SR-SIM: A Fast and High Performance IQA Index based on Spectral Residual School of Software Engineering, Tongji University, Shanghai |
Introduction
This is the website for our paper "SR-SIM: A fast and high performance IQA index based on spectral residual", in: Proc. ICIP, 2012.
Source Code
The source code can be downloaded here: SR-SIM.m.
Evaluation Results
SR-SIM was evaluated on 3 publicly available large-scale IQA databases, including TID2008 database, CSIQ database, and LIVE database. The results (in Matlab .mat format) are provided here, together with performance evaluations for future comparisons. 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 SR-SIM 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 VSI values obtained on the TID2008 database:
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
matData
= load('SRSIMOnTID.mat');
SRSIMOnTID = matData.SRSIMOnTID;
SRSIM_TID_SROCC = corr(SRSIMOnTID(:,1), SRSIMOnTID(:,2), 'type',
'spearman');
SRSIM_TID_KROCC = corr(SRSIMOnTID(:,1), SRSIMOnTID(:,2), 'type', 'kendall');
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
The source codes to calculate the PLCC (Pearson Linear Correlation Coefficient) and RMSE (Root Mean-Squared-Error) 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 [1].
Evaluation results of SR-SIM on three 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.
Database |
SROCC | KROCC | PLCC | RMSE |
TID2008 |
0.8913 | 0.7149 | 0.8866 | 0.6206 |
CSIQ |
0.9319 | 0.7725 | 0.9250 | 0.0997 |
LIVE |
0.9618 | 0.8299 | 0.9553 | 8.0811 |
Weighted-Average |
0.9182 | 0.7566 | 0.9125 | กก |
Reference
[1] 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: Dec. 19, 2011
Last update: May. 06, 2012