Dynamic computational complexity and bit allocation for optimizing H.264/AVC video compression

  • Authors:

  • E. Kaminsky;D. Grois;O. Hadar

  • Affiliations:

  • Electrical and Computer Engineering Department, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel;Electro-Optics Unit of the Engineering Department, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel;Communication Systems Engineering Department, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel

  • Venue:
  • Journal of Visual Communication and Image Representation
  • Year:
  • 2008

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Abstract

In this work, we present a novel approach for optimizing H.264/AVC video compression by dynamically allocating computational complexity (such as a number of CPU clocks) and bits for encoding each coding element (basic unit) within a video sequence, according to its predicted MAD (mean absolute difference). Our approach is based on a computational complexity-rate-distortion (C-R-D) analysis, which adds a complexity dimension to the conventional rate-distortion (R-D) analysis. Both theoretically and experimentally, we prove that by implementing the proposed approach for the dynamic allocation better results are achieved. We also prove that the optimal computational complexity allocation along with optimal bit allocation is better than the constant computational complexity allocation along with optimal bit allocation. In addition, we present a method and system for implementing the proposed approach, and for controlling computational complexity and bit allocation in real-time and off-line video coding. We divide each frame into one or more basic units, wherein each basic unit consists of at least one macroblock (MB), whose contents are related to a number of coding modes. We determine how much computational complexity and bits should be allocated for encoding each basic unit, and then allocate a corresponding group of coding modes and a quantization step-size, according to the estimated distortion (calculated by a linear regression model) of each basic unit and according to the remaining computational complexity and bits for encoding remaining basic units. For allocating the corresponding group of coding modes and the quantization step-size, we develop computational complexity-complexity step-rate (C-I-R) and rate-quantization step-size-computational complexity (R-Q-C) models.