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

Date

2021

Advisors

Journal Title

Journal ISSN

ISSN

1070-986X

Volume Title

Publisher

IEEE

Type

Article

Peer reviewed

Yes

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.

Description

The file attached to this record is the author's final peer reviewed version.

Keywords

Stereoscopic image quality assessment, Satisfied user ratio, Picture-level just noticeable difference, Symmetric stereoscopic compression, Asymmetric stereoscopic compression

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.

Rights

Research Institute

Institute of Engineering Sciences (IES)