关键词: 图像质量评价, 灰度共生矩阵, 相似图, 标准差, 熵, 支持向量回归

Abstract: In order to solve the Image Quality Assessment（IQA）problem，a method based on Similarity maps of Gray Level Co-occurrence Matrix（GLCMS）was proposed innovatively here. The Gray Level Co-occurrence Matrixes（GLCM）of reference image and distorted image were obtained firstly，then the similarity map of the two GLCM was calculated，and the standard deviation and entropy of the GLCMS were extracted as the feature vectors of the distorted image，finally these feature vectors were taken as the input to the Support Vector Regression（SVR）algorithm to predict the image quality. The TID database was established to evaluate image quality specially. The experimental results in the TID database demonstrate that the Spearman and Pearson correlation coefficients of this method with subjective method are all above 0. 93，no matter in the training dataset or in the testing dataset，it shows the method fits well with human visual characteristics. This method provides a new way of thinking for IQA，and achieves better subjective perceived consistency.

Key words: image quality assessment, Gray Level Co-occurrence Matrixe (GLCM), similarity map, standard deviation, entropy, support vector regression