Exploring Contrast Multi-Attribute Representation With Deep Network for No-Reference Perceptual Quality Assessment

Aiming at the effectiveness of contrast feature design, we proposed a promising novel non-reference quality assessment approach in exploring Attribute-Based representation. The method generates three perceptual attribute categories tailored to contrast. The first is semantic attribute derived from deep convolutional neural network, which implements adaptive contrast prediction relevant to scenario content. Second, for perceiving Spatial channel attribute, the global and local features generated by dark channel map through the designed dual convolution structures. Third, for statistical attribute, we assume the enhanced image as "reference" and calculate the structural similarity with pristine image, and the entropy and histogram metrics are also employed to assist learning. After that, for maximizing utilization, the features are embedded and integrated hierarchically to translate into objective score. In addition, a medium-scale contrast distortion database is established to support further research, which is more challenging than existing datasets because of the sufficient content and sophisticated changes. We demonstrate the availability of structures quantitatively and verify the rationality of hypothesis. Extensive experiments reveal that the proposed method outperforms advanced methods and achieves the state-of-the-art on the created database and CSIQ, TID2013, CCID2014.