• DocumentCode
    2560624
  • Title

    Improved CNN algorithm for H.264 motion estimation partitions

  • Author

    Koskinen, Lauri ; Halonen, Kari ; Paasio, Ari

  • Author_Institution
    Electron. Circuit Design Lab., Helsinki Univ. of Technol., Finland
  • fYear
    2005
  • fDate
    28-30 May 2005
  • Firstpage
    142
  • Lastpage
    145
  • Abstract
    To take full advantage of the motion estimation of the new video coding standard H.264, low-power video encoders will need specific hardware accelerators. Presented here is an improved partitioning method to decrease the computational load of variable block size motion estimation. The two-step partitioning method improves on a previous three-step method. The method is derived from a cellular nonlinear network (CNN) segmentation algorithm and, along with the partition, indicates early termination of motion estimation and the skip modes of H.264. The algorithm achieves better rate-distortion performance when compared to motion estimation with only 16×16 sized blocks and, for low bit rates, equivalent performance when compared to Lagrange optimization.
  • Keywords
    cellular neural nets; image segmentation; motion estimation; video coding; CNN segmentation algorithm; H.264 motion estimation partition; cellular nonlinear network; low-power video encoders; partitioning method; video coding standard; Analog computers; Cameras; Cellular neural networks; Hardware; Lagrangian functions; Mobile computing; Motion estimation; Partitioning algorithms; Sensor arrays; Video coding;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cellular Neural Networks and Their Applications, 2005 9th International Workshop on
  • Print_ISBN
    0-7803-9185-3
  • Type

    conf

  • DOI
    10.1109/CNNA.2005.1543181
  • Filename
    1543181