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

TL;DR

This paper introduces a saliency-based deep neural network for blind image quality assessment in surveillance systems, improving the filtering of low-quality images and enhancing detection accuracy.

Contribution

It proposes a novel deep neural network that combines saliency detection with quality assessment to better evaluate surveillance images without reference images.

Findings

Outperforms state-of-the-art BIQA methods on SI quality database

Effective in identifying low-quality surveillance images

Enhances face detection and recognition performance

Abstract

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.