Converting from an analog to a digital image.

1. Sampleing image into pixels----> "picture element"

2. Quantize the pixel value to make discrete or finite

Digital imageing is the process of sampleing and quantizing the analog image (can store in computer)

SAMPLEING

1. fit a set grid of pts over the image

pixel location = (R,C)

Get value of pixel

V(R,C) =(R,G,B)

RED = SS(red(x,y) dxdy)

E= red values of pts. in pixel area

Pixel(r,c)= (red, green, blue)

= color value in RGB at pixel location (r,c)

Image size = (rows)*(colomns)

Resolution = size of image = #pixels

• low resolution ---> # of pixels is small
• high resolution ----> # of pixels is big

HOW DO YOU SELECT A RESOLUTION?

• if grid is too large you will get jagged edges

continuous value ---> discrete value

Z = decision level (Z1, Z2, Z3)

Qk = quantization level (Q0, Q1, Q2)

• the above are all discrete values

The rule to convert

How do we choose the Q's and Z's

where

E = Expected value{(pixel- newpixel)*(pixel-newpixel )}

= mean squared error

Note:

square error= (pixel value - new value)

expected value = a type of average or mean

If we try to minimize this error this leads to the following equation

Qk= (Zk+1+Zk) / 2

So, Steps to follow are:

1. choose Zk

2. calculate Q's via above equation
   

How Do We Choose Z's?

Basically, it is function of image content
You want more levels (Z values) in greyvalue or color ranges in which much of te image pixels fall.

Bits and Bytes... Some Computer Terms

Bit =0 or 1 = it is hte smallest memory storage unit in a computer

Byte = 8 bits grouped together ---> 256 possibilities (2^8 means 2 to the power of 8)= values from 0 to 255

Thus, if we have N bits per pixel ==> there are 2^N or 2 to the Nth power possible values

Types of Images: bits/pixel

Greyscale Image= 8 bits/pixel = 0 - 255 levels

Full colorimage = 24 bits/pixel = 16.7 million colors

• 8 bits/ pixel Red: 256 : 0-255
• 8 bits/ pixel Green : 256: 0- 255
• 8 bits/ pixel Blue : 256 : 0- 255
  

Serial Transmission of an Image

baud rate = bits per second that can be transfered

Time to transfer (in seconds) = [(#of pixels)*(bits per pixel)]/ baud rate

What do you loose in making a Digital Image?

1. Loss from sampleing?

Over-Sampling = when you have more samples than you need

Under-Sampling = not enough samples are used

How many samples do you need?
You need 2 times the Nyquest rate.
Nyquest rate= function of highest frequency in the image
highest frequency in image = function of fastest varying spatial patter in the image = f(how fast things change)

2. Loss from Quantization?

There is always loss from process of quantization