PROJECT TITLE :
Joint structure–texture sparse coding for quality prediction of stereoscopic images
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.
Did you like this research project?
To get this research project Guidelines, Training and Code... Click Here