We propose accessing the MRI quality, perceptual noise level in particular, during a scan to stop it when the image is good enough. A convolutional neural network is trained to map an image to a perceptual score. The label score for training is a statistical estimation of error standard deviation calibrated with radiologist inputs. Image rulers for different scan types are used in the inference phase to determine a flexible classification threshold. Our proposed training and inference methods achieve a 89% classification accuracy. The same framework can be used to tune the regularization parameter for compressed-sensing reconstructions.