Full-reference quality assessment of stereoscopic images by learning sparse monocular and binocular features

Kemeng Li; Feng Shao; Gangyi Jiang; Mei Yu

doi:10.1117/12.2073641

11 November 2014 Full-reference quality assessment of stereoscopic images by learning sparse monocular and binocular features

Kemeng Li, Feng Shao, Gangyi Jiang, Mei Yu

Author Affiliations +

Proceedings Volume 9273, Optoelectronic Imaging and Multimedia Technology III; 927312 (2014) https://doi.org/10.1117/12.2073641
Event: SPIE/COS Photonics Asia, 2014, Beijing, China

Abstract

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.

Citation Download Citation

Kemeng Li, Feng Shao, Gangyi Jiang, and Mei Yu "Full-reference quality assessment of stereoscopic images by learning sparse monocular and binocular features", Proc. SPIE 9273, Optoelectronic Imaging and Multimedia Technology III, 927312 (11 November 2014); https://doi.org/10.1117/12.2073641

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