55 citations · 125 across the 37 of their papers we have counts for
11 papers · 1 filter
Towards joint graph learning and sampling set selection from data
Shashank N. Sridhara, Eduardo Pavez, Antonio Ortega
We explore the problem of sampling graph signals in scenarios where the graph structure is not predefined and must be inferred from data. In this scenario, existing approaches rely…
Color-Guided Flying Pixel Correction in Depth Images
Ekamresh Vasudevan, Shashank N. Sridhara, Eduardo Pavez +3
We present a novel method to correct flying pixels within data captured by Time-of-flight (ToF) sensors. Flying pixel (FP) artifacts occur when signals from foreground and backgrou…
Graph-based Scalable Sampling of 3D Point Cloud Attributes
Shashank N. Sridhara, Eduardo Pavez, Ajinkya Jayawant +3
3D Point clouds (PCs) are commonly used to represent 3D scenes. They can have millions of points, making subsequent downstream tasks such as compression and streaming computational…
Graph-Based Signal Sampling with Adaptive Subspace Reconstruction for Spatially-Irregular Sensor Data
Darukeesan Pakiyarajah, Eduardo Pavez, Antonio Ortega
Choosing an appropriate frequency definition and norm is critical in graph signal sampling and reconstruction. Most previous works define frequencies based on the spectral properti…
Fast DCT+: A Family of Fast Transforms Based on Rank-One Updates of the Path Graph
Samuel Fernández-Menduiña, Eduardo Pavez, Antonio Ortega
This paper develops fast graph Fourier transform (GFT) algorithms with O(n log n) runtime complexity for rank-one updates of the path graph. We first show that several commonly-use…
Feature-Preserving Rate-Distortion Optimization in Image Coding for Machines
Samuel Fernández Menduiña, Eduardo Pavez, Antonio Ortega
With the increasing number of images and videos consumed by computer vision algorithms, compression methods are evolving to consider both perceptual quality and performance in down…
Full reference point cloud quality assessment using support vector regression
Ryosuke Watanabe, Shashank N. Sridhara, Haoran Hong +4
Point clouds are a general format for representing realistic 3D objects in diverse 3D applications. Since point clouds have large data sizes, developing efficient point cloud compr…
Full-reference Point Cloud Quality Assessment Using Spectral Graph Wavelets
Ryosuke Watanabe, Keisuke Nonaka, Eduardo Pavez +2
Point clouds in 3D applications frequently experience quality degradation during processing, e.g., scanning and compression. Reliable point cloud quality assessment (PCQA) is impor…
Adaptive Online Learning of Separable Path Graph Transforms for Intra-prediction
Wen-Yang Lu, Eduardo Pavez, Antonio Ortega +2
Current video coding standards, including H.264/AVC, HEVC, and VVC, employ discrete cosine transform (DCT), discrete sine transform (DST), and secondary to Karhunen-Loeve transform…
Irregularity-Aware Bandlimited Approximation for Graph Signal Interpolation
Darukeesan Pakiyarajah, Eduardo Pavez, Antonio Ortega
In most work to date, graph signal sampling and reconstruction algorithms are intrinsically tied to graph properties, assuming bandlimitedness and optimal sampling set choices. How…
Fast graph-based denoising for point cloud color information
Ryosuke Watanabe, Keisuke Nonaka, Eduardo Pavez +2
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, re…