Perceptual stereoscopic image quality assessment (SIQA) aims to use computational models to measure the image
quality in consistent with human visual perception. In this research, we try to simulate monocular and binocular visual
perception, and proposed a monocular-binocular feature fidelity (MBFF) induced index for SIQA. To be more specific,
in the training stage, we learn monocular and binocular dictionaries from the training database, so that the latent response
properties can be represented as a set of basis vectors. In the quality estimation stage, we compute monocular feature
fidelity (MFF) and binocular feature fidelity (BFF) indexes based on the estimated sparse coefficient vectors, and
compute global energy response similarity (GERS) index by considering energy changes. The final quality score is
obtained by incorporating them together. Experimental results on four public 3D image quality assessment databases
demonstrate that in comparison with the most related existing methods, the devised algorithm achieves high consistency
alignment with subjective assessment.
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