Introduction

Information Content Weighted SSIM (IW-SSIM) index is an extension of MS-SSIM by using spatially varied weights. It is presented by Dr. Zhou Wang et al. on 2011 [1].

Source Code

The authors provide their implementation, which can be downloaded here https://ece.uwaterloo.ca/~z70wang/research/iwssim/iwssim_iwpsnr.zip.

Usage Notes

1. Do not change the default parameter settings.

2. IW-SSIM can only deal with gray-scale images and the luminance range is [0, 255]. So, for color images, before calling IW-SSIM, you need to convert it to [0, 255] gray-scale version. Usually, this can be accomplished by the Matlab routine rgb2gray.

Evaluation Results

The results (in Matlab .mat format) are provided here. Each result file contains a n by 2 matrix, where n denotes the number of distorted images in the database. The first column is the IW-SSIM values, and the second column is the mos/dmos values provided by the database. For example, you can use the following matlab code to calculate the SROCC and KROCC values for IW-SSIM values obtained on the TID2008 database:

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matData = load('IWSSIMOnTID.mat');
IWSSIMOnTID= matData.IWSSIMOnTID;
IWSSIM_TID_SROCC = corr(IWSSIMOnTID(:,1), IWSSIMOnTID(:,2), 'type', 'spearman');
IWSSIM_TID_KROCC = corr(IWSSIMOnTID(:,1), IWSSIMOnTID(:,2), 'type', 'kendall');

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The source codes to calculate the PLCC and RMSE are also provided for each database. This needs a nonlinear regression procedure which is dependant on the initialization of the parameters. We try to adjust the parameters to get a high PLCC value. For different databases, the parameter initialization may be different. The nonlinear fitting function is of the form as described in [2].

Evaluation results of IW-SSIM on seven databases are given below. Besides, for each evaluation metric, we present its weighted-average value over all the testing datasets; and the weight for each database is set as the number of distorted images in that dataset.

Reference                

[1] Z. Wang and Q. Li, "Information content weighting for perceptual image quality assessment", IEEE Trans. on Image Processing, vol. 20, no. 5, pp. 1185-1198, 2011.

[2] H.R. Sheikh, M.F. Sabir, and A.C. Bovik, "A statistical evaluation of recent full reference image quality assessment algorithms", IEEE Trans. on Image Processing, vol. 15, no. 11, pp. 3440-3451, 2006.

Created on: May 09, 2011

Last update: Dec. 07, 2013