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Book ChapterDOI

EvalVid – A Framework for Video Transmission and Quality Evaluation

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TLDR
EvalVid is targeted for researchers who want to evaluate their network designs or setups in terms of user perceived video quality, and has a modular construction, making it possible to exchange both the network and the codec.
Abstract
With EvalVid we present a complete framework and tool-set for evaluation of the quality of video transmitted over a real or simulated communication network. Besides measuring QoS parameters of the underlying network, like loss rates, delays, and jitter, we support also a subjective video quality evaluation of the received video based on the frame-by-frame PSNR calculation. The tool-set has a modular construction, making it possible to exchange both the network and the codec. We present here its application for MPEG-4 as example. EvalVid is targeted for researchers who want to evaluate their network designs or setups in terms of user perceived video quality. The tool-set is publicly available [11].

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Journal ArticleDOI

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

TL;DR: This paper presents a comprehensive literature review on applications of deep reinforcement learning (DRL) in communications and networking, and presents applications of DRL for traffic routing, resource sharing, and data collection.
Proceedings ArticleDOI

ATLAS: A scalable and high-performance scheduling algorithm for multiple memory controllers

TL;DR: It is shown that the implementation of least-attained-service thread prioritization reduces the time the cores spend stalling and significantly improves system throughput, and ATLAS's performance benefit increases as the number of cores increases.
Posted Content

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

TL;DR: In this paper, a comprehensive literature review on applications of deep reinforcement learning in communications and networking is presented, which includes dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation.
Journal ArticleDOI

An Evaluation Framework for More Realistic Simulations of MPEG Video Transmission

TL;DR: The results show that the fraction of decodable frames reflects well the behavior of the PSNR metric, while being less time-consuming, and can be an alternative metric to objectively assess through simulations the delivery quality of transmission in a network of publicly available video trace files.
Journal ArticleDOI

Video Transport Evaluation With H.264 Video Traces

TL;DR: This tutorial introduces a trace-based evaluation methodology for the network transport of H.264 encoded video and gives an overview of the typical video traffic and quality characteristics ofH.264 encode video.
References
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Journal ArticleDOI

Overview of fine granularity scalability in MPEG-4 video standard

TL;DR: An overview of the FGS video coding technique is provided in this Amendment of the MPEG-4 to address a variety of challenging problems in delivering video over the Internet.
Journal ArticleDOI

On end-to-end architecture for transporting MPEG-4 video over the Internet

TL;DR: Simulation results show that the end-to-end transport architecture achieves good perceptual picture quality for MPEG-4 video under low bit-rate and varying network conditions and efficiently utilizes network resources.

Video Quality Measurement Techniques

Stephen Wolf
TL;DR: The goal of this report is to provide a complete description of the ITS video quality metric (VQM) algorithms and techniques, which provide close approximations to the overall quality impressions, or mean opinion scores, of digital video impairments that have been graded by panels of viewers.
Journal ArticleDOI

Delay reduction techniques for playout buffering

TL;DR: The Concord algorithm is presented, which provides a delay-sensitive solution for playout buffering and the use of aging techniques are explored to improve the effectiveness of the historical information and hence, the delay predictions.
Proceedings ArticleDOI

A human vision system model for objective picture quality measurements

TL;DR: The Sarnoff just-noticeable difference (JND) model is described, which is based on known physiological and psychophysical principles of human visual discrimination performance, rather than on particular classes of image distortions, and is therefore more robust across the often unpredictable range of distortions that can occur in modern digital video.
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