3 accepted papers
This paper introduces a novel framework for combining multiple weighted graphs into a single optimized weighted graph. In our framework, we first develop a statistical formulation for the graph combining problem with a maximum likelihood criterion, and derive its optimality conditions. We then use t…
In this paper, we propose a graph-based lifting transform for intra-predicted video sequences. The transform can approximate the performance of a Graph Fourier Transform (GFT) for a given graph, but does not require computing eigenvectors. A predict-update bipartition is designed based on a Gaussian…
We consider the motion-compensated temporal prediction loop at the heart of modern video coders. Rather than using motion-compensated reference frame blocks directly as predictors, we incorporate their spatially-filtered versions into the prediction loop. We design adaptive filters that are geared t…