A fusion-based blind image quality metric for blurred stereoscopic images

Abstract

Blur is certainly one of the most encountered and the most annoying degradation types in image. It is due to several causes such as compression, motion, filtering and so on. In order to estimate the quality of this kind of degraded images, several metrics have been proposed in the literature. In this paper, we focus our attention on stereoscopic images and we propose a fusion-based blind stereoscopic image quality metric for blur degradation. In order to characterize the considered degradation type, some relevant features are first computed. Note that these features are extracted from a cyclopean image (CI) derived from the stereoscopic image. The final index quality is given by combined all features through a Support Vector Machine (SVM) model used as a regression tool. The 3D LIVE and the IEEE image databases have been used to evaluate our method. The achieved performance has been compared to the state-of-the-art.