Image Quality Assessment Based On Human Visual System

Image information has been widely applied with the dramatic development of multimedia,and image quality assessment has become a very meaningful subject in the image process field.The research of assessing multiply-distorted images has very important realistic significance since the steps of acquisition,compression,and transmission might introduce multiple distortions.In recent years biologists have made important progress in the study of human visual system.Researchers of image quality assessment have proposed many methods based on the human visual characteristics,and a good result was achieved.Based on the review to precious works,this paper proposes two image quality assessment methods for multiply-distorted image based on the human visual characteristics.Firstly,this paper proposes a no-reference quality assessment method for multiply-distorted image based on dual-tree complex wavelet transform and local binary pattern.Compared with traditional wavelet transform,dual-tree complex wavelet transform have more directional selectivity and approximate translation invariance.This algorithm processes image with dual-tree complex wavelet transform before extracting statistical features of the amplitude of wavelet transform coefficients with local binary patterns operator.The method uses the sum of the amplitude of wavelet coefficients in six directions of the first scale as the weight of the statistical processing for purpose of highlighting the significant part of the image.The experimental results on two multiply distorted image databases(MDID2013 and MLIVE)show that the proposed method has better consistent alignment with subjective assessment.Secondly,this paper proposes a full-reference quality assessment method for multiply-distorted image based on sparse representation and residual.The research of assessing multiply-distorted images has very important realistic significance since the steps of acquisition,compression,and transmission might introduce multiple distortions.Sparse representation has become the hotspot in field of image quality assessment owing to the ability that could effectively represent the human perception vision for the past few years.However currently sparse representation-based image quality assessment methods mainly work for images with single type of distortion while neglecting to consider the discrepant influence that the various distortions cause on the different components of image.At the same time,they also ignore to think about the effect of distortion on sparse residual and the supplementary role of residual.To tackle these problems,this paper proposes a full-reference image quality assessment method for multiply distorted image.We decompose the image into two parts,i.e.texture and the carton,and calculate the texture sparse coefficient and the carton sparse coefficient respectively.Meanwhile we consider the local residual and the global residual as well.The experimental results on MDID2013 and MLIVE indicate that the proposed method achieves better consistent alignment with subjective assessment.