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Automatic assessment of image quality in accordance with the human visual system (HVS) finds application in various image processing tasks. In the last decade, a substantial proliferation in image quality assessment (IQA) based on structural similarity has been observed. The structural information estimation includes statistical values (mean, variance, and correlation), gradient information, Harris response and singular values. In this paper, we propose a multiscale image quality metric which exploits the properties of Singular Value Decomposition (SVD) to get approximate pyramid structure for its use in IQA. The proposed multiscale metric has been extensively evaluated in the LIVE database and CSIQ database. Experiments have been carried out on the effective number of scales used as well as on the effective proportion of different scales required for the metric. The proposed metric achieves competitive performance with the structural similarity based state-of-the-art methods.