Downupsampling

Downupsampling is a term used to describe processing that combines downsampling and upsampling within a single workflow. It is not a standard technical term with a single agreed meaning, but rather a generic description of techniques that reduce a signal’s sampling rate and then restore or increase it, or alternately allow different parts of a system to operate at different rates.

In practice, downupsampling appears in multi-rate signal processing, where data is downsampled to a lower rate for a computation or analysis stage and later upsampled back to the original rate to interface with other components. It is also used in neural networks and data processing pipelines that build multi-scale representations, such as autoencoders, pyramids, or encoder–decoder architectures, where a feature map is downsampled and then upsampled in a decoder.

Core methods involve careful handling of sampling and filtering. Downsampling typically uses decimation with anti-aliasing filtering to prevent high-frequency components from folding into the low-rate signal. Upsampling uses interpolation or learnable layers (e.g., transposed convolution) to reconstruct higher-resolution representations. In a downupsampling sequence, the choice of filters, interpolation scheme, and timing between stages determines phase accuracy and distortion.

Common applications include sample-rate conversion in audio, scale-space representations in image and video processing, and architectural components in neural networks that require cross-resolution information flow. The approach can enable efficient computation and flexible data integration but introduces potential artifacts and latency. Related concepts include downsampling, upsampling, multi-rate processing, and pyramid or multi-scale representations.