In this paper,we propose a new no-ref erence image quality assessment (IQA) algorithm for blur and noise images using support vector regression (SVR) and singular value decomposition. The algorithm is composed of three steps.First,a re-blurred reference image i s produced by using Gaussian low-pass filter for a test image.Then we do singular value decomposi tion to them and calculate the change of their singular values.Thirdly,we train the support vector regres sion by using change of singular values and predict image quality score.Experimental results on three o pen blur and noise databases show that the proposed algorithm is more reasonable and stable than ot her methods.It has high correlation with human judgments and obtains a better evaluation index.So the proposed method is appropriate for no-reference blurred and noise image quality assessment.For the blur and noise distortion types,the performance indices of Spearman rank correlation coef ficient (SROCC) on the LIVE2database can reach 0.9613and 0.9659,re spectively.