Power-aware content-adaptive H.264 video encoding

Avin Kumar Kannur, Baoxin Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations


H.264 is a computationally intensive video codec striving for achieving the best quality for the compressed video. The computational complexity poses as a challenge for power-constrained applications. We present a system level complexity reduction for H.264 video encoding by allocating resources based on computational complexity and quality trade-off. We develop a framework which allocates the computational power of the encoder adaptive to video contents and also scales with the available battery power using a ROI classification method. Analysis is done to profile the key modules of the encoder which can be power-optimized while allocating resources. The results of the encoder module analysis are combined with the motion content analysis to obtain a power efficient encoder parameter set which reduces the computations and hence the power consumed. Our simulation results on the JM H.264 framework confirm our hypothesis and computational savings of more than 50% with quality degradation less than 1% is achieved thereby extending it's feasibility for battery powered wireless devices.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Acoustics, Speech, and Signal Processing - Proceedings, ICASSP 2009
Number of pages4
StatePublished - 2009
Event2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009 - Taipei, Taiwan, Province of China
Duration: Apr 19 2009Apr 24 2009

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149


Other2009 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2009
Country/TerritoryTaiwan, Province of China


  • H.264 video encoding
  • Power optimization
  • ROI coding
  • Wireless devices

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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