TY - JOUR
T1 - A new metric for grey-scale image comparison
AU - Wilson, D.L.
AU - Owens, Robyn
AU - Baddeley, Adrian
PY - 1997
Y1 - 1997
N2 - Error measures can be used to numerically assess the differences between two images. Much work has been done on binary error measures, but little on objective metrics for grey-scale images. In our discussion here we introduce a new grey-scale measure, Delta(g), aiming to improve upon the most common grey-scale error measure, the root-mean-square error. Our new measure is an extension of the authors' recently developed binary error measure, Delta(b), not only in structure, but also having both a theoretical and intuitive basis. We consider the similarities between Delta(b) and Delta(g) when tested in practice on binary images, and present results comparing Delta(g) to the root-mean-squared error and the Sobolev norm for various binary and grey-scale images. There are no previous examples where the last of these measures, the Sobolev norm, has been implemented for this purpose.
AB - Error measures can be used to numerically assess the differences between two images. Much work has been done on binary error measures, but little on objective metrics for grey-scale images. In our discussion here we introduce a new grey-scale measure, Delta(g), aiming to improve upon the most common grey-scale error measure, the root-mean-square error. Our new measure is an extension of the authors' recently developed binary error measure, Delta(b), not only in structure, but also having both a theoretical and intuitive basis. We consider the similarities between Delta(b) and Delta(g) when tested in practice on binary images, and present results comparing Delta(g) to the root-mean-squared error and the Sobolev norm for various binary and grey-scale images. There are no previous examples where the last of these measures, the Sobolev norm, has been implemented for this purpose.
U2 - 10.1023/A:1007978107063
DO - 10.1023/A:1007978107063
M3 - Article
VL - 24
SP - 5
EP - 17
JO - International Journal of Computer Vision
JF - International Journal of Computer Vision
SN - 0920-5691
ER -