the encoding phase to the remaining energy budget of the node, while meeting the required video quality. The proposed model predicts the energy consumption, considering the adopted Quantization Parameter (QP) and the Frame Rate parameter (FR). A Raspberry Pi 2 card based video sensor node is used for modelling and validation, considering different configurations and spatial resolutions. The obtained results demonstrate that the proposed model describes well the occurred energy dissipation during the video encoding phase, with an average prediction error of 4.5%.
Complete list of metadata

https://hal-uphf.archives-ouvertes.fr/hal-03565939
Contributor : Kathleen TORCK Connect in order to contact the contributor
Submitted on : Friday, February 11, 2022 - 11:57:28 AM
Last modification on : Wednesday, March 23, 2022 - 3:51:16 PM

Identifiers

Citation

Achraf Ait-Beni-Ifit, Othmane Alaoui-Fdili, Patrick Corlay, François-Xavier Coudoux, Driss Aboutajdine. Profiling and Modelling of HEVC Intra Video Encoder’s Energy Consumption for Next Generation WVSNS. Lecture Notes in Computer Science, Springer, 2017, Lecture Notes in Computer Science, 10299, pp.472-482. ⟨10.1007/978-3-319-59647-1_34⟩. ⟨hal-03565939⟩

Share

Metrics

Record views

5