MODULE – I
1.
Introduction to Digital Image and Its Representation
2.
Image Processing to Computer Vision- Low Level, Mid-Level, High Level Image
Processing with Examples
3.
Sensing and Acquisition
4.
Sampling and Quantization
5.
Image Resolution and Storage; Convolution
6.
Basic Relationship between Pixels
7.
Monadic and Dyadic Operators
TOPIC 5: IMAGE RESOLUTION, STORAGE, CONVOLUTION
Grey Level of an Image
k represents the no. of bits used to express grey level (pixel brightness)
Due to processing, storage and sampling hardware consideration, the number of grey level is an
integer power of 2
L = 2k
For 8 bit per pixel, it is 28 = 256
For 6 bit per pixel, it is 26 = 64
For 4 bit per pixel, it is 24 = 16
For 2 bit per pixel, it is 21 = 2
Dynamic Range of an Image
Range of values spanned by the grey scale is called the Dynamic Range of an image. Discrete
levels are equally spaced and they are integers in the interval o to L-1
A High Contrast Image - Images whose grey scale occupies a significant portion of the grey
scale having high Dynamic Range
A Low Contrast image - Images whose grey scale occupies a small portion of the grey scale
having low Dynamic Range
Distinguish between Gray Level Resolution and Spatial Resolution
Gray Level Resolution
Gray-level Resolution of an image is a term that refers to the number of shades of gray that is
used for displaying the image. Variations in gray-level resolution affect the appearance of the
image
Spatial Resolution
Spatial Resolution of an image is the physical size of a pixel in that image; i.e., the area in the
scene that is represented by a single pixel in that image
Commonly available grey level of an image
8 bits are used in common applications, 256 grey levels
16 bits are used in specific applications, where enhancement of specific grey level range is
required
10 or 12 bits are used in some rare application
Zooming of Image
Method 1: Replicating each row and each column a number of times
Two Methods:
-
Nearest Neighbor Interpolation
-
Bilinear Interpolation
Method 2: Replicating each pixel a number of times
Zooming - Nearest neighbor Interpolation
Zooming - Bilinear Interpolation
Shrinking of Image
Method 1: Deleting row and columns
Method 2: Averaging pixel
Shrinking – Deleting rows and columns npd
Changing the grey level resolution of an image
Image Resolution – Varying Grey Level Resolution
Image Resolution – Varying Grey Level Resolution
Image Resolution – Varying Grey Level Resolution
Original image 256X256,
subsequent images have size
(256X256)
At every step, images are
obtained by reducing the no.
of available grey levels from 6
bits to 1 bit
Storage
No. of bits required to store a Grey Scale and a Color Image
M = No. of rows, N = No. of Columns, k= No. of Grey Level,
b = No. of Buffers
Total no. of bits required to store a digitized image
Total = M X N X k X b
If No. of rows = No. of Columns, M = N, Total = N2 Kb
Example:
For Grey Scale Image
Image size: 512 x 512
No. of gray level = 8, Buffer for Grey Scale b = 1
No. of bits required 512 x 512 x 8 its
Storage
No. of bits required to store a Color Image
Example:
For Color Image
Image size: 512 x 512
No. of gray level = 8, Buffer for Grey Scale b = 3
No. of bits required 512 x 512 x 8x3 bits
Convolution
Convolution - What it is?
Convolution is a simple mathematical operation which is fundamental to many common
image processing operators
Two elements required for convolution are:
Image: input image in the form of a 2D array
Kernel or mask: two dimensional array much smaller in size. Size of the mask could be
(3X3), (5X5), (7x7) etc.
-
W1, W2, ….. W9 are the mask coefficients for the 8 neighbors of (x, y)
h[ f( x , y ) ] = w1f( x - 1 , y - 1 ) + w2f( x - 1 , y ) + w3f( x - 1 , y + 1) + w4f( x ,
y - 1) + w5f( x , y ) + w6f( x , y + 1 ) + w7f( x + 1 , y - 1 ) + w8f(x + 1 , y ) + w9
f(x + 1 , y + 1)
0
