Approach to metric and discrimination of blur based on its invariant features

Blur metrics have been used in broad range of applications to quantify the amount of blur especially in images. The spatially varying blur due to defocus or camera shake is hard to estimate. It is observed that the existing blur metrics does not perform well for images having very few or many features. In this work, we present contrast based blur invariant features named as CBIF, which utilizes useful information available in different contrast levels. We further, used CBIF along with local standard deviation to formulate a no reference objective blur metric which shows better results compared with other existing blur metrics. Additionally, the proposed blur metric can be modified for perceptual quality assessment by implementing the scheme which takes advantage of a better correlation with human blur perception. Also, the blur metric can be modified to provide blur assessment in the presence of gaussian noise. The proposed metric is monotonic as well as accurate even for severely blurred images. The comparison of results with subjective scores of CSIQ and LIVE image databases also validated the superiority of our proposed metric over existing metrics. The applicability of our blur metric is also demonstrated for the assessment of JPEG distortions. The property of CBIF for being more sensitive to blur effected regions is also used for obtaining blur likelihood map which is further used in blur segmentation.