Traditional no reference blur image quality assessment methods usually need a pretraining and learning or a reference image constructing procedure

this result in the algorithm with high computation cost. Aiming to this

a simple and effective no reference blur image quality assessment algorithm is proposed based on wavelet high frequency coefficients singular value decomposition. The method is build on the observations that the different wavelet high frequency subbands in the same scale of an image are highly structural correlation

and the degree of correlation would be reduced as the blur distortion deepening. According to this

the new method first makes wavelet transform to the image

then makes singular value decomposition to the high frequency sub-bands to get their structure information. Finally

the angles

which represents the similarity

between different high frequency sub-bands structural vectors are calculated and the sum of angles is used as the last objective assessment index. Experiments results show its good effectiveness and performance on LIVE2

CSIQ and TID2013databases and compared to the traditional noreference methods

the proposed algorithm is more efficient and practical as it does not need to train or create a reference image.