no code implementations • 26 Feb 2024 • Wen-Yang Lu, Eduardo Pavez, Antonio Ortega, Xin Zhao, Shan Liu
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 transforms (KLTs) decorrelate the intra-prediction residuals.
no code implementations • 18 Jan 2024 • Ryosuke Watanabe, Keisuke Nonaka, Eduardo Pavez, Tatsuya Kobayashi, Antonio Ortega
Second, we propose a fast noise level estimation method using eigenvalues of the covariance matrix on a graph.
no code implementations • 14 Dec 2023 • 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.
no code implementations • 15 Mar 2023 • Benjamin Girault, Eduardo Pavez, Antonio Ortega
In this paper, we explore the topic of graph learning from the perspective of the Irregularity-Aware Graph Fourier Transform, with the goal of learning the graph signal space inner product to better model data.
no code implementations • 11 Mar 2023 • Xin Xiong, Eduardo Pavez, Antonio Ortega, Balu Adsumilli
We proposed a geometric criterion for saturation detection that works with rate-distortion optimization, and only requires a few frames from the UGC video.
no code implementations • 3 Mar 2023 • Samuel Fernández-Menduiña, Eduardo Pavez, Antonio Ortega
Most codec designs rely on the mean squared error (MSE) as a fidelity metric in rate-distortion optimization, which allows to choose the optimal parameters in the transform domain but may fail to reflect perceptual quality.
no code implementations • 15 Oct 2022 • Haoran Hong, Eduardo Pavez, Antonio Ortega, Ryosuke Watanabe, Keisuke Nonaka
The scheme includes integer-precision motion estimation and an adaptive graph based in-loop filtering scheme for improved attribute prediction.
no code implementations • 7 Mar 2022 • Eduardo Pavez, Enrique Perez, Xin Xiong, Antonio Ortega, Balu Adsumilli
UGC video is uploaded by users, and then it is re-encoded to be made available at various levels of quality.
no code implementations • 6 Mar 2022 • Eduardo Pavez, Benjamin Girault, Antonio Ortega, Philip A. Chou
Our approach is based on novel graph Fourier transforms (GFTs) given by the generalized eigenvectors of the variation operator.
no code implementations • 1 Feb 2022 • Haoran Hong, Eduardo Pavez, Antonio Ortega, Ryosuke Watanabe, Keisuke Nonaka
Motivated by the success of fractional pixel motion in video coding, we explore the design of motion estimation with fractional-voxel resolution for compression of color attributes of dynamic 3D point clouds.
no code implementations • 31 Oct 2021 • Eduardo Pavez
We obtain a necessary and sufficient condition for existence of this estimator, that consists on checking whether a certain data dependent graph is connected.
no code implementations • 17 Sep 2021 • Tatsuya Koyakumaru, Masahiro Yukawa, Eduardo Pavez, Antonio Ortega
This paper presents a convex-analytic framework to learn sparse graphs from data.
no code implementations • 21 Jun 2021 • Shashank N. Sridhara, Eduardo Pavez, Antonio Ortega
We present an efficient voxelization method to encode the geometry and attributes of 3D point clouds obtained from autonomous vehicles.
no code implementations • 16 Jun 2021 • Eduardo Pavez, Andre L. Souto, Ricardo L. De Queiroz, Antonio Ortega
We propose an intra frame predictive strategy for compression of 3D point cloud attributes.
no code implementations • 23 Oct 2020 • Eduardo Pavez, Benjamin Girault, Antonio Ortega, Philip A. Chou
A major limitation is that this framework can only be applied to the normalized Laplacian of bipartite graphs.
1 code implementation • 4 Mar 2020 • Eduardo Pavez, Benjamin Girault, Antonio Ortega, Philip A. Chou
Since clusters may have a different numbers of points, each block transform must incorporate the relative importance of each coefficient.
no code implementations • 4 Apr 2018 • Eduardo Pavez, Antonio Ortega
We apply our analysis in an active learning framework, where the expected number of observed variables is small compared to the dimension of the vector of interest, and propose a design of optimal sub-sampling probabilities and an active covariance matrix estimation algorithm.
1 code implementation • 7 Mar 2018 • Hilmi E. Egilmez, Eduardo Pavez, Antonio Ortega
This paper introduces a novel graph signal processing framework for building graph-based models from classes of filtered signals.
1 code implementation • 31 May 2017 • Eduardo Pavez, Hilmi E. Egilmez, Antonio Ortega
Then, a graph weight estimation (GWE) step is performed by solving a generalized graph Laplacian estimation problem, where edges are constrained by the topology found in the GTI step.
2 code implementations • 16 Nov 2016 • Hilmi E. Egilmez, Eduardo Pavez, Antonio Ortega
For the proposed graph learning problems, specialized algorithms are developed by incorporating the graph Laplacian and structural constraints.