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This paper presents a block-overlap-based validity metric for use as a measure of motion vector (MV) validity and to improve the quality of the motion field. In contrast to other validity metrics in the literature, the proposed metric is not sensitive to image features and does not require the use of neighboring MVs or manual thresholds. Using a hybrid de-interlacer, it is shown that the proposed metric outperforms other block-based validity metrics in the literature. To help regularize the ill-posed nature of motion estimation, the proposed validity metric is also used as a regularizer in an energy minimization framework to determine the optimal MV. Experimental results show that the proposed energy minimization framework outperforms several existing motion estimation methods in the literature in terms of MV and interpolation quality. For interpolation quality, our algorithm outperforms all other block-based methods as well as several complex optical flow methods. In addition, it is one of the fastest implementations at the time of this writing.
Date of Publication: April 2013