1. Concepts for sampling and quantification (Concepts)
The goal of the captured image is to generate a digital image from the perceived data, but the output of the sensor is a continuous voltage waveform, and therefore it is necessary to convert the continuous sense data into a digital form. This process is accomplished by sampling and quantifying the image. Digitized coordinate values ​​are called samples; digitized amplitude values ​​are called quantizers.
Image sampling
â—† When sampling, if the number of horizontal pixels (number of columns) is M, and the number of pixels in the vertical direction (number of rows) is N, the total number of pixels in the image is M*N pixels.
â—† In general, the larger the sampling interval, the fewer the number of pixels in the resulting image, the lower the spatial resolution, the poorer the quality, and the mosaic effect in severe cases; the smaller the sampling interval, the larger the number of pixels in the resulting image, the higher the spatial resolution, and the image quality Yes, but the amount of data is large.
Image quantification
â—† The more quantification levels, the richer the image levels are, the higher the grayscale resolution, and the better the image quality, but the larger the amount of data; the lower the quantification level, the less abundant image levels, the lower the grayscale resolution, and the false contour phenomenon. Poor quality, but the amount of data is small.
It is clear that the quality of digital images depends to a large extent on the number of samples and gray levels used in sampling and quantification. In general, when defining the size of a digital image, the following principles may be used to obtain a better quality image:
(1) For slowly-varying images, they should be quantified and coarsely sampled to avoid false contours.
(2) Fine-grained images should be sampled and quantized to avoid blurring (aliasing).
2. Digital image description
Black and white image
Refers to the image of each pixel can only be black or white, there is no intermediate transition, it is also called a binary image. The 2-valued image has pixel values ​​of 0 and 1.
Grayscale image
A grayscale image refers to an image where the information of each pixel is described by a quantized grayscale level, without color information.
Color image
A color image is an image in which the information of each pixel is composed of three primary colors of RGB, where RBG is described by different gray levels.
3. Digital representation of the image
Let f(s, t) denote a continuous image function with two continuous variables s and t. Through sampling and quantification, we can convert this function into a digital image. The continuous image is sampled as a two-dimensional array f(x,y) with M rows and N columns, where (x,y) is discrete coordinates.
In some discussions, it is more convenient to use traditional matrix representations to represent digital images and their pixels:
A two-dimensional matrix is ​​an important mathematical form that represents a digital image. An M×N image can be represented as a matrix: each element in the matrix is ​​called the “pixel†of the image. Each pixel has its own "position" and "value", which is the color or intensity of this "position" pixel.
Assuming that the image size is M, N, each pixel has a discrete number of gray levels G; these quantities are taken as an integer power of 2,m,n,k, ie, M=2m, N=2n, G=2k. The number of bits needed to store this image is: If the image is a square, ie M=N, when an image has a gray level, it is actually called the image as a k-bit image. For example, an image with 256 possible gray levels is called an 8-bit image.
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