gridsampling

Grid sampling, sometimes written as gridsampling, is a method of representing a continuous domain by sampling its values at the points of a regular lattice, typically a rectangular Cartesian grid. Each axis is sampled at uniform intervals, producing a grid of sample locations. This approach yields a structured data grid that is easy to index and process, and it is widely used in digital imaging, geographic information systems, 3D scanning, and numerical analysis.

In signal processing and communications, grid sampling of a one- or multi-dimensional signal imposes a sampling rate. To avoid aliasing, the sampling frequency must be at least twice the highest frequency component (Nyquist criterion). In practice, pre-filtering with a low-pass filter is common before downsampling.

When data is mapped back to a grid or when displaying an image at a different resolution, interpolation is used to estimate values at non-grid points. Common methods include nearest neighbor, bilinear, and bicubic interpolation. The fixed geometry simplifies convolution-based operations and hardware acceleration, but can introduce artifacts if the grid is too coarse for the content.

Limitations include aliasing for high-frequency content, and inefficiency when the scene contains features at varying scales; nonuniform or adaptive sampling schemes may be preferred in such cases. Some applications combine grid sampling with oversampling or multi-resolution grids.

Applications include digital photography and video sensors, satellite and aerial imagery, Digital Elevation Models and other gridded rasters in GIS, voxelization in computer graphics and physics simulations, and numerical integration using lattice-based quadrature on regular grids.