Introduction

NQM (Noise Quality Measure) index is presented  [1].

Source Code

The original implementation provided by the authors can be found at http://users.ece.utexas.edu/~bevans/papers/2000/imageQuality/index.html. However, we find that it can only deal with squared images (that means the number of rows and the number of columns of the image is the same). Thus, we used the NQM implementation provided in the MetriX MuX package, which is available at .http://foulard.ece.cornell.edu/gaubatz/metrix_mux/. MetriX MuX's NQM implementation is the same as the original NQM implementation, only having some modifications to make it accept non-squared images.

Usage Notes

1. Using MetriX MuX package, you do not need to convert the RGB image to the grayscale version; such a step is embedded in Metrix MuX.

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 NQM 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 NQM values obtained on the TID2008 database:

%%%%%%%%%%%%%%%

matData = load('NQMOnTID.mat');
NQMOnTID= matData.NQMOnTID;
NQM_TID_SROCC = corr(NQMOnTID(:,1), NQMOnTID(:,2), 'type', 'spearman');
NQM_TID_KROCC = corr(NQMOnTID(:,1), NQMOnTID(:,2), 'type', 'kendall');

%%%%%%%%%%%%%%%

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 NQM 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] N. Damera-Venkata, T.D. Kite, W.S. Geisler, B.L. Evans, and A.C. Bovik, "Image quality assessment based on degradation model", IEEE Trans. on Image Processing, vol. 9, no. 4, pp.636-650, 2000.

[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. 02, 2013