Abstract:

To assess various distorted stereoscopic image quality efficiently, we propose a no reference stereoscopic image quality assessment model that utilizes 3D map information and 2D texture information to drive the multiple kernel learning machine (MKL). Firstly, the model utilizes the stereoscopic matching model to obtain disparity map and disparity map of error energy on the basis of left view and right view. Secondly, left view, right view, disparity map and disparity map of error energy are all transformed by phase congruency and structure tensor to obtain their marginal zone and planar zone. Thirdly, the model extracts the texture feature of the two zones from left view and right view respectively as plane information, and it extracts statistics feature and texture feature of the two zones respectively from disparity map and disparity map of error energy as 3D information. Finally, all features are input to the MKL to predict the quality of tested images. Experiments on the LIVE 3D image quality database demonstrate that the proposed method has good consistency with human subject quality and has high competitiveness in comparison with the state-of-the-art models.

Key words: stereoscopic image quality assessment, universal no-reference, multiple kernel learning, image texture