Reduced-reference (RR) image quality assessment (IQA) intends to utilize less information of the reference image and yield higher evaluation accuracy. In this paper, a novel RR-IQAmetric that measures differences between the energy in reorganized discrete cosine (RDCT) domain of reference and distorted images as perceived by Human is presented. Firstly, we decompose an image into ten sub-bands in this new frequency domain. Since RDCT representation exhibits structural similarities between sub-bands, and can mimic the function of Human Visual System (HVS). Secondly, we extract features from the ten sub-bands by analyzing what are the key elements that influence subjective quality. Finally, we fuse these extracted features based on the principal that we exert different importance in accordance with the different impact each individual subband plays on the perceptual quality. Experimental results demonstrate that the proposedmetric outperforms the state-of-the-art (RR) IQAmetrics and even the full-reference (FR) IQA metrics SVD and SSIM. What is more, compared with many existing RR IQAs, the proposed metric earns obvious superiority in terms of the amount of its required information fromreference image and computational complexity.