We present an optical flow-based no-reference video quality assessment (NR-VQA) algorithm for assessing the perceptual quality of natural videos. Our algorithm is based on the hypothesis that distortions affect flow statistics both locally and globally. To capture the effects of distortion on optical flow, we measure irregularities at the patch level and at the frame level. At the patch level, we measure intra- and inter-patch level irregularities in the flow magnitude's variance and mean. We also measure the correlation in the patch level flow randomness between successive frames. At the frame level, we measure the normalized mean flow magnitude difference between successive frames. We rely on the robust NIQE algorithm for no-reference spatial quality assessment of the frames. These temporal and spatial features are averaged over all the frames to arrive at a video level feature vector. The video level features and the corresponding DMOS scores are used to train a support vector machine for regression (SVR). This machine is used to estimate the quality score of a test video. The competence of the proposed method is clearly demonstrated on SD and HD video databases that include common distortion types such as compression artifacts, packet loss artifacts, additive noise, and blur. © 2016 IEEE.