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The intelligent video surveillance system (IVSS) can automatically analyzethe content of the surveillance image (SI) and reduce the burden of the manuallabour. However, the SIs may suffer quality degradations in the procedure ofacquisition, compression, and transmission, which makes IVSS hard to understandthe content of SIs. In this paper, we first conduct an example experiment (i.e.the face detection task) to demonstrate that the quality of the SIs has acrucial impact on the performance of the IVSS, and then propose asaliency-based deep neural network for the blind quality assessment of the SIs,which helps IVSS to filter the low-quality SIs and improve the detection andrecognition performance. Specifically, we first compute the saliency map of theSI to select the most salient local region since the salient regions usuallycontain rich semantic information for machine vision and thus have a greatimpact on the overall quality of the SIs. Next, the convolutional neuralnetwork (CNN) is adopted to extract quality-aware features for the whole imageand local region, which are then mapped into the global and local qualityscores through the fully connected (FC) network respectively. Finally, theoverall quality score is computed as the weighted sum of the global and localquality scores. Experimental results on the SI quality database (SIQD) showthat the proposed method outperforms all compared state-of-the-art BIQAmethods.

Blind Surveillance Image Quality Assessment via Deep Neural Network Combined with the Visual Saliency