No-reference blurred image quality assessment method based on structure of structure features

The deep structure in the image contains certain information of the image, which is helpful to perceive the quality of the image. Inspired by deep level image features extracted via deep learning methods, we propose a no-reference blurred image quality evaluation model based on the structure of structure features. In spatial domain, the novel weighted local binary patterns are proposed which leverage maximum local variation maps to extract structural features from multi-resolution images. In spectral domain, gradient information of multi-scale Log-Gabor filtered images is extracted as the structure of structure features, and combined with entropy features. Then, the features extracted from both domains are fused to form a quality perception feature vector and mapped into the quality score via support vector regression (SVR). Experiments are conducted to evaluate the performance of the proposed method on various IQA databases, including the LIVE, CSIQ, TID2008, TID2013, CID2013, CLIVE, and BID. The experimental results show that compared with some state-of-the-art methods, our proposed method achieves better evaluation results and is more in line with the human visual system. The source code will be released at https://github.com/JamesC0321/s2s_features/.