Image Quality Measures Software
Abstract The Image Quality Toolbox is a Matlab Toolbox for full reference color (and also achromatic) image quality assessment based on divisive normalization models in DCT and wavelet domains. The general idea to assess the perceptual distance between two images is to compute the qnorm Euclidean distance in the image representation at the V1 visual cortex, as suggested in [Teo&Heeger, IEEE ICIP 1994]. This markedly differs from the Mean Square Error (2norm Euclidean measure in the spatial domain), as shown in [Pons99, Epifanio03] These ideas have been implemented in the wavelet domain in the new code associated to the paper [Laparra10a]. Results using the wavelet based measure outperform SSIM and VIF and are intuitively interpretable in a linear way.


Related Papers
A.M. Pons, J. Malo, J.M. Artigas and P.Capilla Displays Journal, Vol. 20, pp. 93110. (1999) Linear Transform for Simultaneous Diagonalization of Covariance and Perceptual Metric Matrix in Image Coding I. Epifanio, J.Gutiérrez, J.Malo Pattern Recognition. Vol.36, pp. 17991811 (2003) Subjetive image fidelity metricbased on bit allocation of the human visual system in the DCT domain. J. Malo, A.M. Pons and J.M. Artigas Image and Vision Computing, Vol. 15, 7, pp 535548 (1997) Comparison of perceptually uniform quantization with average error minimization in image tranform coding J.Malo, F.Ferri, J.Albert, J.Soret Electronics Letters, Vol. 35, 13, pp. 10671068 (1999) The role of perceptual contrast nonlinearities in image transform quantization J. Malo, F. Ferri, J. Albert, J. Soret and J.M. Artigas Image & Vision Computing, Vol. 18, 3, pp. 233246 (2000) Perceptual feedback in multigrid motion estimation using an improved DCT quantization J.Malo, J.Gutierrez, I.Epifanio, F.Ferri, and J.M.Artigas IEEE Trans. Im. Proc. Vol.10, 10, pp. 14111427 (2001) Perceptual Adaptive Insensitivity for Support Vector Machine Image Coding G. Gómez, G. Camps, J. Gutiérrez, and J. Malo, IEEE Trans. Neural Networks Vol. 16, 6, pp 15741581 (2005) Nonlinear Image Representation for Efficient Perceptual Coding J. Malo, I. Epifanio, R. Navarro and E.P. Simoncelli, IEEE Trans. Im. Proc. Vol. 15, 1, pp 6880 (2006) On the Suitable Domain for SVM Training in Image Coding G. Camps, J. Gutiérrez, G. Gómez and J. Malo Journal of Machine Learning Research , Vol. 9, pp 4966 (2008)
V. Laparra, J. Muñoz and J. Malo. JOSA A, 27(4): 852864 (2010). Also selected by the OSA for the Virtual Journal for Biomedical Optics 5(8), 2010. 

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