A stereoscopic image quality assessment model based on independent component analysis and binocular fusion property

Quality assessment for stereoscopic image is an important research issue in three-dimensional researches. In this paper, an independent component analysis(ICA) and binocular combination-based full reference image quality assessment (FR-IQA) method is proposed for color stereoscopic image. Specifically, image features that reflect the responds of simple cells in the cortex are first extracted by an ICA-based feature detector, which models the functions of the receptive fields of simple cells in the primary visual cortex. Both image feature similarity (IFS) and local luminance consistency (LLC) of the right and left images are calculated to measure the structure and brightness distortions respectively. Image patches that the mean pixel values have been removed are used for IFS calculation, while the computation of LLC is based on the removed mean pixel value. To simulate the binocular fusion properties of the complex cells in the cortex, feature energy of the extracted image features is utilized to calculate the weighting factors of binocular combination, following which a single feature similarity index is obtained by fusing the right and left images IFS. Furthermore, global relative luminance information of the selected image patches is used to integrate the right and left images LLC into a single luminance consistency index. Finally, image quality is obtained by combining above two indexes. Experimental results demonstrate that the proposed algorithm achieves high consistency with subjective assessment on 3D image quality assessment databases.