The Pearson correlation coefficient (PCC) measures how linearly a metric's scores track the human Mean Opinion Score, answering the accuracy question: does a VMAF of 80 land where a MOS of 4.0 should. It runs from -1 to 1, where 1 means a perfect straight-line relationship; higher values mean the metric and the panel move together more closely. PCC is the covariance of the two columns divided by the product of their standard deviations. A crucial catch is that you cannot compute it directly between a 0-100 metric and a 1-5 MOS because the scales differ, so a monotonic fitting curve (a five-parameter logistic) first maps the metric onto the MOS scale; PCC is computed on those mapped values. Unlike SROCC, which judges only rank order, PCC penalizes uneven spacing, so a metric can score a perfect SROCC yet a slightly lower PCC. It is one of the four ITU-T P.1401 validation statistics, alongside SROCC, RMSE, and the outlier ratio.
