Objective image quality assessment plays an important role in various computer vision and image processing applications. The most widely used image quality measures are the classical mean squared error (MSE), computed by averaging the squared intensity differences of distorted and reference image pixels, and its related quantity of the peak signal-to-noise ratio (PSNR). Unfortunately, these measures are not very well matched to perceived visual quality. In this paper, we propose a measure based on the intuitionistic fuzzy set theory, whose performance would be more closely related to the human perception of visual quality. The proposed measure provides a flexible mathematical framework for modelling of imprecise or/and imperfect information often present in digital images. Furthermore, we show how the neighborhood-based intuitionistic fuzzy similarity measures can be combined with intuitionistic fuzzy inclusion measures for improving the perceptive behavior of intuitionistic fuzzy similarity measures. The performance of the proposed measure is evaluated on a set of real images with different distortion types and the obtained results demonstrate its advantages over the classical measures.
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