Raster graphics definition1/26/2024 This mechanism is illustrated in the below image. Nested samplers are derived from the class TNestedSampler.īy nesting samplers, it is possible to create a chain of nested samplers between the sampler that generates the actual sample and the rasterizer. If the input of one sampler is the output from another, then we have a nested sampler. TContourRasterizer - the rasterization path is determined from the intensity of the collected samples.TTesseralRasterizer - rasterization by sub-division.TProgressiveRasterizer - rasterizes in a progressive manner by successively increasing the resolution of the image.TRegularRasterizer - rasterizes the bitmap row by row.Graphics32 includes the following rasterizers: Rasterizers can also provide various transition effects for creating transitions between bitmaps. Some rasterization schemes, such as swizzling, may improve cache-performance for certain applications, since samples are collected in a local neighborhood rather than row by row. Instances of TRasterizer need to be associated with a sampler and an output destination bitmap. A rasterizer class is derived from TRasterizer, by overriding the protected DoRasterize method. The rasterizer is responsible for the order in which output pixels are sampled and how the destination bitmap is updated. Rasterizationīy rasterizing an image, we collect samples for each pixel of an output bitmap. This way the kernel can be constrained to a certain width and reduce the amount of computations.įor further details about resampling, see Resamplers_Ex example project. TWindowedSincKernel is a base class for kernels that use the sinc function together with a window function (also known as tapering function or apodization function). Since this function has infinite extent, it is not practical for using as a convolution kernel (because of the computational overhead). ![]() ![]() The ideal low-pass filter is often referred to as a sinc filter. For high quality resampling, one should consider using a kernel that approximates the ideal low-pass filter. ![]() Graphics32 includes a class called TCustomKernel which is used as an ancestor class for various convolution kernels. This method is used in TKernelResampler, where a convolution filter is specified by the TKernelSampler.Kernel property. A general algorithm reconstructing samples is to perform convolution in a local neighborhood of the actual sample coordinate. In order to determine the color value of a sample at an arbitrary coordinate in a continuous image space, we need to perform interpolation for reconstructing this sample.ĭescendants of TCustomResampler implement various algorithms for performing resampling and sample acquisition. Hence we only know the actual color values at a number of discrete coordinates. We have a number of pixels, aligned on a rectangular square grid. In the 2D case we can think of the bitmap as our signal. The idea can also be extended from the 1D case to 2D. Resampling is the process of reconstructing samples from a discrete input signal. Another very common method for acquiring samples is resampling. It may also be acquired from some input hardware device. A sample may be created synthetically (this is a common technique within ray-tracing, fractal rendering and pattern generation). A sampler can be conceived as a scalar function f( x, y) that returns a color sample given a logical coordinate ( x, y). Graphics32 provides a special class called TCustomSampler, that provides the necessary mechanism for implementing different sampling techniques. ![]() Sampling is a process where color samples are acquired given their logical coordinates in the ( x, y) coordinate space. Sampling is a very important concept within digital image processing and image analysis.
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