20 accepted papers
Despite their theoretical advantages, spectral methods based on the graph Fourier transform (GFT) are seldom used in graph neural networks (GNNs) due to the cost of computing the eigenbasis and the lack of vertex-domain locality in spectral representations. As a result, most GNNs rely on local appro…
Feature coding for machines (FCM) is a lossy compression paradigm for split-inference. The transmitter encodes the outputs of the first part of a neural network before sending them to the receiver for completing the inference. Practical FCM methods ``sandwich'' a traditional codec between pre- and p…
This paper develops fast graph Fourier transform (GFT) algorithms with O(nlogn) runtime complexity for rank-one updates of the path graph. We first show that several commonly-used audio and video coding transforms belong to this class of GFTs, which we denote by DCT+. Next, starting from an arbitrar…
Choosing an appropriate frequency definition and norm is critical in graph signal sampling and reconstruction. Most previous works define frequencies based on the spectral properties of the graph and use the same frequency definition and ℓ<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xli…
In real-time applications utilizing point clouds, no-reference point cloud quality assessment (NR-PCQA) methods are essential to improve the accuracy of downstream tasks. For example, in point cloud denoising, NR-PCQA results can be benchmarks for determining the optimal parameters when reference da…
Point clouds are utilized in various 3D applications such as cross-reality (XR) and realistic 3D displays. In some applications, e.g., for live streaming using a 3D point cloud, real-time point cloud denoising methods are required to enhance the visual quality. However, conventional high-precision d…
In most work to date, graph signal sampling and reconstruction algorithms are intrinsically tied to graph properties, assuming bandlimitedness and optimal sampling set choices. However, practical scenarios often defy these assumptions, leading to suboptimal performance. In the context of sampling an…
Point cloud applications suffer from geometric noise caused by measurement errors induced by the point cloud acquisition system. We propose a novel graph construction method, surface-consistent non-negative kernel regression (SC-NNK), that can achieve more accurate denoising of geometry information…
Point clouds are utilized in many 3-D applications such as cross-reality (XR) and realistic 3-D display. They consist of a set of points with 3-D coordinates and associated color signals. These color signals are often perturbed by noise induced by the measurement errors of scanning devices. In this…
User generated content (UGC) refers to videos that are uploaded by users and shared over the Internet. UGC may have low quality due to noise and previous compression. When re-encoding UGC for streaming or downloading, a traditional video coding pipeline will perform rate-distortion (RD) optimization…
We propose a novel graph-based denoising method to correct the quantization error (step noise) arising in the process of generating the visual hull, a commonly used technique to synthesize free-viewpoint video. To reduce this step noise effectively, we propose two new notions of consistency, pixel v…
This paper considers the problem of estimating high dimensional Laplacian constrained precision matrices by minimizing Stein’s loss. We obtain a necessary and sufficient condition for existence of this estimator, that consists on checking whether a certain data dependent graph is connected. We also…
We introduce chroma subsampling for 3D point cloud attribute compression by proposing a novel technique to sample points irregularly placed in 3D space. While most current video compression standards use chroma subsampling, these chroma subsampling methods cannot be directly applied to 3D point clou…
Many applications that use point clouds, such as 3D immersive telepresence, suffer from geometric quality degradation. This noise may be caused by measurement errors of the capturing device or by the point cloud estimation method. In this paper, we propose a novel graph-based point cloud denoising a…
This paper presents a graph learning framework to produce sparse and accurate graphs from network data. While our formulation is inspired by the graphical lasso, a key difference is the use of a nonconvex alternative of the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://ww…
Biorthogonal graph wavelet filterbanks, also known as GraphBior, are one of the most popular graph transforms used in image compression, but up to now, they could be designed based on two known admissible fundamental matrices: i) the random walk Laplacian, which heavily penalizes low degree pixels,…
In the past decade, several multi-resolution representation theories for graph signals have been proposed. Bipartite filter-banks stand out as the most natural extension of time domain filter-banks, in part because perfect reconstruction, orthogonality and bi-orthogonality conditions in the graph sp…
We study covariance matrix estimation for the case of partially observed random vectors, where different samples contain different subsets of vector coordinates. Each observation is the product of the variable of interest with a 0 - 1 Bernoulli random variable. We analyze an unbiased covariance esti…
We introduce a compressible representation of 3D geometry (including its attributes, such as color texture) intermediate between polygonal meshes and point clouds called a polygon cloud. Polygon clouds, compared to polygonal meshes, are more robust to live capture noise and artifacts. Furthermore, d…
Graph signal processing models high dimensional data as functions on the vertices of a graph. This theory is constructed upon the interpretation of the eigenvectors of the Laplacian matrix as the Fourier transform for graph signals. We formulate the graph learning problem as a precision matrix estim…