No-Reference Video quality assessment of H.264 video streams based on semantic saliency mapsReport as inadecuate




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1 LaBRI - Laboratoire Bordelais de Recherche en Informatique 2 Department of Communication Systems Engineering Be-er Sheva 3 Audemat - WorldCast Systems Group

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.

Keywords : Semantic saliency No-Reference Video Quality Assessment Saliency Maps H.264 HDTV





Author: Hugo Boujut - Jenny Benois-Pineau - Toufik Ahmed - Ofer Hadar - Patrick Bonnet -

Source: https://hal.archives-ouvertes.fr/



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