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Similarity or distance measures play an important role in various pattern recognition applications such as classification, clustering, change detection, information retrieval, energy minimization and optimization problems. We shall analyze theoretically the two most popular quality measures MSE and SSIM used in image processing by showing their origin, similarities/differences and advantages/drawbacks. Both measures depend on the same parameters: sample means, standard deviations and correlation coefficient. It is shown that SSIM originates from two Dice measures and thus inherit their main drawback -dependence on the absolute mean and standard deviation values. Similarly, MSE depends on the absolute standard deviation values. A new similarity measure Composite quality index based on Means, Standard deviations and Correlation coefficient (CMSC) is proposed inheriting advantages of the both measures but at the same time avoiding their drawbacks.

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Abstract:A new Joint Quality Measure (JQM), which is a sole measure, is proposed for quality ranking of pansharpening methods. It is based on a newly proposed Composite similarity measure, which consists of Means, Standard deviations and Correlation coefficient (CMSC), and is translation invariant with respect to means and standard deviations. The JQM itself consists of a weighted sum of two terms. The first term is measured between a low pass filtered pansharpened image and original multispectral image at a reduced/low resolution scale. The second term is measured between the intensity calculated from spectrally weighted pansharpened multispectral image and original panchromatic image in a high resolution scale. Experimental results show advantages of a new measure, JQM, for quality assessment of pansharpening methods on the one hand, and drawbacks or unexpected properties of the already known measure, Quality with No Reference (QNR), on the other hand.