Structural Approaches to Image Quality Assessment

[1]  Zhou Wang,et al.  Stimulus synthesis for efficient evaluation and refinement of perceptual image quality metrics , 2004, IS&T/SPIE Electronic Imaging.

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

[3]  Sanjit K. Mitra,et al.  Special issue on objective video quality metrics , 2004, Signal Process. Image Commun..

[4]  Zhou Wang,et al.  Video quality assessment based on structural distortion measurement , 2004, Signal Process. Image Commun..

[5]  Eero P. Simoncelli,et al.  A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients , 2000, International Journal of Computer Vision.

[6]  Alexander Toet,et al.  A new universal colour image fidelity metric , 2003 .

[7]  Zhou Wang,et al.  Multi-scale structural similarity for image quality assessment , 2003 .

[8]  Jesús Malo,et al.  Linear transform for simultaneous diagonalization of covariance and perceptual metric matrix in image coding , 2003, Pattern Recognit..

[9]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[10]  Zhou Wang,et al.  Why is image quality assessment so difficult? , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  Rajesh P. N. Rao,et al.  Probabilistic Models of the Brain: Perception and Neural Function , 2002 .

[12]  Eero P. Simoncelli,et al.  Natural image statistics and divisive normalization: Modeling nonlinearity and adaptation in cortical neurons , 2002 .

[13]  Zhou Wang,et al.  Embedded foveation image coding , 2001, IEEE Trans. Image Process..

[14]  Eero P. Simoncelli,et al.  Natural signal statistics and sensory gain control , 2001, Nature Neuroscience.

[15]  Eero P. Simoncelli,et al.  Natural image statistics and neural representation. , 2001, Annual review of neuroscience.

[16]  Francesc J. Ferri,et al.  Non-linear Invertible Representation for Joint Statistical and Perceptual Feature Decorrelation , 2000, SSPR/SPR.

[17]  Song-Chun Zhu,et al.  Prior Learning and Gibbs Reaction-Diffusion , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  James R. Bergen,et al.  Pyramid-based texture analysis/synthesis , 1995, Proceedings., International Conference on Image Processing.

[19]  Patrick C. Teo,et al.  Perceptual image distortion , 1994, Proceedings of 1st International Conference on Image Processing.

[20]  Geoffrey M. Boynton,et al.  New model of human luminance pattern vision mechanisms: analysis of the effects of pattern orientation, spatial phase, and temporal frequency , 1994, Other Conferences.

[21]  D. Ruderman The statistics of natural images , 1994 .

[22]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[23]  André Gagalowicz,et al.  A New Method for Texture Fields Synthesis: Some Applications to the Study of Human Vision , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Olivier D. Faugeras,et al.  Decorrelation Methods of Texture Feature Extraction , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.