Quadtree-structured variable-size block-matching motion estimation with minimal error

This paper reports two efficient quadtree-based algorithms for variable-size block matching (VSBM) motion estimation. The schemes allow the dimensions of blocks to adapt to local activity within the image, and the total number of blocks in any frame can be varied while still accurately representing true motion. This permits adaptive bit allocation between the representation of displacement and residual data, and also the variation of the overall bit-rate on a frame-by-frame basis. The first algorithm computes the optimal selection of variable-sized blocks to provide the best-achievable prediction error under the fixed number of blocks for a quadtree-based VSBM technique. The algorithm employs an efficient dynamic programming technique utilizing the special structure of a quadtree. Although this algorithm is computationally intensive, it does provide a yardstick by which the performance of other more practical VSBM techniques can be measured. The second algorithm adopts a heuristic way to select variable-sized square blocks. It relies more on local motion information than on global error optimization. Experiments suggest that the effective use of local information contributes to minimizing the overall error. The result is a more computationally efficient VSBM technique than the optimal algorithm, but with a comparable prediction error