Generally speaking，distortion will change the statistical characteristics of low frequency and high frequency components of images.With this consideration，a novel no-reference image quality assessment algorithm was proposed to the predict the perceived quality of multiply-distorted images.First，the images were transformed into local Walsh Hadamard Transform maps by local Walsh Hadamard Transform.Then，the features (i.e.,rotation invariant local binary pattern features of zero sequency term and non-zero sequency term) were extracted on local Walsh Hadamard Transform maps to reflect low frequency and high frequency components of images.Finally，the extracted features were trained using support vector regression to form the model，which implemented mapping from the feature space to the quality scores.Comprehensive evaluations were conducted on two multiply-distorted databases (MLIVE database and MDID2013 database)，and experimental results show that the proposed method consists well with human subjective perception.Besides，the performance of algorithm is statistically superior to the existence of better full-reference image quality assessment and non-reference image quality assessment algorithms.