Download files
Abstract
Motion estimation has many applications in visual media processing. Most estimation schemes require a matching metric. One popular metric is mean square error in the image domain. In this case, information from different frequency bands and orientations are mixed together and treated the same way in the metric. Other metrics, such as Hadamard SAD, utilise decomposed image information as is, without further enhancement.
This thesis proposes a series of matching metrics involving a split-enhance-fuse approach. The information in a video frame is first split into different frequency and/or orientation bands; each band is enhanced and matched independently. For each pixel location, scores calculated in detail bands are fused by summing across all scales and/or orientations. This diversifies the treatment of information in images. Several metrics of this type are analysed and compared to show that such diversity generates more accurate motion field.
The decomposition consists of either a Laplacian pyramid or a directional pyramid. The enhancement involves adaptive thresholding and morphologically dilation. The thesis analyses these non-linear transforms and shows how the utility of high frequency bands can be improved for motion estimation, especially when motion is non-translational or when there is more than one motion model in a matching window, i.e., the window contains motion discontinuities. Mutual information inspired scores are calculated on enhanced bands, which is especially useful in the fusion process. An efficient implementation of the proposed method is described. The computational complexity is closely related to that of the disjoint block-based motion estimation by matching MSE. Finally the effects of edge preserving aggregation filters are investigated. A new edge-aware moving average is also proposed.