No-Reference Image Sharpness Assessment Using Scale and Directional Models

We propose a new method of no-reference (NR) image sharpness assessment. Our method is based on a multiscale decomposition of non-overlapping blocks, and on analyzing the statistics of Local-Mean Magnitude (LMM) maps. With the detected high-activity blocks, a set of inter-scale and inter-direction sharpness ratios are found. These sharpness ratios are closely correlated with the level of blurring, and is capable of measuring image sharpness effectively from a multiscale and a directional view. They are linearly combined using automatic estimated weighting parameters to induce an overall effective sharpness model. To enhance accuracy, a sharpness factor based on the blur effect on DCT edges and a log-energy sharpness model are also incorporated into the method. Experiments on a large number of public images have shown that our method consistently produces predictions that are highly correlated with human perceptual of image sharpness, and outperforms the current state-of-the-art algorithms.