NOVEL IMAGE-DEPENDENT QUALITY ASSESSMENT MEASURES

Abstract

The image is a 2D signal whose pixels are highly correlated in a 2D manner. Hence, using pixel by pixel error what we called previously Mean-Square Error, (MSE) is not an efficient way to compare two similar images (e.g., an original image and a compressed version of it). Due to this correlation, image comparison needs a correlative quality measure. It is clear that correlation between two signals gives an idea about the relation between samples of the two signals. Generally speaking, correlation is a measure of similarity between the two signals. An important step in image similarity was introduced by Wang and Bovik where a structural similarity measure has been designed and called SSIM. The similarity measure SSIM has been widely used. It is based on statistical similarity between the two images. However, SSIM can produce confusing results in some cases where it may give a non-trivial amount of similarity while the two images are quite different. This study proposes methods to determine a reliable similarity between any two images, similar or dissimilar, in the sense that dissimilar images have near-zero similarity measure, while similar images give near-one (maximum) similarity. The proposed methods are based on image-dependent properties, specifically the outcomes of edge detection and segmentation, in addition to the statistical properties. The proposed methods are tested under Gaussian noise, impulse noise and blur, where good results have been obtained even under low Peak Signal-to-Noise Ratios (PSNR’s).