- DIP - Computer Vision and Graphics
- DIP - Optical Character Recognition
- DIP - JPEG compression
- DIP - Introduction to Color Spaces
- DIP - High Pass vs Low Pass Filters
- DIP - Convolution theorm
- DIP - Fourier series and Transform
- DIP - Frequency Domain Analysis
- DIP - Laplacian Operator
- DIP - Krisch Compass Mask
- DIP - Robinson Compass Mask
- DIP - Sobel operator
- DIP - Prewitt Operator
- DIP - Concept of Edge Detection
- DIP - Concept of Blurring
- DIP - Concept of Masks
- DIP - Concept of convolution
- DIP - Gray Level Transformations
- DIP - Histogram Equalization
- DIP - Introduction to Probability
- DIP - Histogram Stretching
- DIP - Histogram Sliding
- DIP - Image Transformations
- DIP - Brightness and Contrast
- DIP - Histograms Introduction
- DIP - Concept of Dithering
- DIP - ISO Preference curves
- DIP - Concept of Quantization
- DIP - Gray Level Resolution
- DIP - Pixels Dots and Lines per inch
- DIP - Spatial Resolution
- DIP - Zooming methods
- DIP - Concept of Zooming
- DIP - Pixel Resolution
- DIP - Concept of Sampling
- DIP - Grayscale to RGB Conversion
- DIP - Color Codes Conversion
- DIP - Types of Images
- DIP - Concept of Bits Per Pixel
- DIP - Perspective Transformation
- DIP - Concept of Pixel
- DIP - Camera Mechanism
- DIP - Image Formation on Camera
- DIP - Concept of Dimensions
- DIP - Applications and Usage
- DIP - History of Photography
- DIP - Signal and System Introduction
- DIP - Image Processing Introduction
- DIP - Home
DIP Useful Resources
Selected Reading
- Who is Who
- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes
Concept of Samppng
Conversion of analog signal to digital signal:
The output of most of the image sensors is an analog signal, and we can not apply digital processing on it because we can not store it. We can not store it because it requires infinite memory to store a signal that can have infinite values.
So we have to convert an analog signal into a digital signal.
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.
Samppng
Quantization
We will discuss samppng now, and quantization will be discussed later on but for now on we will discuss just a pttle about the difference between these two and the need of these two steps.
Basic idea:
The basic idea behind converting an analog signal to its digital signal is
![basic_idea](/dip/images/basic_idea.jpg)
to convert both of its axis (x,y) into a digital format.
Since an image is continuous not just in its co-ordinates (x axis), but also in its ampptude (y axis), so the part that deals with the digitizing of co-ordinates is known as samppng. And the part that deals with digitizing the ampptude is known as quantization.
Samppng.
Samppng has already been introduced in our tutorial of introduction to signals and system. But we are going to discuss here more.
Here what we have discussed of the samppng.
The term samppng refers to take samples
We digitize x axis in samppng
It is done on independent variable
In case of equation y = sin(x), it is done on x variable
It is further spanided into two parts , up samppng and down samppng
![samppng](/dip/images/samppng.jpg)
If you will look at the above figure, you will see that there are some random variations in the signal. These variations are due to noise. In samppng we reduce this noise by taking samples. It is obvious that more samples we take, the quapty of the image would be more better, the noise would be more removed and same happens vice versa.
However, if you take samppng on the x axis, the signal is not converted to digital format, unless you take samppng of the y-axis too which is known as quantization. The more samples eventually means you are collecting more data, and in case of image, it means more pixels.
Relation ship with pixels
Since a pixel is a smallest element in an image. The total number of pixels in an image can be calculated as
Pixels = total no of rows * total no of columns.
Lets say we have total of 25 pixels, that means we have a square image of 5 X 5. Then as we have dicussed above in samppng, that more samples eventually result in more pixels. So it means that of our continuous signal, we have taken 25 samples on x axis. That refers to 25 pixels of this image.
This leads to another conclusion that since pixel is also the smallest spanision of a CCD array. So it means it has a relationship with CCD array too, which can be explained as this.
Relationship with CCD array
The number of sensors on a CCD array is directly equal to the number of pixels. And since we have concluded that the number of pixels is directly equal to the number of samples, that means that number sample is directly equal to the number of sensors on CCD array.
Oversamppng.
In the beginning we have define that samppng is further categorize into two types. Which is up samppng and down samppng. Up samppng is also called as over samppng.
The oversamppng has a very deep apppcation in image processing which is known as Zooming.
Zooming
We will formally introduce zooming in the upcoming tutorial, but for now on, we will just briefly explain zooming.
Zooming refers to increase the quantity of pixels, so that when you zoom an image, you will see more detail.
The increase in the quantity of pixels is done through oversamppng. The one way to zoom is, or to increase samples, is to zoom optically, through the motor movement of the lens and then capture the image. But we have to do it, once the image has been captured.
There is a difference between zooming and samppng
The concept is same, which is, to increase samples. But the key difference is that while samppng is done on the signals, zooming is done on the digital image.
Advertisements