No-reference objective blur metric based on the notion of wavelet gradient, magnitude edge width

In the past decade, the number and popularity of digital cameras has increased many fold, increasing the demand for a blur metric and quality assessment techniques to evaluate digital images. There is still no widely accepted industry standard by which an image's blur content may be assessed so it is imperative that better, more reliable, no-reference metrics be created to fill this gap. In this paper, a new wavelet based scheme is proposed as a blur metric. This method does not rely on subjective testing other than for verification. After applying the discrete wavelet transform to an image, we use adaptive thresholding to identify edge regions in the horizontal, vertical, and diagonal sub-images. For each sub-image, we utilize the fact that detected edges can be separated into connected components. We do this because, perceptually, blur is most apparent on edge regions. From these regions it is possible to compute properties of the edge such as length and width. The length and width can then be used to measure the area of a blurred region which in turn yields the number of blurred pixels for each connected region. Ideally, an edge point is represented by only a single pixel so if a found edge has a width greater than one it likely contains blur. In order to not skew our results, a one by n-length rectangle is removed from the computed blur area. The areas are summed which will represent the total blur pixel count per image. Using a series of test images, we determined the blur pixel ratio as the number of blur pixels to the total pixels in an image.