No-reference Quality Assessment for Blur Image Combined with Re-blur Range and Singular Value Decomposition

In this paper, a new no-reference image quality assessment（IQA） algorithm is proposed for blur images based on re-blur range and singular value decomposition. Firstly, a reference image is constructed based on the difference of re-blur range. Secondly, singular value decomposition is utilized on reference image and test image, and distortion feature vectors are extracted by computing the comparability of singular value matrixes. Thirdly, visual saliency is detected by Log-Gabor filters and the difference of Gaussian model. Finally, the image quality is assessed from distortion feature vectors weight by visual saliency. Extensive experiments conducted on publicly IQA databases demonstrate that this method has higher correlation with human judgment and obtains a better evaluation index compared to other methods. The performance indices of Spearman rank correlation coefficient and root mean square errors on the LIVE2 database are 0.968 7 and 4.858 9, respectively. It doesn’t need training to assess image quality and has wide value for application and popularization.