Attention-driven residual-dense network for no-reference image quality assessment

With the rapid development of deep learning, convolutional neural networks have been applied to no-reference image quality assessment (NR-IQA), but most methods focus on the design of complex networks, which not only increase network parameters and make the training process more difficult, but also fail to make full use of the rich global and local information in images. To address this problem, this paper proposed an effective NR-IQA method, namely, attention-driven residual dense network, which can evaluate the quality of images quickly and accurately. Specifically, three different sizes of convolution kernels are first used to extract features from images by parallel, so that the feature information of images can be expressed at different scales. Next, several cascaded residual dense channel attention blocks are used to further extract high-level feature information, which can capture the most effective feature. In addition, we embed a novel channel attention mechanism into the multi-scale feature extraction block and the residual dense block to filter out channel-specific attention by learning correlations between channels. A series of experiments on public synthetic databases show that the proposed method outperforms the state-of-the-art NR-IQA methods.