Non-reference Image Quality Evaluation Based on Multi-scale Convolutional Neural Network
Image quality evaluation is widely applied in image processing field.In this paper,an evaluation method for non-reference image quality based on multi-scale convolutional neural network is proposed,which combines convolutional neural network with migration learning.Firstly,by using the combination of Resnet-50 network and perception module,the multi-scale semantic information features satisfying human perception system are extracted effectively Then,the features of the local semantic information are fused by the adaptive fusion network,and finally,the local semantic information after fusion is connected with the global semantic information,and input into the full-connection regression network to effectively evaluate the image quality.In order to verify the validity of the model,the performance comparison experiments are done on LIVE,Kaniq-10k and LIVEC data sets respectively.The experimental results show that the performance of the proposed model is better than most of the current mainstream algorithms,and the generalization performance on the true distortion data set is better,which is suitable for natural distortion scenes.
deep learningno-reference image quality evaluationmulti-scale semantic informationfeature fusion
NETL
NSTL
万方数据
