Image Quality Assessment based on Dual Domains Fusion

Image quality assessment is an important and necessary task in the field of image processing. It can simulate human visual perception accurately and effectively to ensure the credibility of information. Although the existing IQA algorithm based on CNN has achieved excellent success, the generalization and robustness of the algorithm are limited due to the loss of image information during feature extraction. The research shows that the phase and amplitude of image frequency domain will change with the quality, so we proposes an image quality assessment algorithm based on dual domains fusion (DualD-IQA). Using the frequency domain and spatial domain as multiple inputs of convolutional neural network, we can complement each other to represent image quality related information. Moreover, the input of any scale can be accepted by adding bilinear pooling, so as to ensure the reliability and robustness of the quality evaluation results. Experimental results show that the algorithm in this paper achieves higher consistency and accuracy in two commonly used public databases, and has higher robustness in different distortion types and cross databases.