Motion trajectory based visual saliency for video quality assessment

In this paper, we propose a novel visual saliency detection method for video sequences by considering the object motion trajectories. Firstly, each frame of the video sequence is described in a new Quaternion Representation (QR), which comprises the spatial image content and the temporal motion characteristics. Based on the QR, Quaternion Fourier Transform (QFT) is employed to construct the visual saliency of the video sequence. Finally, the detected visual saliency map is incorporated with several video quality metrics. Compared with other visual saliency models, the proposed method can improve the performances of video quality metrics. It further confirms that the proposed visual saliency model can accurately depict the Human Vision System (HVS) properties.