Evaluating algorithms used to assess image and video quality requires performance measures. Traditional performance measures (e.g., Pearson’s linear correlation coefficient, Spearman’s rank-order correlation coefficient, and root mean square error) compare quality predictions of algorithms to subjective mean opinion scores (mean opinion score/differential mean opinion score). We propose a subjective root-mean-square error (SRMSE) performance measure for evaluating the accuracy of algorithms used to assess image and video quality. The SRMSE performance measure takes into account dispersion between observers. The other important property of the SRMSE performance measure is its measurement scale, which is calibrated to units of the number of average observers. The results of the SRMSE performance measure indicate the extent to which the algorithm can replace the subjective experiment (as the number of observers). Furthermore, we have presented the concept of target values, which define the performance level of the ideal algorithm. We have calculated the target values for all sample sets of the CID2013, CVD2014, and LIVE multiply distorted image quality databases.The target values and MATLAB implementation of the SRMSE performance measure are available on the project page of this study.