@unpublished{Hardeberg2009_0,<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Abstract = {To measure how an image has been distorted from an original state, in a way corresponding to human perception, is a key goal of image quality research. Full reference image quality metrics have been proposed but they have not been good enough due to limited knowledge of the human visual system. Here we present recent contributions such as new image difference metrics based on bilateral filtering, hue angle weighting, the contrast sensitivity function, saliency maps, and eye tracking; a new measure of perceived contrast in complex images; a new image quality model for color prints; and an algorithm for halftone artifact detection. We also discuss how image quality metrics can be used to make better pictures by optimizing algorithms for image representation and reproduction.},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Author = {Jon Hardeberg},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Keywords = {quality},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Month = {April},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Note = {In seminar 'Chester F. Carlson Center for Imaging Science Seminar Series'},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Title = {Better Pictures from Better Measurements of Image Quality},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Url = {http://www.cis.rit.edu/files/2623_JonHardeberg_abstract.pdf},<BR>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Year = {2009}