Search Results for author: Hilmi E. Egilmez

Found 7 papers, 3 papers with code

A Combined Deep Learning based End-to-End Video Coding Architecture for YUV Color Space

no code implementations1 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.

Transform Network Architectures for Deep Learning based End-to-End Image/Video Coding in Subsampled Color Spaces

no code implementations27 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.

Parametric Graph-based Separable Transforms for Video Coding

no code implementations16 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.

Graph-based Transforms for Video Coding

no code implementations3 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.

Video Compression

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

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.

Graph Learning

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