Confidence Measure for Block-Based Motion Vector Field

نویسندگان

  • Karen Simonyan
  • Sergey Grishin
چکیده

In this paper we propose a confidence measure for block-based motion vector field. The measure is calculated as an average of two a posteriori estimates which reflect various aspects of MVF accuracy: motion-compensated interframe difference distribution and motion vectors distribution. Experimental results show that the proposed measure outperforms its contemporary counterpart while demanding less information about the motion estimation process.

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تاریخ انتشار 2008