Image quality metrics attempt to assess the perceived difference between an original and a distorted image. One aspect of quality assessment that is not yet well understood is the scale at which images are analyzed. This paper examines the effects that two scale-reducing operations, filtering and down-sampling, have on quality assessment. It has been shown that several popular quality metrics are equivalent to weighted measurements of mean-squared-error (MSE). Analysis is provided suggesting that the relative rankings of weighted MSE computations are not very sensitive to the scale of the input images; these predictions are verified experimentally using both weighted MSE-based and non-weighted-MSE-based assessment techniques. Featured algorithms include MSE, structural similarity index (SSIM), multi-scale SSIM (MSSIM), visual information fidelity (VIF) and visual signal-to-noise ratio (VSNR). Extensive testing on the LIVE database demonstrates that all algorithms except VSNR can be computed using images decimated by a factor of 6-8 without a substantial degradation in rank-order performance. This result illustrates that significant savings in processing are therefore possible.