Hierarchical and Polynomial Motion Modeling with Quad-Tree Leaf Merging

Recent work into quad-tree representations has commented on the sub-optimal performance of quad-trees due to not exploiting the dependency between neighboring leaf nodes with different parents. Leaf merging therefore has been proposed to rectify this performance loss. In the context of quad-tree motion models, the performance of leaf merging, was recently demonstrated in which an optimally pruned H.264 motion model was taken as the starting point for subsequent merging steps. In this paper we explore the performance of leaf merging over a wider range of conditions by starting with a general tree structure where each node is allowed to represent motion using either polynomial models or a single translational vector. We also explore two cases of motion prediction methods, these being spatial prediction and hierarchical prediction. Experimental results show the benefit of merging across these broad conditions. In comparison to the merged H.264 model reported in the previous work, substantial gains are evident. Furthermore we explore applying the merging principles to branch nodes of a quad-tree to achieve efficient hierarchical motion representation.