Matlab Code for Sparse Feature Fidelity (SFF)
SFF is a new algorithm for evaluating perceptual quality of color images.
For quality evaluation, you can just run 'SFF' as follows:
load('W.mat'); % load the feature detector
score = SFF(refImg, disImg, W); % refImg and disImg respectively denote the reference image and distorted image
The quality scores are between 0 and 1, where 1 represents the same quality as the reference image.
Feature detector W is a matrix of size 8*192 generated by running TrainW(18000,8,8) on data1. W can be used for extracting features from image patches. The Training code is in the folder '\Training'. You can run 'TrainW' to get the feature detector. For example:
W = TrainW(18000,8,8);
In our paper we used 18000 sample patches of size 8*8, and retained only 8 components. Two sets of images are provided for the training stage, i.e., data1 and data2, which is described in our paper. We suggest you use data1 for training.
SFF performs very well on CSIQ, LIVE, IVC, TID2008, TID2013, and Toyama database. This package also provides four examples for testing this algorithm on CSIQ, LIVE, TID2008, and TID2013 databases.
Thorough discussion can be found in:
Hua-wen Chang, Hua Yang, Yong Gan, and Ming-hui Wang, "Sparse Feature Fidelity for Perceptual Image Quality Assessment", IEEE Transactions on Image Processing, vol. 22, no. 10, pp. 4007-4018, October 2013
Cite As
Hua-wen Chang (2024). Matlab Code for Sparse Feature Fidelity (SFF) (https://www.mathworks.com/matlabcentral/fileexchange/43783-matlab-code-for-sparse-feature-fidelity-sff), MATLAB Central File Exchange. Retrieved .
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- Image Processing and Computer Vision > Image Processing Toolbox > Image Segmentation and Analysis > Image Quality >
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