Full-reference quality assessment of stereoscopic images by learning sparse monocular and binocular features
Kemeng Li, Feng Shao, Gangyi Jiang, Mei Yu · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2014
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.