Journal of Real-Time Image Processing

, Volume 12, Issue 2, pp 495–507 | Cite as

Low power HEVC software decoder for mobile devices

  • Erwan Raffin
  • Erwan Nogues
  • Wassim Hamidouche
  • Seppo Tomperi
  • Maxime Pelcat
  • Daniel Menard
Special Issue Paper

Abstract

In the context of mobile handheld devices, energy consumption is a primary concern and the process of video decoding is often among the most resource-intensive applications. Recent embedded processors are equipped with advanced features such as dynamic voltage frequency scaling (DVFS) in order to reduce their power consumption. These features can be used to perform low power video decoding when no hardware decoding support is available for a given standard. High efficiency video coding (HEVC) is a recent video standard offering state-of-the-art compression rates and advanced parallel processing solutions. This paper presents strategies for the power optimization of a real-time software HEVC decoder on NEON architecture. These strategies include the exploitation of data and task-level parallelism, as well as the use of a new frequency control system to optimize the processor DVFS, based on an estimation of the decoding complexity. Extensive power measurement results, based on a multi-core ARM big.LITTLE processor, are provided and compared to state-of-the-art. These results show that the proposed open-source implementation can reach an energy consumption below 21 nJ/px for HD decoding at 2.2 Mbits/s.

Keywords

HEVC H.265 DVFS Low power processing Software video decoder 

Notes

Acknowledgments

This work is partially supported by BPI France, Region Ile-de-France, Region Bretagne and Rennes Metropole through the GreenVideo Project and by European Celtic-Plus through the H2B2VS Project. The authors would like to thank all the contributors of the OpenHEVC open source project leaded by Mickaël Raulet.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Erwan Raffin
    • 1
  • Erwan Nogues
    • 1
  • Wassim Hamidouche
    • 1
  • Seppo Tomperi
    • 2
  • Maxime Pelcat
    • 1
  • Daniel Menard
    • 1
  1. 1.INSA Rennes, IETR, UMR CNRS 6164, UEBRennesFrance
  2. 2.VTT Technical Research Centre of FinlandOuluFinland

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