Computer Science ›› 2021, Vol. 48 ›› Issue (8): 99-105.doi: 10.11896/jsjkx.200700106

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Full Reference Color Image Quality Assessment Method Based on Spatial and Frequency Domain Joint Features with Random Forest

YANG Xiao-qin, LIU Guo-jun, GUO Jian-hui, MA Wen-tao   

  1. School of Mathematics and Statistics,Ningxia University,Yinchuan 750021,China
  • Received:2020-07-16 Revised:2020-09-03 Published:2021-08-10
  • About author:YANG Xiao-qin,born in 1996,postgra-duate.Her main research interests include image quality assessment and machine learning.( Guo-jun,born in 1978,Ph.D,professor,Ph.D supervisor,master tutor.His main research interests include wavelet and partial differential equations for image processing,image quality assessment,and machine learning.
  • Supported by:
    Natural Science Foundation of Ningxia(2018AAC03014),National Natural Science Foundation of China(61461043,51769026),Key Research and Development Projects of Ningxia(2019BEG03056) and Graduate Innovation Project of Ningxia University(GIP2019011).

Abstract: This paper is to design an objective evaluation algorithm that automatically evaluates image quality and is consistent with the human visual system.In view of the fact that most traditional full reference image quality assessment methods only analyze images in the spatial domain,and have shortcomings in pooling strategies,this paper proposes a random forest based spatial-frequency domain joint feature full reference color image quality evaluation method.Firstly,this method extracts the chroma and gradient features in the spatial domain,which are used to characterize the color information and spatial structure information of images.The texture detail information of the response of the log-Gabor filter bank and spatial frequency features are extracted in the frequency domain,which are used to be joint features.Then,random forest is implemented for learning the mapping relationship between the feature vector and the subjective opinion score to predict the objective quality score.Experiments conducted on three standard databases,i.e.TID2013,TID2008,and CSIQ show that the comprehensive evaluation performance by our method is better than the state-of-the-art full reference assessment algorithms,especially on TID2013 database,the Pearson linear correlation coefficient value can reach 0.9397.

Key words: Frequency domain, Image quality assessment, Random forest, Spatial domain

CLC Number: 

  • TP391
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