Motion Estimation by Quadtree Pruning and Merging

In this paper we propose a rate-distortion optimized motion estimation algorithm that is built upon a quadtree structure. Each node of the quadtree represents a block in the current frame together with its motion vector, and the block size decreases from the root to the leaves. In the first step, the quadtree is pruned according to a rate-distortion criterion in order to obtain blocks of variable sizes. A further rate rebate can be achieved by merging those leaf nodes of the quadtree that can be efficiently represented by the same motion vector. The proposed merging scheme provides a reduction of up to 50% of the rate spent for the motion model with respect to the case that performs pruning only