Image quality assessment (IQA) is an important research area in image processing. Reduced-reference (RR) IQA methods contained therein mainly aim to estimate image quality degradations with partial information about the reference image. Following the remarkable achievement of SSIM, structural information has been recognized as one key factor, and has aroused many image quality metrics so far. In this paper, we design a structural degradation model (SDM). Then, the quality score of an image is defined as a nonlinear combination, or SVM based integration, of distance between the structural degradation information of the original and distorted images. Accordingly, a new RR IQA approach using the SDM model is exploited. Experimental results on LIVE database are provided to justify the superior prediction accuracy performance of the proposed method as compared to three significant image quality metrics, PSNR, SSIM and FEDM.