Learning Receptive Fields and Quality Lookups for Blind Quality Assessment of Stereoscopic Images

Blind quality assessment of 3D images encounters more new challenges than its 2D counterparts. In this paper, we propose a blind quality assessment for stereoscopic images by learning the characteristics of receptive fields (RFs) from perspective of dictionary learning, and constructing quality lookups to replace human opinion scores without performance loss. The important feature of the proposed method is that we do not need a large set of samples of distorted stereoscopic images and the corresponding human opinion scores to learn a regression model. To be more specific, in the training phase, we learn local RFs (LRFs) and global RFs (GRFs) from the reference and distorted stereoscopic images, respectively, and construct their corresponding local quality lookups (LQLs) and global quality lookups (GQLs). In the testing phase, blind quality pooling can be easily achieved by searching optimal GRF and LRF indexes from the learnt LQLs and GQLs, and the quality score is obtained by combining the LRF and GRF indexes together. Experimental results on three publicly 3D image quality assessment databases demonstrate that in comparison with the existing methods, the devised algorithm achieves high consistent alignment with subjective assessment.