No-Reference Stereoscopic Image Quality Assessment by Both Complex Contourlet Domain with Spatial Domain Features

Abstract

Stereoscopic image quality assessment (SIQA) is an essential and tricky part of image processing. Recently, many scholars have conducted image quality assessment in either spatial or frequency domain respectively. In this paper, we propose a no-reference stereoscopic image quality evaluation method by considering both complex contourlet and spatial features. Firstly, the original views are converted to the Lab color space. Then the luminance channel of Lab color space and the synthetic cyclopean image are calculated from the original stereo pairs. Next, the perceptual features of these types of images are extracted in the complex contour domain and the spatial domain respectively. Finally, all pre-extracted features are sent to the regression model for training and predicting image quality scores. Our experimental results show that the proposed algorithm achieves high consistency with human subjective perception, and compete with state-of-the-art algorithms.