A fine-grain distortion and complexity aware parameter tuning model for the H.264/AVC encoder

  • Authors:

  • Mehdi Semsarzadeh;Atieh Lotfi;Mahmoud Reza Hashemi;Shervin Shirmohammadi

  • Affiliations:

  • Multimedia Processing Laboratory, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran and Distributed Collaborative Virtual Environments Research Lab, School of Elect ...;Multimedia Processing Laboratory, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran;Multimedia Processing Laboratory, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran;Multimedia Processing Laboratory, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran and Distributed Collaborative Virtual Environments Research Lab, School of Elect ...

  • Venue:
  • Image Communication
  • Year:
  • 2013

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Abstract

Most existing video encoders currently used in mobile applications are unable to gracefully degrade their output quality as the battery life nears its end. In other words, they cannot manage power consumption to efficiently utilize the available power resources. To be able to effectively adapt to changes in the encoder's software and hardware platforms, especially due to the power limitations of mobile devices, the effect of encoder parameters on the encoding quality and power consumption has to be represented using a Rate-Distortion-Complexity (R-D-C) model. Most existing R-D-C models only consider macroblock level parameters, and overlook other higher level parameters that may have a more significant impact on complexity. In this paper, the distortion and complexity of the H.264/AVC encoder is controlled considering a subset of higher level encoding parameters consisting of search range, number of reference frames, and motion vector resolution. First, the complexity of full and fast motion estimation methods is modeled in an implementation and platform independent manner. Then, using this complexity model, a common encoding parameter setting table is derived, which leads to the least amount of distortion for each complexity condition. Finally, a complexity control mechanism is proposed which tunes the encoding parameters in a real-time manner. The proposed model can be combined with other existing macroblock level models in order to design a two-phase fine grain complexity controller. Simulation results indicate that when our method is integrated with the direct resource allocation (DRA) approach, performance increases by an average of 1.02dB and 1.06dB for full and fast motion estimation approaches, respectively.