Blind quality algorithm to analyze streaming video contents in 5g networks

López Velasco, Juan Pedro and Authier, Anton and Jiménez Bermejo, David and Menéndez García, José Manuel and Sánchez Almodóvar, Nuria (2017). Blind quality algorithm to analyze streaming video contents in 5g networks. In: "International Conferences WWW / Internet 2017 and Applied Computing 2017", 18 – 20 October 2017, Algarve, Portugal.

Description

Title: Blind quality algorithm to analyze streaming video contents in 5g networks
Author/s:
  • López Velasco, Juan Pedro
  • Authier, Anton
  • Jiménez Bermejo, David
  • Menéndez García, José Manuel
  • Sánchez Almodóvar, Nuria
Item Type: Presentation at Congress or Conference (Article)
Event Title: International Conferences WWW / Internet 2017 and Applied Computing 2017
Event Dates: 18 – 20 October 2017
Event Location: Algarve, Portugal
Title of Book: International Conferences WWW / Internet 2017 and Applied Computing 2017
Date: 20 October 2017
Subjects:
Freetext Keywords: Streaming, 5G networks, Video Quality, Artefacts, Metrics, QoE
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
UPM's Research Group: Aplicación de Telecomunicaciones Visuales G@TV
Creative Commons Licenses: Recognition

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (593kB) | Preview

Abstract

Internet traffic is growing by an average of 40% a year and video streaming stands for 50% of the traffic. The expansion of new applications and services for video distribution makes users demand video quality and faster loads for contents with higher resolutions. For that reason, it is necessary to improve detection systems with check the received contents and insure comfortable use. The wide variety of technologies for streaming and encoding systems make necessary the implementation of blind quality models that detect errors independently of the transmission source, focused on analyzing the decoded image as consumed by the final user. Additionally, the absence of a video reference in streaming systems oblige the creation of No-Reference quality metrics, because the content is not available to compare the video received with the original source. For that reason, this paper proposes a novel algorithm based on the detection transmission artefacts using visual VQA techniques in order to improve users' Quality of Experience (QoE). The algorithm is based on the detection of main streaming visual errors and artefacts, such as color degradation, frozen frames, complete absence of video or packet losses. The algorithm is included in a quality probe application that is part of a 5G transmission network system for streaming high quality video. The algorithm analyzes the video quality for detecting streaming errors and the application warns the broadcaster for the retransmission of content if necessary. For the evaluation of the algorithm, a database of videos containing different types of errors was used in order to simulate streaming conditions and approach reality. The development of this application is necessary for the introduction of next generation 5G telecommunications networks and the consequent new paradigm of video broadcasting.

Funding Projects

TypeCodeAcronymLeaderTitle
Horizon 20206715985G-CrosshaulUniversidad Carlos IIIFuture Internet: Software, hardware, Infrastructures, technologies and services

More information

Item ID: 48650
DC Identifier: http://oa.upm.es/48650/
OAI Identifier: oai:oa.upm.es:48650
Deposited by: Juan Pedro López Velasco
Deposited on: 01 Dec 2017 10:13
Last Modified: 01 Dec 2017 10:13
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM