Image quality evaluation method based on multiple path fusion guided by reference images

Aiming at the problem that the existing image quality evaluation indexes are sometimes inconsistent with the visual perception of the human eye, this paper proposes an image quality evaluation method based on a reference image-guided multiple path fusion network. Firstly, the fused image (the image to be evaluated) and the reference image are inputted into two network branches respectively, and each network branch contains a Transformer module (VIT-Block), a Convolution module (CNN-Block) and a Center Difference Convolution module (CDC-Block), which are used for extracting the remote information, retaining the background information, and extracting the texture features, and the reference image is used as the output of each module of the fused image branch, and the reference image is used as the output of each module of the fused image branch. The output of each module of the branch is used as the weight of the output feature maps of each module of the fused image branch, and the weighting process is carried out to obtain three groups of feature maps, and then the dimensionality reduction process is carried out by using two 1×1 convolutional layers respectively to obtain three significant feature maps, and then the global average pooling is carried out respectively to obtain three evaluation scores, and finally, the average of the three scores is taken as the final objective evaluation index value of the fused image. The experimental results show that the results obtained by the image evaluation method proposed in this paper are consistent with the subjective visual perception.