  The intelligent video surveillance system (IVSS) can automatically analyze
the content of the surveillance image (SI) and reduce the burden of the manual
labour. However, the SIs may suffer quality degradations in the procedure of
acquisition, compression, and transmission, which makes IVSS hard to understand
the 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 a
crucial impact on the performance of the IVSS, and then propose a
saliency-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 and
recognition performance. Specifically, we first compute the saliency map of the
SI to select the most salient local region since the salient regions usually
contain rich semantic information for machine vision and thus have a great
impact on the overall quality of the SIs. Next, the convolutional neural
network (CNN) is adopted to extract quality-aware features for the whole image
and local region, which are then mapped into the global and local quality
scores through the fully connected (FC) network respectively. Finally, the
overall quality score is computed as the weighted sum of the global and local
quality scores. Experimental results on the SI quality database (SIQD) show
that the proposed method outperforms all compared state-of-the-art BIQA
methods.
