To measure perceptual similarity between an original image and its modified version

we propose an objective measure that is relatively stable to normal image processing but quite sensitive to significant changes of the image content in local areas. This is achieved by low pass filtering the two images

dividing them into overlapping blocks and determining similarity between the corresponding blocks in terms of correlation coefficient that is mapped to the interval \[0

1\]. Based on previous calculated correlation coefficients

a ratio is calculated between the smallest and largest correlation coefficients and defined as the perceptual similarity. 〖BP(〗Products of a predefined number of the smallest and largest correlation coefficients are calculated. Perceptual similarity is defined as the ratio between these two products.〖BP)〗 Experimental results show that the proposed metric is not substantially affected by normal image processing. It provides indication of changes in the image contents when its value becomes less than a given threshold. The proposed metric is useful in applications such as image hashing and CBIR.