Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals
that live naturally on irregular data kernels described by graphs (eg, social networks …

Compressing attributes on 3D point clouds such as colors or normal directions has been a
challenging problem, since these attribute signals are unstructured. In this paper, we …

Block-based compression tends to be inefficient when blocks contain arbitrary shaped
discontinuities. Recently, graph-based approaches have been proposed to address this …

Graph is a data structure that can represent complex relationships among data. Graph signal
processing, unlike traditional signal processing, explicitly considers the structure and …

Current formulations of critically-sampled graph wavelet filterbanks work only for bipartite
graphs where downsampling signals on either partition leads to a spectrum folding …

In this work, we propose a new method of graph construction for graph-based image
compression. In particular, because of the overhead incurred by graph transmission to the …

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 …

Multi-view video transmission poses great challenges because of its data size and
dimension. Therefore, how to design efficient 3D scene representations and coding (of …

Graph-based models have recently attracted attention for their potential to enhance
transform coding image compression thanks to their capability to efficiently represent …