Perceptual objective quality assessment of stereoscopic stitched images

Abstract Large view stereoscopic images can provide users with immersive depth experience. Image stitching techniques aim to obtain large view stitched images, and there have been various image stitching algorithms proposed recently. However, there is still no effective objective quality assessment for stereoscopic stitched images. In this paper, we propose a new perceptual objective stereoscopic stitched image quality assessment (S-SIQA) method by considering different distortion types in the existing stitching methods, including color distortion, ghost distortion, structure distortion(shape distortion, information loss), and disparity distortion. The quality evaluation methods for these distortion types are designed by using the color difference coefficient, points distance, matched line inclination degree, information loss, and disparity difference. Then we fuse these measures in the proposed S-SIQA model by an optimally weighted linear combination. In addition, to evaluate the performance of the proposed S-SIQA, we build a subjective quality assessment database for stereoscopic stitched images. Experimental results have confirmed the proposed method can effectively measure the perceptual quality of stereoscopic stitched images.