Hilmi E. Egilmez

Qualcomm · Multimedia R&D and Standards

This chapter presents methods for building graph Fourier transforms (GFTs) for image and video compression. A key insight is that classical transforms, such as the discrete sine/cosine transform (DCT) or the Karhunen–Loeve transform (KLT), can be interpreted from a graph perspective. The chapter considers two sets of techniques for designing graphs...

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 superior coding performance, many state-of-the-art block-based compression standards such as High Efficiency Video Coding (HEVC/H.265) and Versatile Video Coding (VVC/H.266) are...

In the past decade, the development of transform coding techniques has achieved significant progress and several advanced transform tools have been adopted in the new generation Versatile Video Coding (VVC) standard. In this paper, a brief history of transform coding development during VVC standardization is presented, and the transform coding tool...

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 subsamp...

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 superior coding performance, many state-of-the-art block-based compression standards such as High Efficiency Video Coding (HEVC/H.265) and Versatile Video Coding (VVC/H.266) are...

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 parametric approach to build graph-based separable transforms (GBSTs) for video coding. Specifically, a GBST is derived from a pair of line graphs, whose weights are determ...

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. This paper introduces a class of transforms called graph-based transforms (GBTs) for video compression, and proposes two different techniques to d...

This article describes the main video coding technologies included in a joint proposal submitted by Qualcomm and Technicolor, in response to a Call for Proposals (CfP) issued by ITU-T SG16 WP3 Q.6 (VCEG) and ISO/IEC JTC1/SC29/WG11 (MPEG) in Oct. 2017. The proposal contains the majority of the tools that have been adopted into the Joint Exploration...

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 parametric approach to build graph-based separable transforms (GBSTs) for video coding. Specifically, a GBST is derived from a pair of line graphs, whose weights are determ...

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. This paper introduces a class of transforms called graph-based transforms (GBTs) for video compression, and proposes two different techniques to d...

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 formulated as a graph system identification problem, where the goal is to learn a weighted graph (a graph Laplacian matrix) and a graph-based filter (a function of graph Laplacia...

Learning graphs with topology properties is a non-convex optimization problem. We propose a tractable algorithm that finds the generalized Laplacian matrix of a graph with the desired type of topology. Given the covariance/similarity matrix, our algorithm first solves a combinatorial optimization problem to find a graph topology that satisfies the...

Learning graphs with structural properties is in general a non-convex optimization problem. We consider graph families closed under edge removal operations, and graphs with multiple connected components. We propose a tractable algorithm that finds the generalized Laplacian matrix of a graph with the desired type of structure. Our algorithm has two...

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 graphs from data. The proposed framework includes (i) formulation of various graph learning problems, (ii) their probabilistic interpretations and (iii) efficient algorit...

The graph Fourier transform (GFT) — adaptive to the signal structures of local pixel blocks — has recently been shown to be a good alternative to fixed transforms, e.g., the Discrete Cosine Transform (DCT), for image coding. However, the majority of proposed GFTs assume an underlying 4-connected graph structure with vertical and horizontal edges on...

In video coding, motion compensation is an essential tool to obtain residual block signals whose transform coefficients are encoded. This paper proposes novel graph-based transforms (GBTs) for coding inter-predicted residual block signals. Our contribution is twofold: (i) We develop edge adaptive GBTs (EA-GBTs) derived from graphs estimated from re...

The Karhunen-Loeve transform (KLT) is known to be optimal for decorrelating stationary Gaussian processes, and it provides effective transform coding of images. Although the KLT allows efficient representations for such signals, the transform itself is completely data-driven and computationally complex. This paper proposes a new class of transforms...

Spectrum sensing is an essential functionality of cognitive radio wireless networks (CRWNs) that enables detecting unused frequency sub-bands for dynamic spectrum access. This paper proposes a compressed spectrum sensing framework by (i) constructing a sparsity basis in wavelet domain that helps compressed sensing at sub-Nyquist rates and (ii) appl...

This paper presents novel QoS extensions to distributed control plane architectures for multimedia delivery over large-scale, multi-operator Software Defined Networks (SDNs). We foresee that large-scale SDNs shall be managed by a distributed control plane consisting of multiple controllers, where each controller performs optimal QoS routing within...

This paper introduces a novel spectral anomaly detection method by developing a graph-based filtering framework. In particular, we consider the problem of unsupervised data anomaly detection over wireless sensor networks (WSNs) where sensor measurements are represented as signals on a graph. In our framework, graphs are chosen to capture useful pro...

This demo abstract describes an initial design of a new adaptive video streaming protocol for device-to-device WiFi-based mobile platforms and its software implementation. For the demonstration, two mobile servers and two mobile users will be deployed verifying that our device-to-device adaptive video streaming implementation works with desirable u...

OpenFlow, which has recently been deployed worldwide for research purposes, is a programmable network protocol and associated hardware designed to effectively manage and direct traffic by decoupling control and forwarding layers of routing. This paper presents an analytical framework for optimization of forwarding decisions at the control layer to...

This paper proposes a new Quality of Service (QoS) optimized routing architecture for video streaming over large-scale multi-domain OpenFlow networks managed by a distributed control plane, where each controller performs optimal routing within its domain and shares summarized intra-domain routing data with other controllers to reduce problem dimens...

OpenFlow is a Software Defined Networking (SDN) paradigm that decouples control and data forwarding layers of routing. In this paper, we propose OpenQoS, which is a novel OpenFlow controller design for multimedia delivery with end-to-end Quality of Service (QoS) support. Our approach is based on QoS routing where the routes of multimedia traffic ar...

OpenFlow is a clean-slate Future Internet architecture that decouples control and forwarding layers of routing, which has recently started being deployed throughout the world for research purposes. This paper presents an optimization framework for the OpenFlow controller in order to provide QoS support for scalable video streaming over an OpenFlow...