Measuring noise in colour images

Algorithms exist for removing unwanted noise from colour images; however little has been said about how to actually quantify the effect of these algorithms or generally how to quantify the amount of noise in a colour image. The standard methods include calculating the normalised mean square error (NMSE) between the original and the filtered image, calculating the mean chromaticity error (MCRE) between the original and the filtered image and of course visual inspection. The problem of MCRE and NMSE is that they only quantify the differences between two images-they are not a specific measure for noise in an image. Visual inspection is problematic because it is subjective. In this paper we propose a novel method for specifically calculating the amount of noise in a colour image. We give a general definition of how pixels could be classified as `noisy´ based on well-established vector processing methodologies. We then describe how the sum of these `noisy´ pixels can then be calculated and normalised to give a general measure of the percentage of neighbourhoods containing noisy pixels, which can then be used as a non-subjective estimate of the amount of noise in a colour image