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