To blindly evaluate the visual quality of image is of great importance in many image processing and computer vision applications. In this paper, we develop a novel training-free no-reference (NR) quality metric (QM) based on a unified brain theory, namely, free energy principle. The free energy principle tells that there always exists a difference between an input true visual signal and its processed one by human brain. The difference encompasses the “surprising” information between the real and processed signals. This difference has been found to be highly related to visual quality and attention. More specifically, given a distorted image signal, we first compute the aforesaid difference to approximate its visual quality and saliency via a semi-parametric method that is constructed by combining bilateral filter and auto-regression model. Afterwards, the computed visual saliency and a new natural scene statistic (NSS) model are used for modification to infer the final visual quality score. Extensive experiments are conducted on popular natural scene image databases and a recently released screen content image database for performance comparison. Results have proved the effectiveness of the proposed blind quality measure compared with classical and state-of-the-art full- and no-reference QMs.