Content-Insensitive Blind Image Blurriness Assessment Using Weibull Statistics and Sparse Extreme Learning Machine

Most of the existing image blurriness assessment algorithms are proposed based on measuring image edge width, gradient, high-frequency energy, or pixel intensity variation. However, these methods are content sensitive with little consideration of image content variations, which causes variant estimations for images with different contents but same blurriness degrees. In this paper, a content-insensitive blind image blurriness assessment metric is developed utilizing Weibull statistics. Inspired by the property that the statistics of image gradient magnitude (GM) follows Weibull distribution, we parameterize the GM using beta (scale parameter) and gamma (shape parameter) of Weibull distribution. We also adopt skewness (eta) to measure the asymmetry of the GM distribution. In order to reduce the influence of image content and achieve more robust performance, divisive normalization is then incorporated to moderate the beta, gamma, and eta. The final image quality is predicted using a sparse extreme learning machine. Performances evaluation on the blur image subsets in LIVE, CSIQ, TID2008, and TID2013 databases demonstrate that the proposed method is highly correlated with human perception and robust with image contents. In addition, our method has low computational complexity which is suitable for online applications.