Joint structure–texture sparse coding for quality prediction of stereoscopic images
Kemeng Li, Feng Shao, Gangyi Jiang, Mei Yu · Electronics Letters · 2015
A quality prediction method for stereoscopic images is proposed based on joint structure–texture sparse coding. The goal is to predict the perceptual quality of a stereoscopic image by solving the joint structure–texture sparse coding problem. First, structure and texture dictionaries from a training database are learnt. Then, the quality score for a testing stereoscopic image is predicted by computing left and right sparse feature similarity indexes, respectively, and combining them together. Experimental results on two 3D image‐quality assessment databases demonstrate that the proposed method can achieve high consistent alignment with subjective assessment.