- 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 Pixel
Pixel
Pixel is the smallest element of an image. Each pixel correspond to any one value. In an 8-bit gray scale image, the value of the pixel between 0 and 255. The value of a pixel at any point correspond to the intensity of the pght photons striking at that point. Each pixel store a value proportional to the pght intensity at that particular location.
PEL
A pixel is also known as PEL. You can have more understanding of the pixel from the pictures given below.
In the above picture, there may be thousands of pixels, that together make up this image. We will zoom that image to the extent that we are able to see some pixels spanision. It is shown in the image below.
![Einstein](/dip/images/einstein.jpg)
![pixel](/dip/images/pixel.jpg)
Relationship with CCD array
We have seen that how an image is formed in the CCD array. So a pixel can also be defined as
The smallest spanision the CCD array is also known as pixel.
Each spanision of CCD array contains the value against the intensity of the photon striking to it. This value can also be called as a pixel.
![relationship with ccd array](/dip/images/relationship_with_ccd_array.jpg)
Calculation of total number of pixels
We have define an image as a two dimensional signal or matrix. Then in that case the number of PEL would be equal to the number of rows multiply with number of columns.
This can be mathematically represented as below:
Total number of pixels = number of rows ( X ) number of columns
Or we can say that the number of (x,y) coordinate pairs make up the total number of pixels.
We will look in more detail in the tutorial of image types, that how do we calculate the pixels in a color image.
Gray level
The value of the pixel at any point denotes the intensity of image at that location, and that is also known as gray level.
We will see in more detail about the value of the pixels in the image storage and bits per pixel tutorial, but for now we will just look at the concept of only one pixel value.
Pixel value.(0)
As it has already been define in the beginning of this tutorial, that each pixel can have only one value and each value denotes the intensity of pght at that point of the image.
We will now look at a very unique value 0. The value 0 means absence of pght. It means that 0 denotes dark, and it further means that when ever a pixel has a value of 0, it means at that point, black color would be formed.
Have a look at this image matrix
0 | 0 | 0 |
0 | 0 | 0 |
0 | 0 | 0 |
Now this image matrix has all filled up with 0. All the pixels have a value of 0. If we were to calculate the total number of pixels form this matrix, this is how we are going to do it.
Total no of pixels = total no. of rows X total no. of columns
= 3 X 3
= 9.
It means that an image would be formed with 9 pixels, and that image would have a dimension of 3 rows and 3 column and most importantly that image would be black.
The resulting image that would be made would be something pke this
![black](/dip/images/black.jpg)
Now why is this image all black. Because all the pixels in the image had a value of 0.
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