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.
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.
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.
no code implementations • 3 Sep 2019 • Hilmi E. Egilmez, Yung-Hsuan Chao, Antonio Ortega
In many state-of-the-art compression systems, signal transformation is an integral part of the encoding and decoding process, where transforms provide compact representations for the signals of interest.
no code implementations • 16 Nov 2019 • Hilmi E. Egilmez, Oguzhan Teke, Amir Said, Vadim Seregin, Marta Karczewicz
In many video coding systems, separable transforms (such as two-dimensional DCT-2) have been used to code block residual signals obtained after prediction.
no code implementations • 27 Feb 2021 • Hilmi E. Egilmez, Ankitesh K. Singh, Muhammed Coban, Marta Karczewicz, Yinhao Zhu, Yang Yang, Amir Said, Taco S. Cohen
Most of the existing deep learning based end-to-end image/video coding (DLEC) architectures are designed for non-subsampled RGB color format.
no code implementations • 1 Apr 2021 • Ankitesh K. Singh, Hilmi E. Egilmez, Reza Pourreza, Muhammed Coban, Marta Karczewicz, Taco S. Cohen
Most of the existing deep learning based end-to-end video coding (DLEC) architectures are designed specifically for RGB color format, yet the video coding standards, including H. 264/AVC, H. 265/HEVC and H. 266/VVC developed over past few decades, have been designed primarily for YUV 4:2:0 format, where the chrominance (U and V) components are subsampled to achieve superior compression performances considering the human visual system.