Abstract
Objective quality measures or measures of comparison are of great importance in the field of image processing. These measures serve as a tool to evaluate and to compare different algorithms designed to solve problems, such as noise reduction, deblurring, compression … Consequently these measures serve as a basis on which one algorithm is preferred to another. It is well-known that classical quality measures, such as the MSE (mean square error) or the PSNR (peak signal to noise ratio), not always correspond to visual observations. Therefore, several researchers are—and have been—looking for new quality measures, better adapted to human perception.
The existing similarity measures are all pixel-based, and have therefore not always satisfactory results. To cope with this drawback, we propose a similarity measure based on a neighborhood, so that the relevant structures of the images are observed very well. The new similarity measure is designed especially for the use in image processing.