Computer Science ›› 2020, Vol. 47 ›› Issue (11): 159-167.doi: 10.11896/jsjkx.190900052

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Relative Image Quality Assessment Based on CPNet

LI Kai-wen, XU Lin, CHEN Qiang   

  1. School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China
  • Received:2019-09-06 Revised:2019-11-21 Online:2020-11-15 Published:2020-11-05
  • About author:LI Kai-wen,born in 1994,postgraduate.His main research interests includeima-ge quality evaluation and so on.
    CHEN Qiang,born in 1979,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include image processing and analysis,pattern recognition and machine learning.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61671242).

Abstract: For two images with different quality,the human visual system (HVS) can easily distinguish their quality difference.Thus,it is more accurate to judge the relative quality of two images by simulating HVS than to give the absolute quality score of images.A CPNet (Compare-net) model for evaluating the relative quality between images is proposed in this paper.It is a score-independent algorithm that uses the form of image combination to solve the limitation of data volume.Compared with the absolute quality score label,the proposed relative quality label and relative quality order label have a broader application scenario than the absolute quality score label and are more convenient and accurate to obtain.Firstly,by analyzing the influence of convolutional neural network structure related parameters on network performance,a reasonable network infrastructure is constructed.Secondly,the quality difference characteristics of two images are obtained by the methods of two-channel input network and the feature differentiation,and the classification learning is completed by combining the relative quality labels of the image pairs.Finally,experiments on public database show that the accuracy of the proposed algorithm is better than that of other algorithms.CPNet achieved the best accuracy of 0.971 and 0.947 in the same reference image experiment,and also achieved a very competitive accuracy in different reference image experiments,0.926 and 0.860 respectively.In addition,a three-channel network is designed and experiments are carried out to explore the possibility of extending the proposed algorithm to multiple channels.

Key words: Convolutional network, CPNet, Image quality assessment, Quality difference, Relative quality order

CLC Number: 

  • TP391.4
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