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In this paper, we have shown how the fuzzy set theory is used in establishing measures for image quality evaluation. Objective quality measures or measures of comparison are of great importance in the field of image processing. These measures serve as a tool to evaluate and to compare different algorithms designed to solve problems, such as noise reduction, deblurring, compression, etc. It is well-known that classical quality measures, such as the MSE (mean square error) or the PSNR (peak signal to noise ratio), do not always correspond to human visual observations. Therefore, several researchers are - and have been - looking for new quality measures, better adapted to human perception. In this paper, we show how the neighbourhood-based similarity measures can be combined with similarity measures for histogram comparison in order to improve the perceptive behaviour of these similarity measures.