Learning-based Satisfied User Ratio Prediction for Symmetrically and Asymmetrically Compressed Stereoscopic Images

dc.cclicenceCC-BY-NCen
dc.contributor.authorFan, Chunling
dc.contributor.authorZhang, Yun
dc.contributor.authorHamzaoui, Raouf
dc.contributor.authorZiou, Djemel
dc.contributor.authorJiang, Qingshan
dc.date.acceptance2021-02-08
dc.date.accessioned2021-02-18T16:00:09Z
dc.date.available2021-02-18T16:00:09Z
dc.date.issued2021
dc.descriptionThe file attached to this record is the author's final peer reviewed version.en
dc.description.abstractThe Satisfied User Ratio (SUR) for a given distortion level is the fraction of subjects that cannot perceive a quality difference between the original image and its compressed version. By predicting the SUR, one can determine the highest distortion level which allows to save bit rate while guaranteeing a good visual quality. We propose the first method to predict the SUR for symmetrically and asymmetrically compressed stereoscopic images. Unlike SUR prediction techniques for 2D images and videos, our method exploits the properties of binocular vision. We first extract features that characterize image quality and image content. Then, we use gradient boosting decision trees to reduce the number of features and train a regression model that learns a mapping function from the features to the SUR values. Experimental results on the SIAT-JSSI and SIAT-JASI datasets show high SUR prediction accuracy for H.265 All-Intra and JPEG2000 symmetrically and asymmetrically compressed stereoscopic images.en
dc.funderOther external funder (please detail below)en
dc.funder.otherNSFCen
dc.funder.otherGuangdong Provinceen
dc.funder.otherShenzhen Science and Technology Programen
dc.identifier.citationFan, C., Zhang, Y., Hamzaoui, R., Ziou, D., Jiang, Q. (2021) Learning-based satisfied user ratio prediction for symmetrically and asymmetrically compressed stereoscopic images. IEEE MultiMedia, in press.en
dc.identifier.doihttps://doi.org/10.1109/mmul.2021.3060831
dc.identifier.issn1070-986X
dc.identifier.urihttps://dora.dmu.ac.uk/handle/2086/20644
dc.language.isoen_USen
dc.peerreviewedYesen
dc.projectid61871372, 61902389 and 61901459; 2019B010137002 and 2018A0303130126; JCYJ20180507183823045, JCYJ20200109110410133, and JCYJ20170818163403748en
dc.publisherIEEEen
dc.researchinstituteInstitute of Engineering Sciences (IES)en
dc.subjectStereoscopic image quality assessmenten
dc.subjectSatisfied user ratioen
dc.subjectPicture-level just noticeable differenceen
dc.subjectSymmetric stereoscopic compressionen
dc.subjectAsymmetric stereoscopic compressionen
dc.titleLearning-based Satisfied User Ratio Prediction for Symmetrically and Asymmetrically Compressed Stereoscopic Imagesen
dc.typeArticleen

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