Research on Dual Channel Non Reference Image Quality Evaluation Combined with Global Local Features
Aiming at the problem that most Image Quality Assessment(IQA)algorithms are not effective in evaluating the quality of non-uniform distorted images,A two-channel No-Reference Image Quality Assessment(NR-IQA)algorithm combining global and local features is proposed.Firstly,considering the different sizes of input images,the local distortion recombination algorithm is used to preprocess the input images.Secondly,the two-channel neural network based on Swin Transformer module is used to extract the global and local features of the image.Finally,the global-local feature is mapped to the image quality score through the quality regression prediction network.The experimental results show that the Spearman Rank Order Correlation Coefficient(SROCC)index values of 0.823 and 0.871 are obtained on the two datasets, respectively, indicating that the proposed algorithm is in good agreement with human subjective perception.
NETL
NSTL
万方数据
