Remote visualization allows clients to visualize a dataset stored on remote servers without first securing a local copy of the dataset. In conventional remote visualization the visualization parameter changes requested by the client are transmitted to the server which computes a new frame and transmits it to the client. Network bandwidth and latency limitations reduce the quality and interactivity of such an approach. We propose instead to transmit superimages from the server to the client, where a superimage contains sufficient data for a quality reconstruction of thousands of frames at the client, without any additional data from the server. Whereas conventional remote visualization frames become obsolete with the slightest visualization parameter change, superimages cover a subvolume of the multidimensional space of visualization parameters. Dynamic datasets are particularly challenging since sample motion exacerbates disocclusion errors and increases the size of the superimage encoding. In this presentation I will give an introduction to the remote visualization problem, I will give an overview of our system, I will be presenting ongoing work for addressing the disocclusion error challenge, and I will be discussing remaining open problems.