摘要
提出了一种将图像的梯度幅值、相位以及结构相似度(SSIM)三者相结合的图像质量评价新模型——梯度相似度(GSIM)模型,以及基于该模型的图像质量评价算法。新模型与SSIM模型及基于梯度的模型相比,不仅包含亮度、对比度和结构三部分信息,同时增加了梯度相位信息。通过对LIVE图像数据库的982幅失真图像和924幅遥感压缩影像的实验,结果显示新模型的性能优于其他模型。与SSIM等模型相比,新模型能真实反映失真图像的视觉感知质量,具有较高的评价可靠性。
In order to evaluate the quality of the distorted image,it is necessary to calculate the similarity degree between the distorted image and the original image.By integrating gradient magnitude and gradient phase of image with structural similarity（SSIM）,this paper proposed a new image quality assessment model—— gradient similarity（GSIM）,and the image quality assessment algorithm based on this model.Compared with the SSIM model and the Gradient-based model,this new model not only includes luminance,contrast and structure of image,but more important lies in that it adds gradient phase information on the new model.The result of experiments,through evaluating 982 distorted images in the LIVE database and 924 remote sensing images compression,shows that this new model is superior to traditional models of MSE,PSNR,SSIM and the Gradient-based model.This new model,contrast with traditional model of SSIM,can find better solutions to the problem of objective assessment on seriously distorted images inconsistent with the subjective perception,and also the problem of the mixing evaluation effectiveness relatively worse to multiple types distorted images.Therefore,this new model can truly reflect the quality of the visual perception of the distorted image with higher assessment reliability.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2014年第12期1412-1418,共7页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金资助项目(41271456)~~
