Efficient Edit Propagation Using Hierarchical Data Structure

This paper presents a novel unified hierarchical structure for scalable edit propagation. Our method is based on the key observation that in edit propagation, appearance varies very smoothly in those regions where the appearance is different from the user-specified pixels. Uniformly sampling in these regions leads to redundant computation. We propose to use a quadtree-based adaptive subdivision method such that more samples are selected in similar regions and less in those that are different from the user-specified regions. As a result, both the computation and the memory requirement are significantly reduced. In edit propagation, an edge-preserving propagation function is first built, and the full solution for all the pixels can be computed by interpolating from the solution obtained from the adaptively subdivided domain. Furthermore, our approach can be easily extended to accelerate video edit propagation using an adaptive octree structure. In order to improve user interaction, we introduce several new Gaussian Mixture Model (GMM) brushes to find pixels that are similar to the user-specified regions. Compared with previous methods, our approach requires significantly less time and memory, while achieving visually same results. Experimental results demonstrate the efficiency and effectiveness of our approach on high-resolution photographs and videos.