Human Visual System（HVS） first roughly perceives global areas，then centers on the detailed local areas for the perception of image quality. In this paper，a novel blind Image Quality Assessment（IQA） algorithm was proposed for tone-mapped images by combining local and global features. First，the global features were extracted based on color moments，global entropy and bright/dark pixels' distribution under overexposure/underexposure conditions. Then，local contrast，local entropy and wavelet energy based on blocks were utilized to extract local features. Finally，global features were combined with local features to constitute a final feature vector. And all these feature vectors mentioned above were trained using Support Vector Regression（SVR） to generate a model，which bridges the feature space with quality space. Extensive experiments on a public ESPL-LIVE HDR database have demonstrated that the proposed method has a high consistency with subjective evaluation and outperforms state-of-the-art no-reference IQA metrics.