Technology used to automatically assess video quality plays a significant role in video processing areas. Because of the complexity of video media, there are great limitations to assess video quality with only one factor. We propose a new method using artificial random neural networks (RNNs) with motion evaluation as an estimation of perceived visual distortion. The results are obtained through a nonlinear fitting procedure and well correlated with human perception. Compared with other methods, the proposed method performs more adaptable and accurate predictions.