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Let L/M denote the upsampling factor, where L > M. Upsample by a factor of L; Downsample by a factor of M; Upsampling requires a lowpass filter after increasing the data rate, and downsampling requires a lowpass filter before decimation. Therefore, both operations can be accomplished by a single filter with the lower of the two cutoff frequencies.
In digital signal processing, downsampling, compression, and decimation are terms associated with the process of resampling in a multi-rate digital signal processing system. Both downsampling and decimation can be synonymous with compression , or they can describe an entire process of bandwidth reduction ( filtering ) and sample-rate reduction.
More specific types of resampling include: upsampling or upscaling; downsampling, downscaling, or decimation; and interpolation. The term multi-rate digital signal processing is sometimes used to refer to systems that incorporate sample-rate conversion.
A more explicit way than oversampling or downsampling could be to select a Pareto optimum by assign explicit costs to missclassified samples and then minimize the total (scalarized) costs via cost-sensitive machine learning. [18]
Multidimensional Filtering, downsampling, and upsampling are the main parts of multidimensional multirate systems and filter banks. [3] A complete filter bank consists of the analysis and synthesis sides. The analysis filter bank divides an input signal to different subbands with different frequency spectra.
Functions of space, time, or any other dimension can be sampled, and similarly in two or more dimensions. For functions that vary with time, let () be a continuous function (or "signal") to be sampled, and let sampling be performed by measuring the value of the continuous function every seconds, which is called the sampling interval or sampling period.
Comparison of Bicubic interpolation with some 1- and 2-dimensional interpolations. Black and red / yellow / green / blue dots correspond to the interpolated point and neighbouring samples, respectively.
The stationary wavelet transform (SWT) [1] is a wavelet transform algorithm designed to overcome the lack of translation-invariance of the discrete wavelet transform (DWT). ). Translation-invariance is achieved by removing the downsamplers and upsamplers in the DWT and upsampling the filter coefficients by a factor of () in the th level of the alg