The field of video super-resolution and image restoration is rapidly advancing with the development of new methods and techniques. One of the key trends is the use of diffusion models, which have shown promising results in video super-resolution tasks. These models are able to effectively capture temporal dependencies and fine spatial details, leading to significant improvements in reconstruction quality. Another area of focus is the development of more efficient and scalable methods, which can handle long sequences and high-definition videos. Researchers are also exploring new evaluation metrics and methodologies to better assess the performance of these models. Notably, some papers have proposed innovative approaches to address the challenges of video super-resolution, such as patch-wise processing and autoregressive modeling. Overall, the field is moving towards more efficient, effective, and scalable methods for video super-resolution and image restoration. Noteworthy papers include: DeLiVR, which proposes a differential spatiotemporal Lie bias for efficient video deraining, and PatchVSR, which breaks video diffusion resolution limits with patch-wise video super-resolution. Additionally, InfVSR and LVTINO have also made significant contributions to the field, with InfVSR breaking length limits of generic video super-resolution and LVTINO proposing a latent video consistency inverse solver for high-definition video restoration.