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 fac...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.展开更多
文摘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.
