An optimal motion vector regularization method using variance-distortion curve

This paper proposes a new motion vector (MV) regularization method. We present an optimization method of the tradeoff between motion accuracy and regularity. The proposed method adopts Lagrangian multiplier and greedy algorithm for optimization. By smoothing MVs while preserving motion accuracy, the proposed method can more efficiently regularize MVs than previous regularization methods. The experimental results show that the proposed method outperforms previous regularization methods by generating objectively and subjectively better results.