Title: Structural similarity image quality assessment based on distortion model
Abstract: This work proposed a new structural similarity(SSIM) method,which is adapted for assessing images of different distortion types and different distortion intensities.The new method models a distorted image as an original image that subjects to linear frequency distortion(LFD) and additive noise injection(NI).LFD is local,and the SSIM of this type can be clustered by being weighted on quality sensitive regions.NI is uncorrelated with the original image,and the image quality of this type is underestimated by SSIM.So quality compensation is used to unify SSIM metric of these two types.Finally,the new method was validated with subjective quality scores on LIVE database which containing 982 images.Experimental results showed that the performance of the new method is comparable with the art-of-the-state objective methods.
Publication Year: 2009
Publication Date: 2009-01-01
Language: en
Type: article
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot