The Use of Intensity-Based Measures to Produce Image Function Metrics Which Accommodate Weber's Models of Perception

[1]  Zhou Wang,et al.  Some "Weberized" L^2 -Based Methods of Signal/Image Approximation , 2014, ICIAR.

[2]  Zhou Wang,et al.  Information Content Weighting for Perceptual Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[3]  Zhou Wang,et al.  Structural Similarity-Based Approximation of Signals and Images Using Orthogonal Bases , 2010, ICIAR.

[4]  Alan C. Bovik,et al.  Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures , 2009, IEEE Signal Processing Magazine.

[5]  Jianhong Shen,et al.  Weberized Mumford-Shah Model with Bose-Einstein Photon Noise , 2006 .

[6]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[7]  Jianhong Shen,et al.  On the foundations of vision modeling: I. Weber’s law and Weberized TV restoration , 2003 .

[8]  Marios S. Pattichis,et al.  Foveated video quality assessment , 2002, IEEE Trans. Multim..

[9]  Jack Y. B. Lee On a unified architecture for video-on-demand services , 2002, IEEE Trans. Multim..

[10]  B. Forte,et al.  ERRATA: "SOLVING THE INVERSE PROBLEM FOR FUNCTION AND IMAGE APPROXIMATION USING ITERATED FUNCTION SYSTEMS, I. Theoretical Basis, II. Algorithm and Computations" , 1995 .

[11]  B. Wandell Foundations of vision , 1995 .

[12]  Bernd Girod,et al.  What's wrong with mean-squared error? , 1993 .

[13]  A. Oppenheim,et al.  Nonlinear filtering of multiplied and convolved signals , 1968 .

[14]  J. Michon Note on the generalized form of Weber’s Law , 1966 .