Efficient Image Sharpness Assessment Based on Content Aware Total Variation

State-of-the-art sharpness assessment methods are mostly based on edge width, gradient, high-frequency energy, or pixel intensity variation. Such methods consider very little the image content variation in conjunction with the sharpness assessment which causes the sharpness metric to be less effective for different content images. In this paper, we propose an efficient no-reference image sharpness assessment called content aware total variation (CATV) by considering the importance of image content variation in sharpness measurement. By parameterizing the image TV statistics using generalized Gaussian distribution, the sharpness measure is identified by the standard deviation, and the image content variation evaluator is indicated by the shape parameter. However, the standard deviation is content-dependent which is different for the regions with strong edges, high frequency textures, low frequency textures, and blank areas. By incorporating the shape-parameter in moderating of the standard deviation, we propose a content aware sharpness metric. The experimental results show that the proposed method is highly correlated with the human vision system and has better sharpness assessment results than the state-of-the-art techniques on the blurred subset images of LIVE, TID2008, CSIQ, and IVC databases. Also, our method has very low computational complexity which is suitable for online applications. The correlations with the subjective of the four databases and statistical significance analysis reveal that our method has superior results when compared with previous techniques.