摘要
The objective assessment of image quality is important for image processing, which has been paid much attention to in recent years. However, there were few reports about objective quality assessment methods for geometrically distorted images. Different from the routine image degradation processing （for example, noise addi- tion, contrast change and lossy compression）, the geo- metric distortion results in the changes of the spatial relationship of image pixels, which makes the traditional quality assessment algorithms, such as mean square error （MSE） and peak signal to noise ratio （PSNR） failure to obtain expected assessment results. In this paper, a full reference image quality assessment algorithm is proposed specifically for the quality evaluation of geometrically distorted images. This assessment algorithm takes into account three key factors, such as distortion intensity, distortion change rate and line feature index for perceptual quality assessment of images. Experimental results in this study show that the proposed assessment algorithm not only is significantly better than those of the traditional objective assessment methods such as PSNR and structural similarity index measurement （SSIM）, but also has significant correlation with human subjective assessment.
The objective assessment of image quality is important for image processing, which has been paid much attention to in recent years. However, there were few reports about objective quality assessment methods for geometrically distorted images. Different from the routine image degradation processing （for example, noise addi- tion, contrast change and lossy compression）, the geo- metric distortion results in the changes of the spatial relationship of image pixels, which makes the traditional quality assessment algorithms, such as mean square error （MSE） and peak signal to noise ratio （PSNR） failure to obtain expected assessment results. In this paper, a full reference image quality assessment algorithm is proposed specifically for the quality evaluation of geometrically distorted images. This assessment algorithm takes into account three key factors, such as distortion intensity, distortion change rate and line feature index for perceptual quality assessment of images. Experimental results in this study show that the proposed assessment algorithm not only is significantly better than those of the traditional objective assessment methods such as PSNR and structural similarity index measurement （SSIM）, but also has significant correlation with human subjective assessment.
