Please use this identifier to cite or link to this item: https://open.uns.ac.rs/handle/123456789/6589
Title: Full-reference SSIM metric for video quality assessment with saliency-based features
Authors: Romani E.
da Silva W.
Fonseca K.
Ćulibrk, Dubravko 
Pohl A.
Issue Date: 1-Jan-2015
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract: © Springer International Publishing Switzerland 2015. This paper uses models of visual attention in order to estimate the human visual perception and thus improve metrics of Video Quality Assessment. This work reports on the use of the saliency based model in a fullreference structural similarity metric for creating new metrics that take into account regions that greatly attract the human attention. Correlation results with the differential mean opinion score values from the LIVE Video Quality Database are presented and discussed.
URI: https://open.uns.ac.rs/handle/123456789/6589
ISBN: 9783319232218
ISSN: 3029743
DOI: 10.1007/978-3-319-23222-5_66
Appears in Collections:FTN Publikacije/Publications

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