Motion Estimation with Similarity Constraint and its Application to Distributed Video Coding

In this paper we present a new motion estimation scheme that minimizes the objective distance function with a constraint of similarity measure to exploit the motion correlation among adjacent pixel blocks with similar statistics features. We formulate this correlation as a similarity measure on the motion vectors between the current pixel block and its neighboring blocks weighted by the corresponding statistical similarity. We then use this similarity measure as a constraint in the objective distance function to reduce the noise effects and improve performance by effectively trading off the difference in the pixel values with the smoothness in the adjacent motion vectors. Thus, in motion estimation, our new scheme not only minimizes the pixel differences but tries to preserve the motion smoothness among statistically similar neighbors. We applied this new motion estimation scheme to a distributed video coding system for side information generation for Wyner-Ziv decoding and compared its performance to the scheme without similarity constraint. The results have shown that our motion estimation scheme can achieve significant gains in the fidelity of the side information and the decoded Wyner-Ziv frames over the scheme without the similarity constraint.