Satisfied user ratio prediction with support vector regression for compressed stereo images

Date

2020-07

Advisors

Journal Title

Journal ISSN

ISSN

DOI

Volume Title

Publisher

IEEE

Type

Conference

Peer reviewed

Yes

Abstract

We propose the first method to predict the Satisfied User Ratio (SUR) for compressed stereo images. The method consists of two main steps. First, considering binocular vision properties, we extract three types of features from stereo images: image quality features, monocular visual features, and binocular visual features. Then, we train a Support Vector Regression (SVR) model to learn a mapping function from the feature space to the SUR values. Experimental results on the SIAT-JSSI dataset show excellent prediction accuracy, with a mean absolute SUR error of only 0.08 for H.265 intra coding and only 0.13 for JPEG2000 compression.

Description

Keywords

Satisfied user ratio, Picture-level just noticeable difference, Stereo images

Citation

Fan, C., Zhang, Y., Hamzaoui, R., Ziou, D., Jiang, Q. (2020) Satisfied user ratio prediction with support vector regression for compressed stereo images. IEEE International Conference on Multimedia & Expo Workshops (ICMEW), London, July 2020.

Rights

Research Institute

Institute of Engineering Sciences (IES)