No-reference stereoscopic image quality assessment on both complex contourlet and spatial domain via Kernel ELM

Abstract

Stereoscopic imaging is widely used in many fields. To guarantee the best quality of experience, it is necessary to design a robust and accurate quality assessment model for stereoscopic content. In this paper, we proposed a no-reference stereoscopic image quality assessment (NR-SIQA) model using both complex contourlet and spatial domain features of monocular and binocular images. Monocular features extracted from the CIELAB color space are exploited to characterize quality degradation, including the across-scale and across-orientation correlation in complex contourlet domain and natural scene statistics-based (NSS) features in spatial domain. Then, the binocular features consist of energy along with energy difference and structural correlation in complex contourlet domain, and statistics distribution in spatial domain extracted from the synthesized cyclopean image and the 3D visual discomfort measure based on the statistics of disparity map. Finally, the above features are mapped into the predicted quality scores by the Kernel ELM (KELM) regression model. Experimental results on four public datasets show that the proposed model is highly consistent with human subjective perception in terms of accuracy and generalization.