Learning-based quality assessment of retargeted stereoscopic images

Abstract

Stereoscopic image retargeting techniques aim to flexibly display 3D images with different aspect ratios and simultaneously preserve salient regions and comfortable depth perception. Various stereoscopic image retargeting techniques have been proposed recently. However, there is still no effective objective metric for visual quality assessment of retargeted stereoscopic images. In this paper, we build a stereoscopic image retargeting database and propose a learning-based objective method to evaluate the stereoscopic image retargeting quality. The perception quality of the database are evaluated by subjects. We extract new features of quality assessment and fuse them to assess stereoscopic image retargeting quality using neural network. Experiments conducted with above-mentioned database confirm the effectiveness of the proposed method. The results show the good consistency between the objective assessments and subjective rankings.