Spatio Temporal Downsampling

Spatiotemporal downsampling involves reducing the spatial and temporal resolution of data, such as video or sensor readings, while aiming to preserve essential information. Current research focuses on developing intelligent downsampling methods, often employing neural networks, that minimize information loss and artifacts, particularly in the presence of missing data. These techniques are crucial for efficient data storage, processing, and forecasting, with applications ranging from video compression and upscaling to improved predictions in sensor networks and other time-series analyses. The development of effective downsampling strategies is vital for handling large datasets and improving the accuracy of various applications.