We present a deep dual attention network (DDAN) for video super-resolution, which cascades a motion compensation network (MCNet) and an SR reconstruction network (ReconNet). The MCNet utilize pyramid framework and learn the optical flow representations progressively to synthesize the motion information across adjacent frames. And it extracts detail components of LR neighboring frames for more accurate motion compensation. In ReconNet, we combine dual attention mechanisms and residual learning strategy for recovering high-frequency details. The DDAN performs effectively and generally on video super-resolution tasks. Relevant project has been released on Github.