For video compression, motion estimation is popularly employed to exploit the temporal correlation existing in video sequences. When a full search (FS) block matching algorithm (BMA) is used for estimating motion vectors, it requires very heavy computational complexity. Although several fast block matching algorithms have been proposed to solve this problem, those methods sacrifice their reconstructed image qualities. We propose a new motion estimation algorithm by considering matching criteria and statistical properties of object displacement. We exploit the relationship between the motion and frame difference of each block to choose a more compact search pattern for BMA. By changing the search pattern adaptively, we can improve motion prediction, while reducing computational complexity than other fast BMA algorithms.
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