No-Reference video quality assessment of H.264 video streams based on semantic saliency maps

H. Boujut, J. Benois-Pineau, T. Ahmed, O. Hadar, P. Bonnet

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Abstract

    The paper contributes to No-Reference video quality assessment of broadcasted HD video over IP networks and DVB. In this work we have enhanced our bottom-up spatio-temporal saliency map model by considering semantics of the visual scene. Thus we propose a new saliency map model based on face detection that we called semantic saliency map. A new fusion method has been proposed to merge the bottom-up saliency maps with the semantic saliency map. We show that our NR metric WMBER weighted by the spatio-temporal-semantic saliency map provides higher results then the WMBER weighted by the bottom-up spatio-temporal saliency map. Tests are performed on two H.264/AVC video databases for video quality assessment over lossy networks.

    Original languageAmerican English
    Title of host publicationProceedings of SPIE-IS and T Electronic Imaging - Image Quality and System Performance IX
    DOIs
    StatePublished - 13 Feb 2012
    EventImage Quality and System Performance IX - Burlingame, CA, United States
    Duration: 24 Jan 201226 Jan 2012

    Publication series

    NameProceedings of SPIE - The International Society for Optical Engineering
    Volume8293

    Conference

    ConferenceImage Quality and System Performance IX
    Country/TerritoryUnited States
    CityBurlingame, CA
    Period24/01/1226/01/12

    All Science Journal Classification (ASJC) codes

    • Electronic, Optical and Magnetic Materials
    • Condensed Matter Physics
    • Computer Science Applications
    • Applied Mathematics
    • Electrical and Electronic Engineering

    Fingerprint

    Dive into the research topics of 'No-Reference video quality assessment of H.264 video streams based on semantic saliency maps'. Together they form a unique fingerprint.

    Cite this