Feature SIMilarity Index for IQA

FSIM: A Feature SIMilarity Index for Image Quality Assessment

Lin Zhang, Lei Zhang, Xuanqin Mou and David Zhang

IEEE Trans. Image Processing, vol. 20, no. 8, pp. 2378-2386, 2011.

Introduction

Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural-similarity (SSIM) index brings IQA from pixel based stage to structure based stage. In this work, a novel feature-similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS’ perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local similarity map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA databases demonstrate that FSIM can achieve much higher consistency with the subjective evaluations than all the state-of-the-art IQA metrics used in comparison. Although FSIM is designed for grayscale images (or the luminance components of color images), the chrominance information can be easily incorporated by means of a simple extension of FSIM, and we call this extension FSIM_C.

Paper:

Lin Zhang, Lei Zhang, X. Mou and D. Zhang, “FSIM: A Feature Similarity Index for Image Quality Assessment,” IEEE Trans. Image Processing, vol. 20, no. 8, pp. 2378-2386, 2011.

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Source Code

The source code to compute the proposed FSIM/FSIM_C can be downloaded here: FeatureSIM.m.

Usage:

%Given 2 test images img1 and img2. For gray-scale images, their dynamic range should be 0-255.
%For colorful images, the dynamic range of each color channel should be 0-255.
[FSIM, FSIMc] = FeatureSIM(img1, img2);

Note: FSIM compares two images based on their luminance components only; while FSIM_C also considers the chromatic information in addition to the luminance.

Evaluation Results

The FSIM/FSIM_Cvalues are computed (using FeatureSIM.m) for 6 publicly available IQA databases, including TID2008 database, CSIQ database, LIVE database, IVC database, Toyama-MICT database, and Cornell A57 database. The results (in Matlab .mat format) are provided here, together with performance evaluations based on Spearman rank order correlation coefficient (SROCC) and Kendall rank order correlation coefficient (KROCC), for future comparisons. Each result file contains a n by 3 matrix, where n denotes the number of distorted images in the database. The first column is the FSIM values, the second column is the FSIM_Cvalues, and the third 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 FSIM and FSIM_Cvalues obtained on the TID2008 database:

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matData = load('FSIMOnTID2008.mat');
FSIMOnTID2008 = matData.FSIMOnTID2008;
FSIM_TID_SROCC = corr(FSIMOnTID2008(:,1), FSIMOnTID2008(:,3), 'type', 'spearman');
FSIM_TID_KROCC = corr(FSIMOnTID2008(:,1), FSIMOnTID2008(:,3), 'type', 'kendall');
FSIMc_TID_SROCC = corr(FSIMOnTID2008(:,2), FSIMOnTID2008(:,3), 'type', 'spearman');
FSIMc_TID_KROCC = corr(FSIMOnTID2008(:,2), FSIMOnTID2008(:,3), 'type', 'kendall');

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Evaluation results of FSIM/FSIM_C on six databases

Database	Results	FSIM		FSIM_C
Database	Results	SPROCC	KROCC	SPROCC	KROCC
TID2008	FSIMOnTID2008	0.8805	0.6946	0.8840	0.6991
CSIQ	FSIMOnCSIQ	0.9242	0.7567	0.9310	0.7690
LIVE	FSIMOnLIVE	0.9634	0.8337	0.9645	0.8363
IVC	FSIMOnIVC	0.9262	0.7564	0.9293	0.7636
Toyama-MICT	FSIMOnMICT	0.9059	0.7302	0.9067	0.7303
A57	FSIMOnA57	0.9181	0.7639	0.9181	0.7639

Note: since images in A57 are gray-scale, FSIM_Cwill produce exactly the same results with FSIM.

Reference

Lin Zhang, Lei Zhang, X. Mou and D. Zhang, “FSIM: A Feature Similarity Index for Image Quality Assessment,” IEEE Trans. Image Processing, vol. 20, no. 8, pp. 2378-2386, 2011. (paper)

Lei Zhang’s homepage

Last update: Jan 20, 2011.