Search Results for author: Eduardo Pavez

Found 20 papers, 4 papers with code

Adaptive Online Learning of Separable Path Graph Transforms for Intra-prediction

no code implementations26 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.

Fast graph-based denoising for point cloud color information

no code implementations18 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.

Denoising graph construction

Irregularity-Aware Bandlimited Approximation for Graph Signal Interpolation

no code implementations14 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.

Joint Graph and Vertex Importance Learning

no code implementations15 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.

Graph Learning

Rate-Distortion Optimization With Alternative References For UGC Video Compression

no code implementations11 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.

Video Compression

Image Coding via Perceptually Inspired Graph Learning

no code implementations3 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.

Graph Learning MS-SSIM +1

Motion estimation and filtered prediction for dynamic point cloud attribute compression

no code implementations15 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.

Attribute Motion Estimation

Compression of user generated content using denoised references

no code implementations7 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.

Denoising

Two Channel Filter Banks on Arbitrary Graphs with Positive Semi Definite Variation Operators

no code implementations6 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.

Fractional Motion Estimation for Point Cloud Compression

no code implementations1 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.

Motion Compensation Motion Estimation

Laplacian Constrained Precision Matrix Estimation: Existence and High Dimensional Consistency

no code implementations31 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.

Vocal Bursts Intensity Prediction

Cylindrical coordinates for LiDAR point cloud compression

no code implementations21 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.

Attribute Autonomous Vehicles

Multi-resolution intra-predictive coding of 3D point cloud attributes

no code implementations16 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.

Region adaptive graph fourier transform for 3d point clouds

1 code implementation4 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.

Active covariance estimation by random sub-sampling of variables

no code implementations4 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.

Active Learning

Graph Learning from Filtered Signals: Graph System and Diffusion Kernel Identification

1 code implementation7 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.

Graph Learning valid

Learning Graphs with Monotone Topology Properties and Multiple Connected Components

1 code implementation31 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.

Graph Learning from Data under Structural and Laplacian Constraints

2 code implementations16 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.

Computational Efficiency Graph Learning

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