In this study we compare the performance of Support Vector Machine (SVM)-based and Deep Neural Network (DNN)-based active learning for automated assessment of MR image quality. MR images were labeled by radiologists concerning perceived image quality and used as training and test data. DNN and SVM were trained to classify image quality on the training data. An active learning scheme was used for optimization of the training procedure. We found that using acitve learning with either SVM- or DNN- based classification allows for accurate and efficient automated assessment of MR image quality.