Generative adversarial network (GAN) for super-resolution (SR) has attracted enormous interest in recent years. It has been widely used to solve the single-image super-resolution (SISR) task and made superior performance. However, GAN is rarely used for video super-resolution (VSR). VSR aims to improve video resolution by exploiting the temporal continuity and spatial similarity of video sequence frames. We design a GAN with multi-feature discriminators and combine it with optical flow estimation compensation to construct an end-to-end VSR framework OFC-MFGAN. Optical flow estimation compensation makes use of temporal continuity and spatial similarity features of adjacent frames to provide rich detailed information for GAN. Multi-feature discriminators based on visual attention mechanism include the pixel discriminator, edge discriminator, gray discriminator, and color discriminator. GAN with multi-feature discriminators makes the data distribution and visually sensitive features (edge, texture, and color) of SR frames similar to high-resolution frames. OFC-MFGAN effectively integrates the time, space, and visually sensitive features of videos. Extensive experiments on public video datasets and surveillance videos show the effectiveness and robustness of the proposed method. Compared with several state-of-the-art VSR methods and SISR methods, the proposed method can not only recover prominent edges, clear textures, and realistic colors but also make a pleasant visual feeling and competitive perceptual index.