resampled

Resampled refers to the process of changing the sampling rate of a discrete signal, image, or data set by either increasing (upsampling) or decreasing (downsampling) the number of samples. In signal processing, the original signal is usually represented as a sequence of values taken at uniform intervals; resampling adjusts these intervals to meet different analysis or transmission requirements. The operation commonly involves interpolation to estimate intermediate values when upsampling, and decimation—often accompanied by low‑pass filtering—to avoid aliasing when downsampling.

Statistical applications use resampling techniques such as bootstrapping and jackknife to generate multiple pseudo‑samples from an observed data set. These methods assess variability, construct confidence intervals, and evaluate model stability without assuming a specific underlying distribution. In this context, “resampled” denotes the individual pseudo‑samples drawn during the iterative procedure.

In image processing, resampling changes the resolution of a digital picture. Algorithms such as nearest‑neighbor, bilinear, bicubic, and Lanczos interpolation determine how pixel values are computed for the new grid. Proper resampling is crucial for preserving visual quality, especially when scaling images for display on devices with differing pixel densities.

Key considerations when resampling include the preservation of signal or image fidelity, the prevention of artifacts like aliasing or ringing, and computational efficiency. Appropriate filter design, selection of interpolation method, and adherence to the Nyquist criterion are essential to maintain the integrity of the resampled data. Related concepts include sampling theorem, interpolation, decimation, and data augmentation.