55 citations · 147 across the 9 of their papers we have counts for
10 papers
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…
Low-Complexity Transform Adjustments For Video Coding
Amir Said, Hilmi E. Egilmez, Yung-Hsuan Chao
Recent video codecs with multiple separable transforms can achieve significant coding gains using asymmetric trigonometric transforms (DCTs and DSTs), because they can exploit dive…
Highly Efficient Non-Separable Transforms for Next Generation Video Coding
Amir Said, Xin Zhao, Marta Karczewicz +3
For the last few decades, the application of signal-adaptive transform coding to video compression has been stymied by the large computational complexity of matrix-based solutions.…
A Combined Deep Learning based End-to-End Video Coding Architecture for YUV Color Space
Ankitesh K. Singh, Hilmi E. Egilmez, Reza Pourreza +3
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.26…
Transform Network Architectures for Deep Learning based End-to-End Image/Video Coding in Subsampled Color Spaces
Hilmi E. Egilmez, Ankitesh K. Singh, Muhammed Coban +5
Most of the existing deep learning based end-to-end image/video coding (DLEC) architectures are designed for non-subsampled RGB color format. However, in order to achieve a superio…
Parametric Graph-based Separable Transforms for Video Coding
Hilmi E. Egilmez, Oguzhan Teke, Amir Said +2
In many video coding systems, separable transforms (such as two-dimensional DCT-2) have been used to code block residual signals obtained after prediction. This paper proposes a pa…
Graph-based Transforms for Video Coding
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 t…
Graph Learning from Filtered Signals: Graph System and Diffusion Kernel Identification
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. In our framework, graph-based modeling is formulat…
Learning Graphs with Monotone Topology Properties and Multiple Connected Components
Eduardo Pavez, Hilmi E. Egilmez, Antonio Ortega
Recent papers have formulated the problem of learning graphs from data as an inverse covariance estimation with graph Laplacian constraints. While such problems are convex, existin…
Graph Learning from Data under Structural and Laplacian Constraints
Hilmi E. Egilmez, Eduardo Pavez, Antonio Ortega
Graphs are fundamental mathematical structures used in various fields to represent data, signals and processes. In this paper, we propose a novel framework for learning/estimating…