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Feature SIMilarity (FSIM) index between two images

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Description

ref

Lin Zhang, Lei Zhang, Xuanqin Mou, and David Zhang,”FSIM: a feature similarity index for image qualtiy assessment”, IEEE Transactions on Image Processing, vol. 20, no. 8, pp. 2378-2386, 2011.

Input :

(1) imageRef: the first image being compared

(2) imageDis: the second image being compared

Output:

(1) FSIM: is the similarty score calculated using FSIM algorithm. FSIM only considers the luminance component of images. For colorful images,  they will be converted to the grayscale at first.

(2) FSIMc: is the similarity score calculated using FSIMc algorithm. FSIMc considers both the grayscale and the color information.

Note: For grayscale images, the returned FSIM and FSIMc are the same.

 

 

(a) is the PC map of the reference image while (b) ~ (f) are the PC maps of the distorted images. (b) and (d) are more similar to (a) than (c), (e), and (f). In (c), (e), and (f), regions with obvious differences to the corresponding regions in (a) are marked by colorful rectangles.

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