Blind image blur assessment by using valid reblur range and histogram shape difference

• Reference-free, training-free and transformation-free automatic blur assessment.
• A reblur algorithm is proposed to create valid reblur range and reblur images.
• Blurriness is estimated from the shape difference of local histograms.
• Experiment results indicate high correlation with human perception of blurriness.
• The proposed method performs better than recent methods in the spatial domain.

The presence of blur artifact is an annoyance to image viewers, and affects the perceived quality of the image. Telecommunication service providers and imaging product manufacturers are interested in this quality feedback for their process and product improvement. However, human-based quality feedback is tedious, expensive and has to be done in compliance with the standards for subjective evaluation such as the ITU-R BT. 500 standard. Thus, automatic assessment of images is proposed to overcome the difficulties in human-based evaluation. The automatic assessment is basically an objective estimation to predict the blur severity of an image. In this paper, a new model for blind estimation is proposed by using reblur algorithm to create reblur image and measure valid reblur range. Shape difference of local histograms is measured between the reblur and test images to produce the blur score. The proposed model is performed in the spatial domain without the need of data conversion or training. Experiment results show that the proposed model is highly correlated to human perception of blurriness, and performs better than other state-of-the-art blur metrics in the spatial domain.