rescale

Rescale is the process of changing the size or scale of an object, dataset, or measurement so that it fits a different reference frame or range. It is used across disciplines to ensure comparability, numerical stability, or conformity to a standard.

In mathematics and statistics, rescaling involves linear transformations that multiply variables by a scale factor and often add an offset. Common techniques include normalization, which maps data to a fixed range such as 0 to 1, and standardization, which centers data around zero with unit variance. Rescaling can affect distances and variance but generally preserves the order of values for monotone transformations.

In image processing and computer graphics, rescaling (or resizing) adjusts an image’s dimensions. Interpolation methods such as nearest neighbor, bilinear, and bicubic estimate new pixel values. Up-sampling and down-sampling can introduce artifacts like blurring or jagged edges, and maintaining aspect ratio is important to avoid distortion.

In machine learning and data science, feature rescaling is a common preprocessing step that can improve model training and convergence, especially for distance-based algorithms or regularization. It is often combined with other normalization or standardization techniques within data pipelines to ensure consistent input scales across features and experiments.

Other uses include the name of a company, Rescale, Inc., which provides cloud-based simulation and high-performance computing platforms for engineers to run computational analysis such as CFD and finite element simulations.

See also: normalization, standardization, interpolation, image resampling.