Resampling
From Wikipedia, the free encyclopedia
Resampling is the digital process of changing the sample rate or dimensions of digital imagery or audio by temporally or areally analysing and sampling the original data. For the statistical use, please see Resampling (statistics).
[edit] Audio
Audio resampling also called sample rate conversion. This is an operation in digital signal processing, converting a sampled signal from one sampling frequency to another. For instance, the output waveform of a DAW that processes at 96 kHz must be resampled to 44.1 kHz to be placed on a CD. The article Sample rate conversion explains how this is done.
[edit] Bitmap
A digital image is known as a bitmap, it being a literal map of which pixels are what value, to construct an image. (This is not to be confused with the BMP image file format, which is a method of storing bitmaps in file data. PNG, JPEG and GIF are other equally valid methods of storing bitmaps.)
A bitmap is said to be sampled on each pixel, rather than being supersampled (more than one point of data per pixel) or subsampled (less than one point of data per pixel). Resampling this bitmap involves creating a sample grid, which is overlaid on the pixels. According to how far each grid point is away from the original centre of each pixel, and according to whatever resampling algorithm is in use, the new sample point is given a colour value.
The simplest resampling method is known as nearest neighbour or point sampling . The closest pixel center to each sample grid point is used, with no input from other surrounding pixels.
Bilinear interpolation is another method, where a sample point takes the four closest pixel centers and linearly interpolates their colour values according to their distance from the sample point. This method is particularly useful when an image is being enlarged, or transformed or distorted without decrease in average size. Other methods include Lanczos resampling and bicubic interpolation.
Supersampling is a method where several sample points are calculated and the results averaged (or combined according to a convolution kernel) to yield the required value on the sample grid. This method is particularly useful when an image is being reduced in size, or transformed or distorted with a decrease in average size.
