No-reference multi-scale blur metric

In recent years, digital cameras have been widely used for image capturing. These devices are equipped in cell phones, laptops, tablets, webcams, etc. Image quality is an important characteristic for any digital image analysis. Historically, techniques to assess image quality for these mobile products require a standard image to be used as a reference image. In this case, Root Mean Square Error and Peak Signal to Noise Ratio can be employed to measure the quality of the images. However, these methods are not valid if there is no reference image. Recent studies show that a Contourlet is a multi-scale transformation - which is an extension of two dimensional wavelet transformations - that can operate on an image at different noise levels without a reference image. In this paper, we develop a no-reference blur metric for digital images based on edges and noises in images. In our approach, a Contourlet transformation is applied to the blurred image, which applies a Laplacian Pyramid and Directional Filter Banks to get various image representations. The Laplacian Pyramid is a difference of Gaussian Pyramids between two consecutive levels. At each level in the Gaussian Pyramid, an image is smoothed with two Gaussians of different sizes then subtracted, subsampled and the input image is decomposed into directional sub-bands of images. Directional filter banks are designed to capture high frequency components representing directionality of the images which is similar to detailed coefficient in wavelet transformation. We focus on blur-measuring for each level and directions at the finest level of images to assess the image quality. Using the ratio of blur pixels to total pixels, we compare our results, which require no reference image, to standard full-reference image statistics. The results demonstrate that our proposed no reference metric has an increasing relationship with the blurriness of an image and is more sensitive to blur than the correlation full-reference metric.