Short note on: Image sampling
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In order to become suitable for digital processing, an image function f(x,y) must be digitized both spatially and in amplitude. Typically, a frame grabber or digitizer is used to sample and quantize the analogue video signal. Hence in order to create an image which is digital, we need to covert continuous data into digital form. There are two steps in which it is done:

  • Sampling
  • Quantization

The sampling rate determines the spatial resolution of the digitized image, while the quantization level determines the number of grey levels in the digitized image. A magnitude of the sampled image is expressed as a digital value in image processing. The transition between continuous values of the image function and its digital equivalent is called quantization. The number of quantization levels should be high enough for human perception of fine shading details in the image. The occurrence of false contours is the main problem in image which has been quantized with insufficient brightness levels. Image sampling refers to an algorithm for sampling and filtering the image function and producing the final array of pixels that constitute the rendered image. This sampler always takes the same number of samples per pixel. This sampler takes a variable number of samples per pixel depending on the difference in the intensity of the pixel. This sampler divides the image into an adaptive grid like structure and refines depending on the difference in pixel intensity.

(1) Pixels are discrete samples of continuous function.

(2) Frequency analysis.

(3) Sampling due to limited spatial and temporal resolution.

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