  To guarantee a satisfying Quality of Experience (QoE) for consumers, it is
required to measure image quality efficiently and reliably. The neglect of the
high-level semantic information may result in predicting a clear blue sky as
bad quality, which is inconsistent with human perception. Therefore, in this
paper, we tackle this problem by exploiting the high-level semantics and
propose a novel no-reference image quality assessment method for realistic blur
images. Firstly, the whole image is divided into multiple overlapping patches.
Secondly, each patch is represented by the high-level feature extracted from
the pre-trained deep convolutional neural network model. Thirdly, three
different kinds of statistical structures are adopted to aggregate the
information from different patches, which mainly contain some common statistics
(i.e., the mean\&standard deviation, quantiles and moments). Finally, the
aggregated features are fed into a linear regression model to predict the image
quality. Experiments show that, compared with low-level features, high-level
features indeed play a more critical role in resolving the aforementioned
challenging problem for quality estimation. Besides, the proposed method
significantly outperforms the state-of-the-art methods on two realistic blur
image databases and achieves comparable performance on two synthetic blur image
databases.
