activity
20172026
most citedFast Graph Fourier Transforms Based on Graph Symmetry and Bipartition

39 citations · 59 across the 6 of their papers we have counts for

collaborators

6 papers

eess.IV2026

Transform and Entropy Coding in AV2

Alican Nalci, Hilmi E. Egilmez, Madhu P. Krishnan +21

AV2 is the successor to the AV1 video coding standard developed by the Alliance for Open Media (AOMedia). Its primary objective is to deliver substantial compression gains and subj…

eess.IV2025

Joint Optimization of Primary and Secondary Transforms Using Rate-Distortion Optimized Transform Design

Darukeesan Pakiyarajah, Eduardo Pavez, Antonio Ortega +4

Data-dependent transforms are increasingly being incorporated into next-generation video coding systems such as AVM, a codec under development by the Alliance for Open Media (AOM),…

eess.SP2021★ 12 cited

DCT and DST Filtering with Sparse Graph Operators

Keng-Shih Lu, Antonio Ortega, Debargha Mukherjee +1

Graph filtering is a fundamental tool in graph signal processing. Polynomial graph filters (PGFs), defined as polynomials of a fundamental graph operator, can be implemented in the…

eess.IV2020★ 3 cited

Perceptually inspired weighted MSE optimization using irregularity-aware graph Fourier transform

Keng-Shih Lu, Antonio Ortega, Debargha Mukherjee +1

In image and video coding applications, distortion has been traditionally measured using mean square error (MSE), which suggests the use of orthogonal transforms, such as the discr…

eess.SP2019★ 39 cited

Fast Graph Fourier Transforms Based on Graph Symmetry and Bipartition

Keng-Shih Lu, Antonio Ortega

The graph Fourier transform (GFT) is an important tool for graph signal processing, with applications ranging from graph-based image processing to spectral clustering. However, unl…

eess.SP2017★ 5 cited

Closed Form Solutions of Combinatorial Graph Laplacian Estimation under Acyclic Topology Constraints

Keng-Shih Lu, Antonio Ortega

How to obtain a graph from data samples is an important problem in graph signal processing. One way to formulate this graph learning problem is based on Gaussian maximum likelihood…