Image Quality Assessment Based on InterPatch and Intra-Patch

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In this paper, we propose a full-reference (FR) image quality assessment (IQA) scheme,
which evaluates image fidelity from two aspects: the inter-patch similarity and the intrapatch similarity. The scheme is performed in a patch-wise fashion so that a quality map can
be obtained. On one hand, we investigate the disparity between one image patch and its adjacent ones. This disparity is visually described by an inter-patch feature, where the hybrid
effect of luminance masking and contrast masking is taken into account. The inter-patch
similarity is further measured by modifying the normalized correlation coefficient (NCC). On
the other hand, we also attach importance to the impact of image contents within one patch
on the IQA problem. For the intra-patch feature, we consider image curvature as an important complement of image gradient. According to local image contents, the intra-patch similarity is measured by adaptively comparing image curvature and gradient. Besides, a
nonlinear integration of the inter-patch and intra-patch similarity is presented to obtain an
overall score of image quality. The experiments conducted on six publicly available image
databases show that our scheme achieves better performance in comparison with several
state-of-the-art schemes.