PROJECT TITLE :

Joint structure–texture sparse coding for quality prediction of stereoscopic images

ABSTRACT:

A quality prediction technique for stereoscopic pictures is proposed primarily 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 drawback. First, structure and texture dictionaries from a coaching 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 methodology can achieve high consistent alignment with subjective assessment.


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