Image quality assessment (IQA) aims to estimate human perception based image
visual quality. Although existing deep neural networks (DNNs) have shown
significant effectiveness for tackling the IQA problem, it still needs to
improve the DNN-based quality assessment models by exploiting efficient
multi-scale features. In this paper, motivated by the human visual system (HVS)
combining multi-scale features for perception, we propose to use pyramid
features learning to build a DNN with hierarchical multi-scale features for
distorted image quality prediction. Our model is based on both residual maps
and distorted images in luminance domain, where the proposed network contains
spatial pyramid pooling and feature pyramid from the network structure. Our
proposed network is optimized in a deep end-to-end supervision manner. To
validate the effectiveness of the proposed method, extensive experiments are
conducted on four widely-used image quality assessment databases, demonstrating
the superiority of our algorithm