treammm / dog_ssim

Matlab implementation of "Image quality assessment using human visual DOG model fused with random forest"

Image quality assessment using human visual DOG model fused with random forest

This code is a Matlab implementation of the feature extraction part in [1]. The function for extracting features from an image pair is "featurev1.m". To be more specific, the output X(6:10) is the luminance features for DOG-SSIM and X(18) is the chrominance feature. You can train the DOG-SSIMc regression model with the feature set X([6:10, 18]), the Random Forest Regressor that I used can be found in [2] LINK.

References

[1] S. C. Pei and L. H. Chen. Image quality assessment using human visual DOG model fused with random forest. IEEE Trans. Image Process., 24(11):3282–3292, 2015.
[2] A. Jaiantilal, Classification and Regression by Random Forest-MATLAB, 2009, [online] Available: https://code.google.com/p/randomforest-matlab/issues/detail?id=9&q=citation.

Bibtex

@article{DOGSSIM2015, doi = {10.1109/tip.2015.2440172}, year = {2015}, month = nov, volume = {24}, number = {11}, pages = {3282--3292}, author = {Soo-Chang Pei and Li-Heng Chen}, title = {Image Quality Assessment Using Human Visual {DOG} Model Fused With Random Forest}, journal = {{IEEE} Transactions on Image Processing} }