Full-Reference Quality Assessment of Stereoscopic Images by Learning Binocular Receptive Field Properties - 2015


Quality assessment of 3D pictures encounters additional challenges than its 2D counterparts. Directly applying 2D image quality metrics isn't the solution. In this paper, we propose a replacement full-reference quality assessment for stereoscopic images by learning binocular receptive field properties to be additional per human visual perception. To be additional specific, within the training part, we learn a multiscale dictionary from the coaching database, so that the latent structure of images can be represented as a collection of basis vectors. In the standard estimation section, we compute sparse feature similarity index based on the estimated sparse coefficient vectors by considering their section distinction and amplitude difference, and compute world luminance similarity index by considering luminance changes. The ultimate quality score is obtained by incorporating binocular combination based mostly on sparse energy and sparse complexity. Experimental results on five public 3D image quality assessment databases demonstrate that in comparison with the foremost related existing strategies, the devised algorithm achieves high consistency with subjective assessment.

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