Learning-based Satisfied User Ratio Prediction for Symmetrically and Asymmetrically Compressed Stereoscopic Images
dc.cclicence | CC-BY-NC | en |
dc.contributor.author | Fan, Chunling | |
dc.contributor.author | Zhang, Yun | |
dc.contributor.author | Hamzaoui, Raouf | |
dc.contributor.author | Ziou, Djemel | |
dc.contributor.author | Jiang, Qingshan | |
dc.date.acceptance | 2021-02-08 | |
dc.date.accessioned | 2021-02-18T16:00:09Z | |
dc.date.available | 2021-02-18T16:00:09Z | |
dc.date.issued | 2021 | |
dc.description | The file attached to this record is the author's final peer reviewed version. | en |
dc.description.abstract | The 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.funder | Other external funder (please detail below) | en |
dc.funder.other | NSFC | en |
dc.funder.other | Guangdong Province | en |
dc.funder.other | Shenzhen Science and Technology Program | en |
dc.identifier.citation | Fan, 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.doi | https://doi.org/10.1109/mmul.2021.3060831 | |
dc.identifier.issn | 1070-986X | |
dc.identifier.uri | https://dora.dmu.ac.uk/handle/2086/20644 | |
dc.language.iso | en_US | en |
dc.peerreviewed | Yes | en |
dc.projectid | 61871372, 61902389 and 61901459; 2019B010137002 and 2018A0303130126; JCYJ20180507183823045, JCYJ20200109110410133, and JCYJ20170818163403748 | en |
dc.publisher | IEEE | en |
dc.researchinstitute | Institute of Engineering Sciences (IES) | en |
dc.subject | Stereoscopic image quality assessment | en |
dc.subject | Satisfied user ratio | en |
dc.subject | Picture-level just noticeable difference | en |
dc.subject | Symmetric stereoscopic compression | en |
dc.subject | Asymmetric stereoscopic compression | en |
dc.title | Learning-based Satisfied User Ratio Prediction for Symmetrically and Asymmetrically Compressed Stereoscopic Images | en |
dc.type | Article | en |
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