What is the difference between sampling and downsampling?
Terminology: sampling – creating a discrete signal from a continuous process. downsampling (decimation) – subsampling a discrete signal upsampling – introducing zeros between samples to create a longer signal aliasing – when sampling or downsampling, two signals have same sampled representation but differ between sample locations.
How are content-adaptive image downsampling kernels formed?
The key idea is to optimize the shape and locations of the downsampling kernels to better align with local image features. Our content-adaptive kernels are formed as a bilateral combination of two Gaussian kernels defined over space and color, respectively.
Which is the de facto standard for image downscaling?
The de facto standard for image downscaling are linear filters, originating from the signal processing community [Wolberg 1990]. Here, the image is first convolved with a low-pass kernel to reduce the bandwidth before it is resampled to the final resolution.
How are images downscaled to prevent aliasing?
Classical image downscaling techniques find their origin in sam- pling theory [Shannon 1949], and prefilter and reconstruct the sig- nal with a spatially constant lowpass filter in order to prevent alias- ing in the reconstructed signal. However, by suppressing high fre- quencies they also tend to blur the signal.
How are smart sampling and incremental function learning used?
The accuracy and confidence levels of the resulting approximation function are assessed using the probably approximately correct computation framework. The smart sampling and incremental function learning techniques can be easily used in practical applications and scale well in the case of extremely large data.
What makes a sampling method a good sampling method?
Generally speaking a good sampling method should create training data that accurately represents the underlying function preserving the statistical characteristics of the complete dataset. This section presents first a survey of sampling methods grouped in four categories that can be applied to generating high dimensional sample data. 2.1.